doi
stringlengths 10
10
| chunk-id
int64 0
936
| chunk
stringlengths 401
2.02k
| id
stringlengths 12
14
| title
stringlengths 8
162
| summary
stringlengths 228
1.92k
| source
stringlengths 31
31
| authors
stringlengths 7
6.97k
| categories
stringlengths 5
107
| comment
stringlengths 4
398
⌀ | journal_ref
stringlengths 8
194
⌀ | primary_category
stringlengths 5
17
| published
stringlengths 8
8
| updated
stringlengths 8
8
| references
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2307.01848 | 29 | ###Object List GT: [DiningTable, Pillow, HousePlant, Stool, Plate, Drawer, FloorLamp, KeyChain, Painting, RemoteControl. Chair, SideTable, CoffeeTable, Statue, Sofa, Window, LightSwitch, Garbage, Television, Vase, Pen, Can, CreditCard, Box, Pillow, ArmChair, Laptop]
I want to watch TV 1.Locate the TV remote control, which is typically placed on a table or mounted on the wall near the TV. 2.Point the remote control at the TV and press the power button. The power button is often located on the top or bottom of the remote control. 3.If the TV doesnât power on immediately, press the channel or input button to select the correct input source. The input button is usually located on the right side of the remote control or may be a menu button that allows you to access the settings. 4.The TV should now turn on and display the channel or content you have selected.
User LLaMA Sit in a comfortable chair, place a pillow on the ï¬oor, and turn on the TV. | 2307.01848#29 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 29 | The mathematical embedding provides the ability to view the entirety of a mathscape (set of equations) in a paper in one at-a-glance abstracted and consolidated view, and compare related mathematical ecologies. Figure 7a portrays the 3 AdS/CFT mathematical ecologies and the Chern-Simons mathematical ecology. On the one hand, it is surprising that there is not more overlap in the AdS/CFT mathematical ecologies since they are ostensibly of the same mathematics. On the other hand, this is higher math, and the mathscapes of the 43, 68, and 476 equations, respectively, are elaborated separately for different purposes. The AdS/CFT mathematics is a somewhat standard mathematics which equates a messy bulk volume with a boundary field theory in one fewer dimensions. The mathematics has the core structure of four equations defining the structure of the bulk space (the Metric (ds =)), operators to act on the bulk and the boundary (Operators (O =)), the system dynamics from which equations of motion can be derived (the Action (S =), and the system energy operator (the Hamiltonian (H = )). The hypothesis would be that producing | 2307.02502#29 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 30 | User LLaMA Sit in a comfortable chair, place a pillow on the ï¬oor, and turn on the TV.
Open up the drawer to get a remote control and place the TV on a table. Adjust the volume on the speakers. Locate the plug for the television and plug it in. Place the bowl of objects on the table next to the TV. (Objects: chair, sofa, pen, ï¬gurine, table, dog, lampshade, doorknob, bed, toy, drawer, person, statue (sculpture), ï¬owerpot, stool, monitor, (computer equipment), speaker, computer monitor, . . . . . . )
Table 4: Visualization of generated action plans by different large models. The inputs of LLaMA and GPT-3.5 are the predicted object list by our visual perception module, while LLaVA generates the action steps only based on one single image. | 2307.01848#30 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 30 | dynamics from which equations of motion can be derived (the Action (S =), and the system energy operator (the Hamiltonian (H = )). The hypothesis would be that producing embeddings for the full set of 30 AdS/CFT mathematics ecologies might reflect more overlap, at least in the core equations that state the correspondence. From a math studies perspective, the equation clusters and embeddings suggest that it will not necessarily be straightforward to integrate mathematical ecologies. | 2307.02502#30 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 31 | size signiï¬cantly increases the image collection and the computational cost due to the numerous RGB images, while the average success rate remains similar (47.83 vs. 44.78) because the large grid size can collect images with sufï¬cient information of the small-scale scenes from AI2-THOR. Similar reasons result in the phenomenon for random positions that increasing the sampling ratio and reducing the unit angle for camera rotation by collecting images in more locations cannot boost the success rate (47.95 vs. 47.23, 46.93 vs. 47.23). Since the traversal positions with small grid sizes (G=0.25) collects extremely large number of images, decreasing the unit angle for camera rotation signiï¬cantly decreases the success rate because the redundant object list degrades the planning capacity of LLMs. | 2307.01848#31 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 31 | Page 9
In Figure 7b with the Alzheimerâs disease mathematics, the lack of overlap is not surprising as the programs target different phenomenon: transposon dynamics, multiscalar aggregates, tau phosphorylation, and protein kinetics and clearance. The reason that the SIR control model and the Goriely mathematics are close together is because both have a heavy focus on differential equations in their mathscapes. The Alzheimerâs disease mathematical ecologies help as a first- pass view of the landscape of the math, without having to read and digest the paper, with the easy mouse-over view to see the types of mathematics used by the authors in the form of the equation clusters. One research question is whether the Alzheimerâs disease mathematical ecologies should be connected, whether that would be helpful in obtaining a causal understanding of the pathology. There is also the possibility of adding a time dimension to the Equation Cluster embeddings as different mathematics may describe the different phases of the pathologyâs evolution (the data for 3 phases of Alzheimerâs onset and progression now exists, and these data could be accompanied by the relevant mathematics).
Figure 8. Equation Clusters and Data Embedding Visualization: (a) Transposon Math-Data and (b) AdS Math-Data. (a). Transposon Dynamics + Chern-Simons + AD RSIDs
(b). AdS Mathematics + AD RSIDs | 2307.02502#31 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 32 | Comparing all location selection criteria, block- wise center points achieve the highest success rate because of the effective representation of the ex- isted objects in the scene. Block-wise center points observe the scene with the high coverage rate, while only a few RGB images are collected for scene rep- resentation. Therefore, sufï¬cient scene information is captured by the acquired object list without re- dundancy. The performance of random positions and the overall center point is similar because the scale of scenes in AI2-THOR is small and one im- age collection location can also receive sufï¬cient information. The traversal positions obtain the low- est success rate since collecting excess images lead
99, ,_â¢.Counterfactual = Hallucination 5 81.7% 80% 70% ¢0% 50% ° 40.0% 40% [35.0% 30% EUR ee 20% im 18.3% â 10% 0% 13.3% 13.3% LLaVA GPT-3.5 LLaMA TaPA Model
8
Figure 4: The percentage of different failure cases in embodied task planning for various large models.
to the higher probability of false positives in open- vocabulary object detection, which degrades the success rate because of the redundant object list. | 2307.01848#32 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.01848 | 33 | to the higher probability of false positives in open- vocabulary object detection, which degrades the success rate because of the redundant object list.
Among all room types, the success rate in the kitchen scenes is the lowest since the instruction for kitchen tasks (e.g. sandwich making) usually requires long plans with much more action steps. With the increase of the interacted objects in the task plan, the probability of hallucination is higher so that the plans are more likely to fail. On the contrary, the success rate of tasks in the living rooms is high due to the simple instructions (e.g. turning off lights). By observing the success rate of kitchen tasks across different location selection criteria, false positives in object detection that usually appear in traversal location selection criteria degrade the performance most signiï¬cantly. Since the object list is redundant, the complex tasks in kitchen scenarios are more prone to the noise in the object list.
We also show an example of generated action steps from different large models for the given scene in Table 4. The scene is demonstrated in the top-down view, and we also provide the groundtruth object list for reference. The content from LLaMA is irrelevant to the human instructions, while LLaVA provides plans that are not executable due to the non-existed objects. Although GPT-3.5 can also yield plausible embodied task plans, the action steps from our TaPA are more complete and more consistent with human values.
# 5 Conclusion | 2307.01848#33 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 33 | The charts in Figure 8 join mathematics and data in the same view with embedding as the common vernacular. Figure 8a shows the mathematical ecologies for Chern-Simons DNA-RNA host-virus interaction, transposon dynamics, and Alzheimerâs disease SNPs, and Figure 8b shows the mathematical ecologies for AdS/CFT and Alzheimerâs SNPs. The reason for Figure 8a is to investigate the transposon claim of Alzheimerâs disease genesis, which is that viral infection leads to transposable element insertion-deletion movement in DNA which triggers Alzheimerâs disease onset. The equation clusters are of two possible math ecologies that might explain this claim, which, when viewed together with the data, suggest that the Chern-Simons model may have higher explanatory value as the embedding clusters are in closer proximity to those of the data. However, this is only a first-pass hypothesis using the mathematical embedding abstraction tools, as direction-setting for further investigation. In Figure 8b, equation clusters for the AdS/CFT mathematics appear together with the Alzheimerâs disease | 2307.02502#33 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 34 | # 5 Conclusion
In this paper, we have presented a task planning agent called TaPA for embodied task planning, where the executable action steps are generated for subsequent robot navigation and manipulation to complete human instructions. We ï¬rst construct a multimodal dataset where each sample is a triplet including the visual scenes, the instructions and the corresponding plans. The dataset is generated with GPT-3.5 by considering the list of all objects in the scene and the designed text prompt, which is leveraged to tune the instruction model to generate executable actions. For inference, we collect multi-view RGB images in different achievable locations, and leverage an open-vocabulary object detection framework to discover the object list of the scene for the ï¬netuned instruction model. The statistics of our collected multimodal dataset indicate that our tasks are much more complex than conventional benchmarks on instruction-following tasks with longer implementation steps, and the extensive evaluation results show that our TaPA outperforms the state-of-the-art LLMs and LMMs on the plausibility of generated action plans.
# References | 2307.01848#34 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 34 | for further investigation. In Figure 8b, equation clusters for the AdS/CFT mathematics appear together with the Alzheimerâs disease SNPs. It is not readily human-discernable how these data sets may relate, except for possibly that the Kaplan math is closer to the Alzheimerâs data. The method, if any, for working with a mathematical ecology and an underlying data set in a unified embedding visualization is not yet clear. The premise here is that it may be useful to look at the math and data of a problem together through the same lens, that of the embedding, which provides a meta level for examining math and data simultaneously. Even if the two are not related, it is useful to see the size and shape of a mathematical body and a data body in the embedding format. | 2307.02502#34 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 35 | # References
[1] J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser. Tidybot: Personalized robot assistance with large language models. arXiv preprint arXiv:2305.05658, 2023.
[2] C. Li, C. Wong, S. Zhang, N. Usuyama, H. Liu, J. Yang, T. Naumann, H. Poon, and J. Gao. Llava-med: Training a large language-and-vision assistant for biomedicine in one day. arXiv preprint arXiv:2306.00890, 2023.
[3] Z. Zhao, S. Wang, J. Gu, Y. Zhu, L. Mei, Z. Zhuang, Z. Cui, Q. Wang, and D. Shen. Chatcad+: Towards a universal and reliable interactive cad using llms. arXiv preprint arXiv:2305.15964, 2023. | 2307.01848#35 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 35 | Figure 9. (a). Embeddings Data View: Alzheimerâs SNPs and (b-c) Precision Medicine Citizen 1, 2 AD SNPs.
Page 10
(a). Alzheimerâs SNPs (GWAS) and Citizen 1, 2 Total and Homozygous AD SNPs (b). Citizen 1 Homozygous AD SNPs with Genes (c). Citizen 2 Homozygous AD SNPs with Genes
, cas a Cit 1-2 a . C24 ° Cit 2-2
Fc >|» stcoant 4 SIL. OMAP eo racks © rs Maxon fe saerae * WNOIDA = ® PICALM PICALM TSPORPL NMER C160rf95 © SHARPIN isd @ TSPOAPLASI © 134 er ow 2 8 # ure a 3 £833 uncoroes o na8 oe,inge © uuna2 ® alcam BINA @ fina * ms a âââ © cenpw » nome epuad Fa wo PHAL aren eSuix ore HUA RB 8 as 9 9s sks sa | 2307.02502#35 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 36 | [4] Y. Sun, C. Zhu, S. Zheng, K. Zhang, Z. Shui, X. Yu, Y. Zhao, H. Li, Y. Zhang, R. Zhao, et al. Pathasst: Redeï¬ning pathology through generative foundation ai assistant for pathology. arXiv preprint arXiv:2305.15072, 2023.
[5] S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor. Chatgpt for robotics: Design principles and model abilities. Microsoft Auton. Syst. Robot. Res, 2:20, 2023.
[6] F. Stella, C. Della Santina, and J. Hughes. How can llms transform the robotic design process? Nature Machine Intelligence, pages 1â4, 2023.
9
[7] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi`ere, N. Goyal, E. Hambro, F. Azhar, et al. Llama: Open and efï¬cient foundation language models. arXiv preprint arXiv:2302.13971, 2023. | 2307.01848#36 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.01848 | 37 | [8] R. Zhang, J. Han, A. Zhou, X. Hu, S. Yan, P. Lu, H. Li, P. Gao, and Y. Qiao. Llama- adapter: Efï¬cient ï¬ne-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199, 2023.
[9] B. Peng, C. Li, P. He, M. Galley, and J. Gao. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277, 2023.
[10] D. Zhu, J. Chen, X. Shen, X. Li, and M. Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023.
[11] M. Shridhar, L. Manuelli, and D. Fox. Cliport: What and where pathways for robotic manipula- tion. In Conference on Robot Learning, pages 894â906. PMLR, 2022. | 2307.01848#37 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 37 | Figure 9 provides a data-only view of genomic variants and how they might be linked to Precision Medicine initiatives. Figure 9a is the embedding of all SNPs (RSIDs) associated with Alzheimerâs disease large-scale association studies. Figure 9b-c is from the total variants, those SNPs for which the whole-human genome analysis of two individuals, Citizen 1 and Citizen 2, indicates homozygosity (two alternative alleles) and therefore higher risk or interventional starting points. Notably, each individual is homozygous for different subsets of genes; Citizen 1 for more immune system related SNPs such as HLA-DRB1 and CD33, and Alzheimerâs-related clathrin binder (PICALM). Citizen 2 is homozygous for more membrane proteins and cytokine- dependent hematopoietic cell linkers (CLNK). Both are homozygous for the solute carrier protein (SLC24A4) and nexin (SNX1). | 2307.02502#37 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 38 | [12] S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta. R3m: A universal visual representation for robot manipulation. arXiv preprint arXiv:2203.12601, 2022.
[13] E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn. Bc-z: Zero-shot task generalization with robotic imitation learning. In Conference on Robot Learning, pages 991â1002. PMLR, 2022.
[14] A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, et al. Do as i can, not as i say: Grounding language in robotic affordances. In Conference on Robot Learning, pages 287â318. PMLR, 2023.
[15] C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su. Llm-planner: Few- shot grounded planning for embodied agents with large language models. arXiv preprint arXiv:2212.04088, 2022. | 2307.01848#38 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 38 | This could be actionable information as genomic medicine shifts into a new and vastly more expansive era requiring AI assistance to analyze epigenetic markers, transposable element insertions-deletions, and eQTLs (expression quantitative trait loci), the genomic directions to express harmful proteins (a locus that explains a fraction of the genetic variance of a gene expression phenotype). The point of Figure 9 is to show how these kinds of AI embeddings approaches might be helpful in the realization of precision health initiatives, identifying specific genes, pathways, expression profiles, and blood biomarkers, and their causal interactions, that are medically actionable.
The applied genomics precision health implication is starting with known condition alternative allele SNPs in the healthy citizen patient, and obtaining eQTL RNA expression and blood plasma data as needed in causality-tracking personal health dossiers. AI methods engaging mathematics and data are also implicated in broader information system biology approaches to
Page 11 | 2307.02502#38 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 39 | [16] M. Shridhar, J. Thomason, D. Gordon, Y. Bisk, W. Han, R. Mottaghi, L. Zettlemoyer, and D. Fox. Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10740â10749, 2020.
[17] H. Liu, C. Li, Q. Wu, and Y. J. Lee. Visual instruction tuning. arXiv preprint arXiv:2304.08485, 2023.
[18] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877â1901, 2020.
[19] J. D. M.-W. C. Kenton and L. K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, volume 1, page 2, 2019. | 2307.01848#39 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 39 | Page 11
link various diseases previously considered on a standalone basis such as Alzheimerâs disease and diabetes (Alzheimerâs disease as a form of Type 3 Diabetes), ApoE-based links between Alzheimerâs disease and Downâs syndrome (Dooling et al., 2022) and the potential interrelation between Alzheimerâs disease, Parkinsonâs disease, and ALS (amyotrophic lateral sclerosis) as the embedding visualization of the overlap in genomic SNPs as a starting point (Figure 10b). | 2307.02502#39 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 40 | [20] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
[21] A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. Segment anything. arXiv preprint arXiv:2304.02643, 2023.
[22] H. Bangalath, M. Maaz, M. U. Khattak, S. H. Khan, and F. Shahbaz Khan. Bridging the gap between object and image-level representations for open-vocabulary detection. Advances in Neural Information Processing Systems, 35:33781â33794, 2022. | 2307.01848#40 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 40 | AdS/Brain Research Program The AdS/Brain research program develops the idea that multiscalar mathematical models discovered in physics (such as the AdS/CFT correspondence and Chern-Simons topological invariance) may be relevant in theorizing and modeling the multiscalar complexity of biology. This could be useful for information systems biology problems such as aging and chronic disease for which a causal understanding is lacking. Leading multiscalar mathematics programs (the AdS/CFT correspondence and Chern-Simons theory) as mathematical ecologies (sets of equations) may be applied to model the complexities of biosystems which are likewise rigorously multiscalar. The AdS/Brain theory has been proposed theoretically but not tested experimentally, which may start to be feasible with AI-driven at-scale science methods (Swan et al., 2022a,b). The aim of the current project is to explore AI-related tools in an integrated approach to solving disease pathology that has not been easily available prior to AI methods, in the proximate investigation of Alzheimerâs disease. The hypothesis is that multiscalar physics mathematics | 2307.02502#40 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 41 | [23] X. Zhou, R. Girdhar, A. Joulin, P. Kr¨ahenb¨uhl, and I. Misra. Detecting twenty-thousand classes using image-level supervision. In Computer VisionâECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23â27, 2022, Proceedings, Part IX, pages 350â368. Springer, 2022.
[24] L. Floridi and M. Chiriatti. Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines, 30:681â694, 2020.
10
[25] J. Li, D. Li, S. Savarese, and S. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.
[26] F. Sammani, T. Mukherjee, and N. Deligiannis. Nlx-gpt: A model for natural language explanations in vision and vision-language tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8322â8332, 2022. | 2307.01848#41 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 41 | available prior to AI methods, in the proximate investigation of Alzheimerâs disease. The hypothesis is that multiscalar physics mathematics (elucidating near-far relations in systems) may be applied to develop a causal understanding of the pathologies of aging in a genomic theory of medicine involving SNP variations (single nucleotide polymorphism) and transposable element dynamics. Genomics is indicated in Alzheimerâs disease activation (variants linked to disease risk and expression (Sun et al., 2023)), and LLMs are seen as being an indispensable at-scale tool for genomic analysis (Nguyen et al., 2023; Batzoglou, 2023). Into this trajectory, the current work formulates a mathematically-based approach to Alzheimerâs genomics. | 2307.02502#41 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 42 | [27] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al. Sparks of artiï¬cial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023.
[28] T. Schick, J. Dwivedi-Yu, R. Dess`ı, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023.
[29] M. Zong and B. Krishnamachari. Solving math word problems concerning systems of equations with gpt-3. In Proceedings of the Thirteenth AAAI Symposium on Educational Advances in Artiï¬cial Intelligence, 2022.
[30] Y. Zang, W. Li, J. Han, K. Zhou, and C. C. Loy. Contextual object detection with multimodal large language models. arXiv preprint arXiv:2305.18279, 2023. | 2307.01848#42 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 42 | Control Example: Two-tier Precision Medicine SIR Model: Health and Information Flow A control example is selected for this analysis to provide a comparison between the current work and a familiar widely-known example of mathematics from epidemiology (Figure 10a). This is the SIR compartmental model (all individuals in a population are in the categories of being either susceptible, infected, or recovering), expressed as a set of solvable differential equations (dS/dt, dI/dt, dR/dt) (Kermack & McKendrick, 1991). The equation clusters in the mathematical ecology visualization confirm the presence of the expected mathematics, and show how the embedding organizes the mathematics into different equation clusters for the systemâs differential equations both together (SW corner) and separately (NE and S), and other clusters for parameter specification (NE corner). The SIR model is not only useful for comparison as a known mathematics, but also in formulating a mathematical approach to realizing precision medicine. | 2307.02502#42 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 43 | [31] G. Ghiasi, X. Gu, Y. Cui, and T.-Y. Lin. Open-vocabulary image segmentation. arXiv preprint arXiv:2112.12143, 2021.
[32] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748â8763. PMLR, 2021.
[33] Z. Wu, Z. Wang, Z. Wei, Y. Wei, and H. Yan. Smart explorer: Recognizing objects in dense clutter via interactive exploration. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6600â6607. IEEE, 2022.
[34] Z. Liu, Z. Wang, S. Huang, J. Zhou, and J. Lu. Ge-grasp: Efï¬cient target-oriented grasping in dense clutter. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1388â1395. IEEE, 2022. | 2307.01848#43 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 43 | As a compartmental model of the population, the SIR model may be adapted to the precision medicine use case of individuals being in a new cast of SIR states, those of sustaining, intervening, or restoring. An important feature of the traditional SIR model is tracking infectious disease spread, however, in the precision health model, âinfectionâ is the âpositive infectionâ of the spread of information which impels preventive intervention. There could be a two-tier model, the underlying tier of the health graph of individuals in SIR states, and the secondary tier of the information flow of inputs from physicians, apps, clinical trials, health research studies, advocacy groups, health social networks, virtual patient modeling, and quantified-self citizen science efforts to implement necessary preventive initiatives. The aim of precision medicine is to
Page 12
prevent conditions early in the 80% of their lifecycle before they become clinically detectable. A Precision Health SIR Model could be a formal way to implement precision health initiatives in society, possibly in concert with other quantitative models such as value-based healthcare (compensation tied to health outcomes).
Figure 10. (a). SIR Compartmental Model and (b) Multi-disease Genomic View.
(a). SIR (Susceptible, Infected, Recovering) (b). Genomic Variant Overlap: AD, PD, ALS | 2307.02502#43 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 44 | [35] X. Xu, Z. Sun, Z. Wang, H. Liu, J. Zhou, and J. Lu. Dspdet3d: Dynamic spatial pruning for 3d small object detection. arXiv preprint arXiv:2305.03716, 2023.
[36] X. Xu, Y. Wang, Y. Zheng, Y. Rao, J. Zhou, and J. Lu. Back to reality: Weakly-supervised 3d object detection with shape-guided label enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8438â8447, 2022.
[37] V. Blukis, C. Paxton, D. Fox, A. Garg, and Y. Artzi. A persistent spatial semantic representation for high-level natural language instruction execution. In Conference on Robot Learning, pages 706â717. PMLR, 2022.
[38] R. Zellers, A. Holtzman, M. Peters, R. Mottaghi, A. Kembhavi, A. Farhadi, and Y. Choi. Piglet: Language grounding through neuro-symbolic interaction in a 3d world. arXiv preprint arXiv:2106.00188, 2021. | 2307.01848#44 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 44 | (a). SIR (Susceptible, Infected, Recovering) (b). Genomic Variant Overlap: AD, PD, ALS
ds, S Parameters F o| © âle F LC): differential 1 aed 1) equations: . dS, dI, dR 40,0
13 11 4g
Interpretation: Mathematical Position Evaluation The point of the current pilot project is to develop a suite of AI-based mathematical abstraction tools that might be helpful towards in the construction of the digital mathematical infrastructure. The result is producing several high-level tools for viewing and working with mathematical abstraction, together with data sets, namely, the mathematical embedding, equation cluster, and mathematical ecology visualization, as new forms of high-level mathematical abstraction tools. From this demonstration, the broader use case, if any, for the deployment of these tools is not yet fully clear, from the perspective of both human and AI agents. However, these kinds of high- level abstraction tools, used in concert with Math Agent explorers might contribute to math discovery as new elements in the mathematical infrastructure for various operations, including position evaluation â being able to quickly grasp the size and scope of a mathematical ecology. | 2307.02502#44 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 45 | [39] A. Akakzia, C. Colas, P.-Y. Oudeyer, M. Chetouani, and O. Sigaud. Grounding language to autonomously-acquired skills via goal generation. arXiv preprint arXiv:2006.07185, 2020.
[40] W. Huang, P. Abbeel, D. Pathak, and I. Mordatch. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In International Conference on Machine Learning, pages 9118â9147. PMLR, 2022.
[41] S. Li, X. Puig, C. Paxton, Y. Du, C. Wang, L. Fan, T. Chen, D.-A. Huang, E. Aky¨urek, A. Anandkumar, et al. Pre-trained language models for interactive decision-making. Advances in Neural Information Processing Systems, 35:31199â31212, 2022.
11
[42] X. Puig, K. Ra, M. Boben, J. Li, T. Wang, S. Fidler, and A. Torralba. Virtualhome: Simulating household activities via programs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8494â8502, 2018. | 2307.01848#45 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 45 | The intended audience of embeddings is AI systems; embeddings render input in AI-readable form for further AI-based analysis. However, embeddings are also useful to human audiences in providing the ability to see the entirety of a mathscape consolidated in one view with zoomable levels of abstraction, and zoomable in-out capability to examine different aspects of the math. Vector embeddings themselves are emerging as one element in the larger standard body of computational infrastructure in which AI agents can help tackle systems-level problems too complex to address with previous methods. Although embeddings are developed in a human- orchestrated workflow here, they are increasingly being automatically incorporated into the cognitive infrastructure in new waves of GPT release. The kinds of mathematical signals expressed in the new infrastructure have a proximate use in being mobilized in the next steps of building large-scale precision health models to identify and prevent disease.
Section 3. Math-Data Relation AI tools offer more expedient ways to interact with reality at the level of mathematics instead of data. Such a trend is ongoing as the human interaction with the computational infrastructure is already one of mathematics on the computer side as user interfaces employ quantitative formal methods to produce the experience which humans receive as qualitative, with all the rich data properties of everyday existence (âAlexa, turn on some musicâ).
Page 13 | 2307.02502#45 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 46 | [43] B. Li, Y. Zhang, L. Chen, J. Wang, J. Yang, and Z. Liu. Otter: A multi-modal model with in-context instruction tuning. arXiv preprint arXiv:2305.03726, 2023.
[44] C. Lyu, M. Wu, L. Wang, X. Huang, B. Liu, Z. Du, S. Shi, and Z. Tu. Macaw-llm: Multi- modal language modeling with image, audio, video, and text integration. arXiv preprint arXiv:2306.09093, 2023.
[45] Q. Ye, H. Xu, G. Xu, J. Ye, M. Yan, Y. Zhou, J. Wang, A. Hu, P. Shi, Y. Shi, et al. mplug- owl: Modularization empowers large language models with multimodality. arXiv preprint arXiv:2304.14178, 2023. | 2307.01848#46 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 46 | Page 13
What is different is an upleveling in the concept of what the digital infrastructure is, and the kinds of interactions it affords for humans, possible shifting from a âbig dataâ to âbig mathâ era. Whereas the past mode of interaction with the digital infrastructure focused on corralling âdumbâ data, there is an awareness of the digital infrastructure now having âsmartâ (self- computational, formal, math-y) properties, e.g. automatic spell-checker, word-completion, recommendations, advertising-serving. This contributes to the sense that âthe network is alive,â which AI copilot technologies in web interfaces and document processing further accentuate. | 2307.02502#46 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 47 | [46] D. Gao, L. Ji, L. Zhou, K. Q. Lin, J. Chen, Z. Fan, and M. Z. Shou. Assistgpt: A general multi- modal assistant that can plan, execute, inspect, and learn. arXiv preprint arXiv:2306.08640, 2023.
[47] Z. Zhao, L. Guo, T. Yue, S. Chen, S. Shao, X. Zhu, Z. Yuan, and J. Liu. Chatbridge: Bridging modalities with large language model as a language catalyst. arXiv preprint arXiv:2305.16103, 2023.
[48] F. Chen, M. Han, H. Zhao, Q. Zhang, J. Shi, S. Xu, and B. Xu. X-llm: Bootstrapping advanced large language models by treating multi-modalities as foreign languages. arXiv preprint arXiv:2305.04160, 2023. | 2307.01848#47 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 47 | One research question is how AI tools may be changing the math-data relation. The math-data relation is the correspondence between mathematics and data. The embedding is an interesting technology in that it renders mathematics and data in the same format. On the one hand, math and data can be analyzed in the same view. On the other hand, math and data are two different things, and it may be misleading to examine them in the same view. Progressive ideas of how the math-data relation may be changing are presented in Figure 11 as the math-data composite view of two representations of the same system, the multiscalar renormalization of viewing a system at any scale tier through the lens of a conserved quantity, and mathematical abstraction as the interface (Kantian goggles) for data sublation and human and AI interaction with reality.
Figure 11. Mathematics as Foundational Lever for Interacting with Reality: The Math-Data Relation.
One System Two Modes Multiscalar Renormalization Abstraction: Mathematics is the Interface
a \ / \ / \ \ | 2307.02502#47 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 48 | [49] E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, M. Deitke, K. Ehsani, D. Gordon, Y. Zhu, et al. Ai2-thor: An interactive 3d environment for visual ai. arXiv preprint arXiv:1712.05474, 2017.
[50] X.-Y. Jiang, N.-Y. Pa, W.-C. Wang, T.-T. Yang, and W.-T. Pan. Site selection and layout of earthquake rescue center based on k-means clustering and fruit ï¬y optimization algorithm. In 2020 IEEE International Conference on Artiï¬cial Intelligence and Computer Applications (ICAICA), pages 1381â1389. IEEE, 2020.
[51] X. Liu. The site selection of distribution center based on linear programming transportation method. In Proceedings of the 10th World Congress on Intelligent Control and Automation, pages 3538â3542. IEEE, 2012.
12 | 2307.01848#48 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 48 | ~ Math Math A\ say A \ \\ \]
One System Two Modes The math-data relation often centers around the model-fit problem. The model-fit (map-territory) problem is how well a generalized set of descriptive mathematics accurately recapitulates the data of an underlying phenomenon in which exceptions, variation, and irregularity may be inherent. The trade-off is how much specificity may be lost to capture the core salience of the phenomenon. By example, diagnostic healthcare is a notoriously high-dimensional realm in which there are many approaches to the model-fit problem (Montoya & Edwards, 2020).
To the extent that it is valid and useful to examine the mathematics and the data of a system through the same lens of embedding (the mathematical embedding and the data embedding), model-fit problems may be better assessed. Theoretically, the math and the data are two representations of the same system, and one could be viewed from the other (if the math is accurate). For example, there could be a set of data points for how many sales occurred each day, and an accompanying mathematical curve fit through the data which allows an estimate of future sales by day (absent holidays and other irregularities). The âsell more widgetsâ system can be examined with either lens, as data points or as a mathematical curve. Seeing math and data together suggests the idea of a math-data composite view.
Page 14 | 2307.02502#48 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 49 | 12
Rule description: You are an indoor service robot named Garybot and you are inside a room. What you see is provided with a list of objects that contains all the objects in the room you are in. The location of the objects in the list you are guided in advance, without reasoning about the spatial relations of the objects. Execute all the instructions as you are located in the room.
Design a conversation between you and the person you are serving in the room. The answer should be the tone of the service robot located in the room and performing the action speciï¬cally. The generated instructions can be described in different tones. Ask for various instructions and give the corresponding series of actions with a maximum of 15 steps.
Only include instructions for their corresponding actions only utilizing atomic motions (Grasp, Release, Lift, Place, Rotate, Push, Pull, Align, Press, Pour, Move): (1) Generate operation instructions using only the objects in the list with the actions that must be performed to complete the operating instructions; (2) Do not generate any instructions or actions that cannot be executed with conï¬dence; (3) Do not generate any instructions or actions with (Target: [Object]), [Object] is outside the list of objects. | 2307.01848#49 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 49 | Page 14
The math-data composite view is seeing the math and the data for a system together in one view, which is demonstrated in this analysis through the embedding, but there could be other methods. Manipulable zoomable 3D visualization tools, data explorers and math explorers, could allow the size and shape of a âdata set of dataâ and a âdata set of mathâ to be viewed, both separately and superimposed so their correspondence may be assessed, by humans and AI Math Agents. Just as a curveâs model-fit through a set of data points can be somewhat readily interpreted, the idea is a more complicated setup in which different mathematical ecologies applied to different layers of complex data sets may be assessed, to determine the model-fit between a certain math and data. | 2307.02502#49 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 50 | Again, the object being manipulated cannot be located outside the list. Please double-check that Target: [Object] is in the list at each step and that [Object] is in the list. When evaluating the existence of [Object], consider its original part or component, its function, and whether it can be replaced by an object in the list, and if it is satisï¬ed, you can iterate over each element in the list to ï¬nd an alternative and replace [Object].
Few-shot samples: List of objects: [wine, cup, glass, remote control, TV, table, desk, chair] Generate the instruction: Give me a drink Necessary actions: Step 1. Grasp a bottle of wine (Target: wine) Step 2. Grasp a glass (Target: bowl) Step 3. Place the cup on the table (Target: glass, table) Step 4. Pour the wine into the glass (Target: wine, glass) Step 5. Grasp the glass with wine (Target: glass) Step 6. Move to the person and hand over it Step 7. Done | 2307.01848#50 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 50 | In application to science, although a lot of mathematics have been proposed, there is little confirmation, validation, mobilization, replicability, and ease of deployment. Given scale limitations, some large portion of the previous scientific endeavor may have been that of a specialized mathematics developed to model a specific small set of data. However, the at-scale full possibility space thinking enabled by contemporary tools means that the scientific endeavor can be recast to include the targeting of comprehensive solutions and understandings of vast multiscalar ecosystems and the mathematics that models and causally and predictively explains them. Such comprehensive knowledge at scale is the aim of AI systems and tools such as the Math Agent and the mathematical embedding. At minimum, these advances may offer new ways to consider the data stack, math stack, and math-data relation at multiple scale tiers in complex systems together with various actor-observers (humans, AIs, biological processes) interacting with the system in different ways (Figure 12). There could be advantages to working math as an entity, data as its own entity, as well as the data-math composite as a new kind of formal entity.
Figure 12. Math, Data, Math-Data Stacks and Scale-Tier Actor-Observers in Genomic Medicine. Math-Data Stack | 2307.02502#50 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 51 | Generate the instruction: Please turn on the TV Necessary actions: Step 1. Grasp the remote control (Target: remote control) Step 2. Move closer to the TV (Target: TV) Step 3. Rotate the remote control to point at the TV (Target: remote control, TV) Step 4. Press the power button to turn on the remote control (Target: remote control) Step 5. Done
Prompt for training and inference: Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. Instruction: Xq. Input: Xl. Response: Xa.
Table 5: Our prompt for multimodal dataset generation (upper) and training/inference of TaPA (bottom). Xa is empty unless the prompt serves as a ground-truth.
# Supplementary Material | 2307.01848#51 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 51 | Figure 12. Math, Data, Math-Data Stacks and Scale-Tier Actor-Observers in Genomic Medicine. Math-Data Stack
Scale Macro Actor -> Genetic variants Data Stack Math Stack Signal processing math Actor -> Actor -> Genetic variant near-far relations Actor -> ⦠Meso Actor -> Actor -> Transposon indels Actor -> Topological biomath Actor -> Actor -> Transposon dynamics Actor -> ⦠Micro Actor -> Amyloid-beta plaque, tau tangles Actor -> Actor -> Biochemical math Actor -> Actor -> Protein biomarker distribution | 2307.02502#51 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01848 | 52 | # Supplementary Material
The prompts utilized to generate the instruction-following dataset from GPT-3.5 are illustrated in Table 5. Speciï¬cally, we set a speciï¬c work scene for GPT-3.5 and indicate the need to generate instructions and corresponding actions by the agent itself. We also set the rules to constrain the instructions generated by GPT-3.5 with improved executability and conï¬dence. Meanwhile, we require GPT-3.5 to add an additional (Target: [Object]) query to each generated action to further check for hallucinations. If the interacting object in the generated plan is not in the input Xl, it
13
is necessary to check if there is an alternative item to replace it. An exceptional case is that the interacting objects can be part of the existing objects or a synonym of an object. We also provide some examples to standardize the form of the input and output for the generated data.
14 | 2307.01848#52 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world and usually yield infeasible action sequences. In this paper,
we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning
with physical scene constraint, where the agent generates executable plans
according to the existed objects in the scene by aligning LLMs with the visual
perception models. Specifically, we first construct a multimodal dataset
containing triplets of indoor scenes, instructions and action plans, where we
provide the designed prompts and the list of existing objects in the scene for
GPT-3.5 to generate a large number of instructions and corresponding planned
actions. The generated data is leveraged for grounded plan tuning of
pre-trained LLMs. During inference, we discover the objects in the scene by
extending open-vocabulary object detectors to multi-view RGB images collected
in different achievable locations. Experimental results show that the generated
plan from our TaPA framework can achieve higher success rate than LLaVA and
GPT-3.5 by a sizable margin, which indicates the practicality of embodied task
planning in general and complex environments. | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | [
{
"id": "2302.13971"
},
{
"id": "2304.02643"
},
{
"id": "1712.05474"
},
{
"id": "2302.04761"
},
{
"id": "2306.08640"
},
{
"id": "2112.12143"
},
{
"id": "2305.15964"
},
{
"id": "2304.14178"
},
{
"id": "2203.12601"
},
{
"id": "2304.08485"
},
{
"id": "2305.15072"
},
{
"id": "2304.10592"
},
{
"id": "2305.05658"
},
{
"id": "2301.12597"
},
{
"id": "2305.18279"
},
{
"id": "2106.00188"
},
{
"id": "2305.03726"
},
{
"id": "2303.16199"
},
{
"id": "2304.03277"
},
{
"id": "2106.09685"
},
{
"id": "2305.04160"
},
{
"id": "2006.07185"
},
{
"id": "2303.12712"
},
{
"id": "2305.03716"
},
{
"id": "2305.16103"
},
{
"id": "2212.04088"
},
{
"id": "2306.09093"
},
{
"id": "2306.00890"
}
] |
2307.02502 | 52 | Multiscalar System Mathematics One way to conceive the math-data relation is as two sides of one coin or as two different representations of the same system. Another way to see the math-data relation is at different levels of complexity, for example, that the data is an imperfect messy bulk volume and the math is a streamlined equation on the boundary describing the bulk volume. Emergent structure in bulk data can be detected by boundary math. This is the premise of machine learning: write a function describing these data. Since a multiscalar system is connected, every scale tier is an upleveled abstraction from the detail that takes place at the tiers below. For example, a gecko produces a limb as an end product, with many specific underlying steps and coordination. One elaboration of such causal activity between tiers in biosystems is denoted âmultiscale competencyâ in which (in regenesis and development) any scale tier appears to call the entire functionality of lower scale tiers (Levin, 2023).
Particularly in biosystems, there are a number of intricate unpredictable relations between scale tiers so the challenge is not as simple as aggregating up to âtemperatureâ from âparticles.â In
Page 15 | 2307.02502#52 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 53 | Page 15
considering the human brain and its pathologies, nine order-of-magnitude scale tiers have been identified (Sejnowski, 2020) involving 82 billion neurons, 242 trillion synapses, and neuron-glia interactions (Martins et al., 2019). By analogy to economics, the math and the data for the micro and macro scale tiers are known (individual transactions and GDP), but not a full and systematic model of causal behavior in the middle tiers.
Hence, the treatment of complex systems entails not only cataloging scale tiers, but the causal interaction between levels and how the entity operates together as a system. The open nature of biological systems is also important as actor-observers interact in constant feedback loops with the environment. Given these specificities, it is not possible to simply apply normalization (typically percentage-based scaling around 1 or 100) as a formal method to put scale tiers into dialogue. This immediately suggests the physics-based multiscalar method of renormalization. Renormalization is a formal method which allows the ability to view a system at multiple scales by collapsing parameters (degrees of freedom) that are not relevant across scale tiers. Instead, a system-wide factor may be identifiable such as symmetry (in the universe) or free energy (in biological systems) that is conserved, represented, and engageable at different scale tiers. | 2307.02502#53 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 54 | Some of the best-known renormalization programs for viewing systems across difference scale tiers are AdS/CFT and Chern-Simons. The holographic correspondence, AdS/CFT (anti-de Sitter space/conformal field theory), refers to the possibility of describing a messy bulk volume with a boundary theory in one fewer dimensions, any physical system, whether the universe, a brain, a bug on a windshield, or a room (Maldacena, 1999). Chern-Simons theory is a model of topological invariance (âbending not breakingâ unchanging aspects of a system as other changes are applied), a solvable quantum field theory in which (nonlocal) observable measures (Wilson loops) can be represented as knots (that generalize to a known knot invariant, the Jones polynomial) (Chern & Simons, 1974). Both AdS/CFT and Chern-Simons apply across all physical scales (quantum-classical-relativistic). | 2307.02502#54 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 55 | Abstraction: Mathematics is the Interface As a formal data corpus, mathematics is contiguous in ways that other data corpora are not. This suggests that to some extent, even the simplest equation calls the entire corpus of existing and possible mathematics as there may be arbitrarily-many layers of subsequent abstraction (e.g. set theory, category theory, type theory). The idea of mathematical complexity connotes that all mathematics at a certain level of abstraction may be computationally-equivalent in requiring the same degree of computational resources to solve. From a practical perspective, the interconnectedness of mathematics implies a framework for identifying the right level at which to solve multiscalar systems. The implication is working smart not hard. In the protein folding example, both nature and AlphaFold do not try every possible permutation but smart-solve more directly to the final conformation, using chemical bonding energy cues (nature) and high-level algorithms (AlphaFold (Jumper et al., 2021)). | 2307.02502#55 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 56 | The bigger implication of a transition to digitized mathematics is the ability to interact with reality at a higher level. Mathematics could provide a more effective and efficient means of interacting with reality. This is not a new thought. Mathematics is central to the scientific method. The aim is to write descriptive mathematics of a phenomenon such that new predictions about future behavior can be made. Mathematics is a content with high truth value. As more aspects (complex systems and everyday phenomena) of reality become expressible in mathematics, a more effective lever is available for engaging them. Multiple levels of human
Page 16
interfaces to the digital mathematics corpus are required, for example, at the levels of professional mathematician, scientific practitioner, economist, marketing, and lay persons. | 2307.02502#56 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 57 | Page 16
interfaces to the digital mathematics corpus are required, for example, at the levels of professional mathematician, scientific practitioner, economist, marketing, and lay persons.
The mathematical picture provides a different kind of Kantian goggles as a perceptual interface to reality, not just a quantitative scale revealing of the big and the small with the telescope and the microscope, but qualitative access to a more foundational structure of reality. Just as solving a multiscalar system at the right tier is important, so too, upleveling the overall interaction with reality at the right structural level might be similarly right-sized. The further implication of interacting with reality at the level of mathematics is a potential shift from the âbig dataâ era to the âbig mathâ era. Once data are corralled into the digital infrastructure with automated methods (a non-trivial task), mathematics as an abstracted level at which to interact with data is implied. This could be the new math-data relation, the idea of big data -> big math in treating digital reality at higher levels of abstraction which conferring greater effectiveness of salience, relevance, efficiency, and results. | 2307.02502#57 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 58 | Section 4: Evaluation of Mathematical Ecologies Mathematical embeddings or some other form of mathematics as mobile units of digital abstraction are implicated not only for representing large systems of equations (mathematical ecologies) but also for solving them. The implication of mathematics instantiated in a usable graph-based digital architecture is potential for the automated evaluation of mathematical ecologies. Mathematical embeddings are vector-space strings which are in AI-readable format as input to machine learning systems that run on graphs. Just as any equation joins the mathematical corpus of abstraction, so too any graph-formulated entity joins the entirety of the graph-theoretic smart network infrastructure which includes machine learning, blockchains, and quantum computing. Graphs facilitate access to the full range of information-theoretic properties such as energy-entropy formulations, uncertainty relations (quantum scale), and statistical interpretation. The digitized format suggests that automated Math Agent-facilitated evaluation could proceed at a number of levels in the math stack ranging from the brute-force testing of all permutations to the application of various higher-level mathematical methods to more efficiently solve a system. | 2307.02502#58 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 59 | The result of mathematics in the form of graphs is that the corpus interfaces well and can be taken up into the existing smart network infrastructure, which is likewise instantiated in graphs (machine learning, blockchains, quantum computing), in an overall picture of the computational infrastructure of digital reality. Easy-use human interfaces are indicated. The digitization of possibility spaces (data corpora) includes meta-level tools for large-scale mobilization such as âtop-level kernelsâ calling the entirety of the corpus and time-stamping clocks for cause-and- effect tracking. The language of formal methods is abstraction, deployed as Merkle root hashes in blockchains and embeddings in machine learning. | 2307.02502#59 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 60 | The digital approach to mathematics (with AI tools such as mathematical embeddings, equation clusters, Math Agents) is as follows. Mathematics is instantiated in graphs: a mathematical ecology (equations or symbols) is instantiated as the nodes of a graph. Solving proceeds in lock- step movement through the graph, with Math Agents finding the best path, by analogy to blockchain path-routing. The smart network graph is a math engine with Math Agents running on it. Mathematical embeddings and Math Agents that walk on these graphs to evaluate mathematical problems serve as two new elements in the digital mathematical infrastructure that have easy-to-use dialogical interfaces which offer the ability to interact with reality at a higher level. The representation and evaluation of mathematics may be incorporated in any variety of
Page 17
smart network technologies such as machine learning, blockchains, and quantum computing, as well as the network itself (âthe network is the computerâ) (Figure 13). The idea is Math Agents (AI bots) running on the possibility space of the mathematical graph, finding new solution paths in the mathematical possibility space. The result could be a continuing transition from the âbig dataâ era to the âbig mathâ era. Math Agents are stateful information states with action-taking policies, running as a smart graph overlay, taking actions to proceed through the graph as a state- based information system to find new solutions. | 2307.02502#60 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 61 | Figure 13. Computational Infrastructure. Mathematical Functionality and Smart Network Technology.
Smart Network Technology Mathematical Functionality: Representation and Evaluation 1 Machine learning 2 Blockchain 3 Quantum computing Transformer evaluation of entire mathematical corpus at once Equation evaluation, theorem-proving, and IP-logged discovery (mNFT) Uncertainty relation entropy-energy trade-offs (ID cross layer correlations in multiscalar systems); renormalization AdS/CFT DMRG-Q Network-informed information states (computation outsourced to network) | 2307.02502#61 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 62 | Machine Learning Machine learning is the marquis digital infrastructural technology, now in routine use in nearly all fields. Embeddings are the standard means of data input to machine learning systems, as any kind of data is encoded as vector-space strings and passed into the graph. The contemporary machine learning model used by GPT and other LLMs is transformer neural networks, which offer an advance by analyzing all data simultaneously to find relations between tokens (small packages of data). In the main machine learning method, problems are formulated as optimizations, and the network cycles (forward and backpropagating) to find the best weightings for network nodes to deliver a predictive solution. Physics and economics principles inform machine learning in the use of lowest-energy cost functions that descend a gradient to identify the best solution. In the widely-used Boltzmann machine method, an energy-minimizing probability function is used to evaluate machine learning algorithm output (based on the Boltzmann distribution from statistical mechanics in which the probability that a system will be in a certain state is evaluated as a function of the stateâs energy and systemâs temperature). Math Agents could run as an overlay to machine learning to evaluate mathematical ecologies, including incorporating abstraction in different ways. | 2307.02502#62 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 63 | The most straightforward implementation is mathematics being analyzed as the data corpus in the usual machine learning network setup. A secondary implementation could involve the mathematical problem structure being setup in the machine learning network architecture for the system to find the best weights, number of network layers, and overall graph shape corresponding to the mathematical solution. By analogy, a real-world physics problem (the emerging bulk structure of a quark-gluon plasma) is set up in a holographic machine learning model in which the emerging neural network structure corresponds to an emerging physical bulk structure that describes chiral condensates (Hashimoto, 2021). The point is that machine learning networks incorporate a variety of graph-theoretic features that could be employed by Math Agents to evaluate mathematical ecologies, namely, properties such as physics-inspired energy- entropy loss function calculations, causal inference, and probabilistic prediction.
Blockchains Blockchains (distributed ledger systems) are likewise a smart network technology with properties conducive to the Math Agent evaluation of mathematical ecologies, especially with
Page 18 | 2307.02502#63 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 64 | Blockchains Blockchains (distributed ledger systems) are likewise a smart network technology with properties conducive to the Math Agent evaluation of mathematical ecologies, especially with
Page 18
the Layer 2 abstraction layer. The conceptual idea of a blockchain in this context is a living graph that can do math. Whereas both machine learning and blockchains are smart network systems in which problems are instantiated in graphs, blockchains provide more robust functionality. Machine learning graphs operate for high throughput and efficiency with minimal operations at each node, namely the up-down weighting of a probability coefficient as the network cycles backward and forward to obtain an optimal predictive result. Blockchain graphs offer richer node functionality for mathematical equation evaluation and proof. These kinds of smart network features could be useful when high-throughput data processing is not the aim but rather the creation of a self-solving digital mathematical infrastructure. Blockchains, for example, are core providers of clocks (dos Santos, 2019), time-keeping mechanisms in the computational infrastructure which could be incorporated into AI episodic memories to potentially attribute causality to event unfolding such as pathology development (Shapiro, 2021). | 2307.02502#64 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 65 | Solving math in blockchain networks could proceed as follows. Layer 2 overlays such as the Lightning Network run as an abstraction layer to blockchains, with useful functionality for blockchain operates such as allowing a batch of administrative transactions to be consolidated and posted to the blockchain, automated operations such as wallet balancing, smart contract execution, oracle confirmation, and transaction path routing. Nodes are stateful and agents can engage these states in audit-logged transaction paths for mathematics use cases such as equation evaluation and automated proof generation. The Layer-2 overlay enables path routing through the graph in which nodes are equations or lemmas, through which a math agent cannot proceed without appropriate validation (confirmation of the validity of the mathematics at this node). | 2307.02502#65 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 66 | First considering equation evaluation, the mathematical ecology proposed in a published paper (~50-200 equations on average) can be instantiated in a math graph for evaluation, validation, and replication. Each equation is a node in the math graph (other embeddings could be at the level of symbol or token). The Math Agent proceeds through the math graph nodes to confirm and evaluate the chain of equations. Blockchain functionality tracks and administers this process, confirming progress at each node with hash functions before progressing to a subsequent node is allowed. The AI features of the Math Agent can help to address the problem of replicability (that some high percent of mathematical equation ecologies in published literature cannot be simply implemented and run as a whole). In its math graph validation processes, the Math Agent may be able to solve in-situ, filling in clarificatory adjustments or additional mathematics to required proceed through the math graph with the result of producing a tested robust generalized version of the mathematical ecology. Blockchain math agent certifications (with a F(x) symbol for example) could reflect the fact that a body of mathematics has been independently tested and certified. Any new proposed mathematics (whether human, AI, or | 2307.02502#66 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 67 | symbol for example) could reflect the fact that a body of mathematics has been independently tested and certified. Any new proposed mathematics (whether human, AI, or human-AI discovered) could attest to its validity by seeking independent Math Agent certification. In the overall frame of integration, synthesis, and extension of the mathematical corpus, routine tasks for the Math Agents could include finding and applying the best math for a data set, assessing model-fit between math and data, and evaluating multiple mathematical ecologies as an ensemble. To solve a problem, Math Agents might deploy any of the mathematics at their disposal from the digital library of the mathematical infrastructure including machine learning technique such as genetic algorithms, as they solve at different levels of abstraction or computational complexity in the mathematical methods stack. | 2307.02502#67 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 68 | Second, the Math Agent blockchain infrastructure can be used for mathematical proofs. Automated theorem proving is already part of the digital mathematical infrastructure and could
Page 19
be further extended with blockchain features. As with path-routed evaluation, a blockchain-based Math Agent cannot advance to the next node (equation) in a mathematical ecology without confirming the current node, and proof technology layers more functionality onto this. The blockchain step-by-step retraceable transaction log is already conducive to the likewise step-by- step structure of theorem proving. In a more formal blockchain mathematical proof structure, the Math Agent could execute a transaction at each node confirming the validation path, transactionally enacted with an administrative allotment of MathProofCoin. | 2307.02502#68 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 69 | The blockchain audit log is useful not only to document the mathematical proof (in the step-by- step format of proofs), but also for a host of related administrative functions. These could include the automatic minting of new NFTs for proofs in IP (intellectual property) discovery blockchains (MathChains, both MathProofChains and MathTheoremChains), and the logging of unique identifiers in a theorem-addressing system to facilitate finding and calling such theorems later in the digital library of the mathematical infrastructure. The ownership of such proofs might reside with the Math Agent discovering them. The role of MathProofCoin as a token has further potential uses in credit-assignment tracking of how contributed theorems are used (similar to program code use tracking in subsequent software applications), and as a resource allocation mechanism for Math Agent renumeration and as a contribution barometer for further resources to be allocated to the development of needed and promising mathematical ecologies. | 2307.02502#69 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 70 | Economics is a set of principles for interacting in graphs. Aside from conducting proofs on blockchains with Math Agents, there are other related benefits of managing the AI Agent ecosystem with blockchains. The proliferation of AI entities suggests AI registries to track behavior, development, and societal impact, including from a legal, regulatory, and liability perspective. AI Agents could be registered as operating entities in AI Blockchain Registries. There could be AI-related analogs to GAAP (Generally-Accepted Accounting Principles) in the form of GAAiP (Generally-Accepted AI Principles), with a framework of reporting requirements and annual audit overseen by the âFINRAâ (Financial Industry Regulatory Authority) of AI, âFAiNRA.â AI registries as an element of the computational infrastructure can be envisioned to orchestrate AI entities with verified identity and liability accountability. Lawsuits involving AI entities can be imagined: who is liable â the Cigna AI virtual patient modeling bot? As engineers sign bridges and bioengineers sign synthetic biology creations, AI products could be tracked likewise, with a variety of AI certifications in the vein of CC-Licenses for AI | 2307.02502#70 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 71 | bioengineers sign synthetic biology creations, AI products could be tracked likewise, with a variety of AI certifications in the vein of CC-Licenses for AI ethics adherence and behavioral conduct. Tokens allocated to registered AI entities, AIcoin, could be used for administrative matters such as the cost of registration and compliance (to fund FAiNRA) as well as multiagent coordination. Blockchain consensus generation among agents could proceed with a peer mining model (transaction confirmation is a utility function not a wealth-generation financial incentive as any would-be network user must confirms two other transactions (randomly-assigned) before being able to submit their own transaction to the network. AIcoin could be instantiated with lifecycle management demurrage-type principles so that currency allotments expire on an annual basis, thus constructing the token as a purely administrative mechanism disallowing accretion and economic control. | 2307.02502#71 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 72 | Quantum Mathematical Infrastructure The fact that machine learning models already incorporate physics-based energy principles is conducive to a potential AI-quantum computing convergence. Lowest-energy formulations are important in both machine learning and quantum mechanics, and in addition, energy and entropy are related terms. In classical machine learning in the Boltzmann machine model, algorithmPage 20
generated output is evaluated based on loss functions, gradient descent, and lowest-energy probability. In the Born machine as the quantum analog, probabilistic quantum system output is similarly evaluated with the Born rule (the probability density of finding a particle at a given point, calculated by squaring wavefunction amplitudes). | 2307.02502#72 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 73 | Energy and entropy are related in that entropy refers to the number of possible microstates of a system and measures how much energy has been dispersed in a process. Since energy flows from high to low, entropy tends to increase with time (absent the injection of new energy, a desk becomes messier over time not cleaner). In information theory, entropy indicates the number of bits (qubits) required to send a message given some error rate, as Shannon entropy in the classical setting, and von Neumann entropy (taking the minimum over all measurement bases of Shannon entropy) and Rényi entropy (the generalization of Shannon, Hartley, collision, and minimum entropy) in the quantum setting. The Heisenberg uncertainty relation (trade-offs between partner properties such as position-momentum and time-energy) is also employed operationally as an efficient means of calculating entropy (Yunger et al., 2019). | 2307.02502#73 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 74 | Embedding in the generic mathematical sense is used in quantum physics to isolate or freeze parts of a system to facilitate calculations in the overall system. The core quantum object, the density matrix (all the information of a quantum state), is treated through the density matrix embedding theory (many-body embedding of arbitrary fragments of a quantum system) and the density matrix renormalization group (finding the lowest-energy Hamiltonian in a system). In one project, a team embeds the DMRG algorithm in an environment to solve quantum chemistry problems (Dresselhaus et al., 2014). In another project, the team couples a polarized embedding approach to the DMRG to select a region within a larger quantum system while still modeling the surrounding environment. (Hedegard & Reiher, 2016) | 2307.02502#74 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 75 | These ideas are relevant in the sense that AI tools such as Math Agents are implicated in integrating the classical and quantum infrastructure together in a potential era of quantum computing. As part of the digital infrastructure, Math Agents may be able to operate more translationally than humans between classical, quantum, and relativistic domains. Like other smart network setups, quantum computational problems are solved in graphs. Energy-entropy formulations provide a higher-level yet still well-formed (tied to math and physics) level of abstraction for the expedient solving of a system at the right scale tier. Renormalization techniques may be applied to the graph itself, as relating energy in the graph is similar to relating free energy across tiers in a biosystem, or symmetry across tiers in the universe. Energy-entropy formulations could become the abstraction layer at which graph problems are solved. As the Boltzmann machine centers on the lowest-energy gradient descent, so too quantum graph technologies solve for the lowest-energy activity level in the graph. The blockchain math agent evaluating equations, proving theorems, and discovering new algorithms can path-route through the lowest-energy configuration of | 2307.02502#75 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 76 | the graph. The blockchain math agent evaluating equations, proving theorems, and discovering new algorithms can path-route through the lowest-energy configuration of the graph, or conversely, when the objective is the opposite structure, the highest-entropy configuration of the graph (in quantum-secure lattice-based cryptography). The idea is having digital infrastructure that incorporates natural energy-based formulations to a greater extent (not just Feynmanâs computing with atomic object but also principles of atomic energy). In practice, the Math Agent could evaluate equations as the lowest- energy path through the math space. In other future work, there could be more extensive non- classical formulations of embeddings in hyperbolic vector space, and with non-linear time. | 2307.02502#76 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 77 | The Network is the Computer
Page 21
The ânetwork is the computerâ scenario is the natural progression endpoint in which active cognitive agent functionality is incorporated into network itself so that network-informed information states drive the infrastructure. It would be indistinguishable whether there is an agent running on the network or if it is an agent-enabled network. The Math Agent as an actor- observer in the cognitive architecture could operate as an overlay to networks or within networks. Instead of AI agents running on the network engaging data corpora (math, language, images), the cognitive functionality could be implemented directly into the network itself.
The more speculative farther future could be one in which the network is alive, in this sense, with mathematics, with computing; the network is the computer; the network is the math. The level of the computational stack at which the Math Agent is deployed is less relevant that the role it fulfills in validating, affirming, safeguarding the content in various digital possibility spaces and generating, integrating, extending the knowledge base of how mathematical ecologies are related to one another as a core digital mathematical infrastructural tool. | 2307.02502#77 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 78 | From the network point of view, in the network-eye view, the network is the observer-agent. Traditionally the network can interact with data but not math; in the network agent frame, the network can also interact with math (or any formal data corpora). Computation could be outsourced to the network for certain operations instead of the traditional edict of keeping the network exclusively for transport. The network could be reconceived as not simply a dumb transport layer, but rather itself as the location of richer levels of computation. Instead of computation only taking place at network ends for transport efficiency, there could be some still- efficient smart processing taking place in the network itself, as smart infrastructure layers. This would be within the standard 7-layer OSI network stack which includes abstraction layers: physical, data link, network, transport, session, presentation and application, which are morphing to accommodate the cognitive infrastructure of AI technologies (AI chips) and quantum networks (entanglement heralding, quantum key distribution) | 2307.02502#78 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 79 | âMath networksâ could be more extensively developed as a smart network layer with mathematical well-formedness functionality. Higher mathematical formulations could be incorporated into the computational infrastructure in the concept of âmath networksâ as a math network layer which includes AI-readable mathematical embeddings orchestrated, operated, and maintained by Math Agents. Math networks could join other ânetwork is a living graphâ ideas such as network-informed information states that enable self-propagated action-taking by other network-based agent-entities. The Math Agent and network-enabled AI functionality are examples of the emerging cognitive architecture (Shapiro, 2021) in that Math Agents (AI bots) could run as actor-observer operators on the digital mathematical smart network infrastructure, finding new paths through the graph of the mathematical possibility space. | 2307.02502#79 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 80 | Risks and Limitations There are many different risks and limitations associated with this work. First and foremost is at the level of AI in that any AI project must consider AI Alignment, taking all steps to produce AI that has broadly humanity-benefiting values. The current work enacts this ethos by being pointed at widespread democratized access to mathematics and disease-preventing healthy well-being. This work follows and endorses initiatives underway to establish global AI regulatory agencies with innovation sandboxes, registries, and accountability frameworks (FLI, 2023). Within this context, the current project aims to produce easy-to-use accessible interfaces to mathematics as a digital data corpus for the implementation of these tools to solve global health problems in
Page 22
delivering high quality of life and equitable economic impact. Other work discusses the Math Agent and human-AI entities in the responsible development of quantum intelligence (AI- enabled technologies in quantum-classical-relativistic domains) (Swan & dos Santos, 2023). | 2307.02502#80 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 81 | Second, at the level of the current project, there are issues related to the proposal of a digital mathematical infrastructure and AI-enabled tools such as Math Agents, equation clusters, and mathematical embeddings. The first point is that AI functionality is constantly evolving, and any solution may face âimmediate obsolescence.â However, this does not invalidate the need to create prototypes that harness AI technologies in new ways, including for human comfort and understanding. This project demonstrates the mathematical embedding in theory and practice, as a potential precursor step to embeddings starting to be included as standard functionality in AI engines such as GPT. The second point is the claim that just because math is produced in consumable digital units does not make it any less recalcitrant to the general human understanding. However, the goal is to render mathematics human-deployable without having to be human-understandable, in the analogy of an automobile (trusted operation without detailed knowledge). At present, mathematics is a specialist-access only data corpus, but could be a general-access tool, as it occurs to more endusers that âThereâs a math for that!â | 2307.02502#81 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 82 | Conclusion The current moment may be one of a period of accelerated computational infrastructure build- out, the audience of whom is AI first and primarily and humans second. The surprise is that LLM functionality is for AIs, not for humans, serving as a linguistic interface for AI access to all digitally-rendered languages including human natural language, programmatic code, and mathematics. The research question is how AI agents may be deployed to elucidate these formal spaces and discover new ones, all towards broadly humanity-serving aims. The implication is AI-based tool deployment to hasten scientific discovery in biology, energy, and space science, for example in new cloud-based high-sensitivity use cases such as personal brain files. | 2307.02502#82 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 83 | Smart network infrastructure is becoming cognitive infrastructure â the network is the computer. Instead of smart technologies running on networks (AI, machine learning, blockchains, quantum computing), smart technologies are becoming the network; the network is becoming a cognitive agent. Whereas human observer-agents interact with data, AI observer-agents interact with math. Any observer-agent is simply accessing ârealityâ through whatever set of perceptually- constrained goggles they have, all of which are simply datapoints as each entity reaches out to describe âthe elephantâ of the encountered world. The idea of different user audiences accessing differentially-viewable formal spaces and technology-enabled levels in the same overall reality is explored in the concept of user-selected tech-locks (Schroeder, 2005), and also by Kant (1781). | 2307.02502#83 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 84 | We have long been using technology tools to extend human perception beyond the evolutionary Kantian goggles of sensory experience in 3D space and 1D time. Telescopes and microscopes reveal scales of reality that are not directly human-perceivable, and how matter properties are different in quantum mechanical and relativistic domains. There is an awareness that not only matter properties, but also time and space are fundamentally different in the âPlanck spaceâ of quantum mechanical and relativistic domains, properties which are necessarily incorporated into the modern computational infrastructure (e.g. quantum computing, GPS). In a Copernican shift, the familiar everyday 3D space and 1D time are unseated as the norm. There is a growing awareness that Kantian goggles are merely one actor-observer interface on a larger reality with
Page 23
different flavors of space and time; spherical-flat-hyperbolic space and linear time as a user- selected parsing mechanism through possible simultaneity. | 2307.02502#84 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 85 | Page 23
different flavors of space and time; spherical-flat-hyperbolic space and linear time as a user- selected parsing mechanism through possible simultaneity.
Although humans have always been inventing various kinds of goggles to extend perceptual reach, the mathematical view provides a different kind of Kantian goggles as a perceptual interface to reality, not just a quantitative scale revealing of the big and the small with the telescope and the microscope, but qualitative access to a more foundational structure of reality. We do not human-see mathematics, but Math Agents could provide the interface. Mathematics is argued to be foundational (quark properties are quantitative; distinguished exclusively by numbers (mass, charge, and spin) (Tegmark, 2021)), but what is new is having a potential way to interact with reality at this level and test the claim. Math Agents could be Kantian goggles that extend human reach to other formal spaces (programmatic code, mathematics, computational complexity), and also possibly other forms of intelligence, artificial, computational, and quantum, and biological (overlooked due to lack of the right translational interface). | 2307.02502#85 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 86 | The result of this work is to introduce a suite of AI math tools â mathematical embeddings, equation clusters, mathematical ecologies and mathscapes, and AI Math Agents â to enable the further development of the digital mathematical infrastructure. This could also include the mathematics-certified symbol F(x) as an icon to designate digital mathematically-verified content. The result of a cognitive infrastructure that includes mathematics in a more readily usable form is a modern advance which delivers democratized access to mathematics to a wide range of agential audiences (human and AI) at various theoretical and practical levels. Mathematics is widely regarded as a high-value discovery, but one that is under-developed, under-deployed, and perhaps near the limit non-AI-aided methods. A digital mathematical infrastructure may have uses not only in expanding mathematical discovery, deployment in a new larger slate of human-facing challenges in health, energy, and space, but also as a high- validation technology for achieving AI alignment goals with humanity-serving values and global health-preserving well-being.
# Supplemental Information Software Code Availability Open-source Code Repository: https://github.com/eric-roland/diygenomics | 2307.02502#86 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 87 | # Supplemental Information Software Code Availability Open-source Code Repository: https://github.com/eric-roland/diygenomics
Data Availability Whole-human Genome Citizen 1: (Nebula): https://www.diygenomics.org/citizengenomics/rsid_query.php (Citizen 17) Citizen 2: (Illumina): http://startcodon.org/ https://www.diygenomics.org/citizengenomics/rsid_query.php (Citizen 18)
Glossary Agent: AI (artificial intelligence) predictive entity (set of algorithms) tasked with learning, problem-solving, and behavior-updating in a context per a rewards-driven action-taking policy AI Alignment: AI compatibility with human values (responsible broad humanity-serving values) Autonomous Cognitive Entity: AI agent empowered to undertake autonomous operations Cognitive Infrastructure: computational infrastructure that incorporates AI agent capabilities Computational Infrastructure: concept of digital reality as a collection of formal methods
Page 24 | 2307.02502#87 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 88 | Digital Mathematical Infrastructure: mathematics digitized as an easy-access data corpus, learned and mobilized by Math Agents for insight and deployment operations Formal methods: systematic mathematically rigorous techniques for operating in a context Formalization: rendering a domain with formal methods (systematic mathematical techniques) Formalization space: the possibility space of all formal (systematic) approaches: mathematical, algorithmic, programmatic, information-theoretic, graph-theoretic, computational complexity Embedding: character string representation of a data element in high dimensional vector space Equation Cluster: similar equations grouped in mathematical ecology embedding visualization HITL (human in the loop): AI results supervised and validated by a human Human-AI Entities: RLHF partnerships operating as substrate-agnostic scale-free intelligence Math Agent: AI agent operating in digital mathematical domain to identify, represent, analyze, integrate, write, discover, solve, theorem-prove, steward, and care-take mathematical ecologies Mathematical ecology (mathscape): set of related mathematical equations Mathematical embedding: mathematical entity (symbol, equation) represented as a character | 2307.02502#88 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 89 | ecologies Mathematical ecology (mathscape): set of related mathematical equations Mathematical embedding: mathematical entity (symbol, equation) represented as a character string in vector space for high-dimensional analysis in AI-based machine learning systems RLHF (reinforcement learning human feedback): iterative dialogical human-AI interaction; AI agents that have a learned model of the environment, a decision-making policy, and a reward prediction mechanism, engaged in iterative feedback loops with humans Smart Network technologies: AI agents, machine learning, blockchains, quantum computing available as standard formal methods deployed in the computational infrastructure | 2307.02502#89 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 90 | References Banuelos, M. & Sindi, S. (2018). Modeling transposable element dynamics with fragmentation equations. Mathematical Biosciences. 302:46â66. https://doi.org/10.1016/j.mbs.2018.05.009.
Banwarth-Kuhn, M. & Sindi, S. (2019). Multi-Scale Mathematical Modeling of Prion Aggregate Dynamics and Phenotypes in Yeast Colonies. Biomathematics. 1-30. http://dx.doi.org/10.5772/intechopen.88575.
Batzoglou, S. (2023). Large Language Models in Molecular Biology: Deciphering the language of biology, from DNA to cells to human health. Towards Data Science. 2 June 20213. https://towardsdatascience.com/large-language-models-in-molecular-biology-9eb6b65d8a30.
Capozziello, S., Pincak, R., Kanjamapornkul, K., and Saridakis, E.N. (2018). The Chern-Simons current in systems of DNA-RNA transcriptions. Annalen der Physik. 530(4):1700271. doi:10.1002/andp.201700271. | 2307.02502#90 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 91 | Cheong, B. (2023). The Wilds of Artificial Intelligence. Entitled Opinions. https://entitled- opinions.com/2023/03/13/the-wilds-of-artificial-intelligence-with-bryan-cheong/.
Chern, S.-S. & Simons, J. (1974). Characteristic Forms and Geometric Invariants. Annals of Mathematics. Second Series. 99(1):48-69. https://doi.org/10.2307/1971013.
Cottrell, S.S. (2012). How Many Theorems Are There? Ask a Mathematician/Ask a Physicist. https://www.askamathematician.com/2012/11/q-how-many-theorems-are-there/.
Depue, W. (2023). Embeddings for every research paper on the arXiv. Twitter. 25 May 2023.
Page 25
https://twitter.com/willdepue/status/1661781355452325889?lang=en. arXiv Title and Abstract embeddings available at: https://alex.macrocosm.so/download. | 2307.02502#91 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 92 | Dooling, B., Johnson, N.R., Joshi, M. et al. (2022). Interrogating the role of APOE4 in Alzheimerâs disease and Down syndrome using human induced pluripotent stem cells (hiPSC)- derived cerebral organoids. Alzheimerâs & Dementia. https://doi.org/10.1002/alz.068061.
Dos Santos, R.P. (2019). Consensus Algorithms: A Matter of Complexity? Swan, M., Potts, J., Takagi, S., Witte, F. & Tasca, P., Eds. Blockchain Economics: Implications of Distributed Ledgers - Markets, Communications Networks, and Algorithmic Reality. London: World Scientific. Pp. 147-170.
Dresselhaus, T., Neugebauer, J., Knecht, S. et al. (2014). Self-Consistent Embedding of Density- Matrix Renormalization Group Wavefunctions in a Density Functional Environment. arXiv:1409.1953v1.
Eskildsen, S. (2023). Parsing Math Equations. Twitter. 26 June 2023. https://twitter.com/Sirupsen/status/1673309920769323008?cxt=HHwWgMC9qbyg5bguAAAA. | 2307.02502#92 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 93 | Fornari, S., Schafer, A., Kuhl, E. & Goriely, A. (2020). Spatially-extended nucleation- aggregation-fragmentation models for the dynamics of prion-like neurodegenerative protein- spreading in the brain and its connectome. Journal of Theoretical Biology 486:110102. https://doi.org/10.1016/j.jtbi.2019.110102.
Future of Life Institute (FLI). (2023). Policymaking in the Pause What can policymakers do now to combat risks from advanced AI systems? 19 April 2023 https://futureoflife.org/wp- content/uploads/2023/04/FLI_Policymaking_In_The_Pause.pdf.
Guo, M., Niu, C., Tian, Y. & Zhang, H. (2016). Modave lectures on applied AdS/CFT with numerics. In: Proceedings, 11th Modave Summer School in Mathematical Physics. arXiv:1601.00257v2, PoS Modave2015(2016)003. | 2307.02502#93 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 94 | Hales, T.C. (2005). A proof of the Kepler conjecture. Annals of Mathematics. 2(162):1063â 1183. https://annals.math.princeton.edu/wp-content/uploads/annals-v162-n3-p01.pdf.
Hales, T.C., Adams, M., Bauer, G. et al. (2017). A Formal Proof of the Kepler Conjecture. Forum of Mathematics, Pi. 5(e2). https://doi.org/10.1017/fmp.2017.1.
Hao, W. & Friedman, A. (2016). Mathematical model on Alzheimerâs disease. BMC Syst Biol 10(108). https://doi.org/10.1186/s12918-016-0348-2.
Hashimoto, K., Hu, H.-Y. & You, Y.-Z. (2021). Neural ordinary differential equation and holographic quantum chromodynamics. Mach Learn: Sci. Technol. 2(03):035011.
Hedegard, E.D. & Reiher, M. (2016). Polarizable Embedding Density Matrix Renormalization Group. J. Chem. Theory Comput. 12(9):4242â4253. https://doi.org/10.1021/acs.jctc.6b00476.
Page 26 | 2307.02502#94 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 95 | Page 26
Heule, M. (2018). Schur Number Five. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). https://doi.org/10.1609/aaai.v32i1.12209.
Jumper, J., Evans, R., Pritzel, A. et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature. 596:583â589. https://doi.org/10.1038/s41586-021-03819-2.
Kant, I. (1988 [1781/1787]). Critique of Pure Reason. Trans. & Ed. P. Guyer & A.W. Wood. Cambridge: Cambridge University Press.
Kaplan, J. (2016). Lectures on AdS/CFT from the bottom up. Johns Hopkins Lecture Course.
Karpathy, A. (2017). Software 2.0. Medium. 11 November 2017. https://karpathy.medium.com/software-2-0-a64152b37c35.
Kermack, W. & McKendrick, A (1991). Contributions to the mathematical theory of epidemics â I. Bulletin of Mathematical Biology. 53(1â2):33â55. doi:10.1007/BF02464423. | 2307.02502#95 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 96 | Krantz, S.G. (2007). The Proof is in the Pudding: A Look at the Changing Nature of Mathematical Proof. Cham Switzerland: Springer.
Levin, M. (2023). Darwinâs agential materials: evolutionary implications of multiscale competency in developmental biology. Cell Mol Life Sci. 80(6):142. doi: 10.1007/s00018-023- 04790-z.
Li, Y. & Gao, X. (2019). PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks. bioRxiv: http://dx.doi.org/10.1101/532226.
Maldacena, J.M. (1999). The large N limit of superconformal field theories and supergravity. Intl. J. Theor. Phys. 38(4):1113â1133. doi:10.1023/A:1026654312961.
Martins, N.R.B., Angelica, A., Chakravarthy, K. et al. (2019). Human Brain/Cloud Interface. Front. Neurosci. 13(112):1â23. doi:10.3389/fnins.2019.00112. | 2307.02502#96 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 97 | Meijer, H.J., Truong, J. & Karimi, R. (2021). Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF. arXiv:2107.05151v1.
Nguyen, E., Poli, M., Faizi, F. et al. (2023). HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. arXiv:2306.15794v1.
Schroeder, K. (2005). Lady of Mazes. New York: Tor.
Sejnowski, T.J. (2020). The unreasonable effectiveness of deep learning in artificial intelligence. Proc. Natl. Acad. Sci. U.S.A. 117(48): 30033â30038. doi/10.1073/pnas.1907373117.
Shapiro, D. (2021). Natural Language Cognitive Architecture: A Prototype Artificial General Intelligence. https://github.com/daveshap/NaturalLanguageCognitiveArchitecture.
Page 27
Steingart, A. (2022). Axiomatics: Mathematical Thought and High Modernism. Chicago: University Chicago Press. | 2307.02502#97 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 98 | Page 27
Steingart, A. (2022). Axiomatics: Mathematical Thought and High Modernism. Chicago: University Chicago Press.
Sun, N., Akay, L.A., Murdock, M.H. et al. (2023.) Single-nucleus multi-region transcriptomic analysis of brain vasculature in Alzheimerâs disease. Nat Neurosci. 26:970â982. https://doi.org/10.1038/s41593-023-01334-3.
Swan, M. & dos Santos, R.P. (2023). Quantum Intelligence: Responsible Human-AI Entities. AAAI 2023 Spring Symposium: Socially Responsible AI for Well-being. South San Francisco CA, March 27â29, 2023. https://www.slideshare.net/lablogga/quantumintelligence- responsible-humanai-entities.
Swan, M., dos Santos, R.P., Lebedev, M.A. & Witte, F. (2022a). Quantum Computing for the Brain. London: World Scientific. https://doi.org/10.1142/q0313. | 2307.02502#98 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.02502 | 99 | Swan, M., dos Santos, R.P. & Witte, F. (2022b). Quantum Neurobiology. Quantum Reports. 4(1):107â127. https://doi.org/10.3390/quantum4010008.
Tegmark, M. (2021). Our Mathematical Universe: My Quest for the Ultimate Nature of Reality. New York: Knopf.
Thompson, T.B., Meisl, G., Knowles, T.P.J. et al. (2021). The role of clearance mechanisms in the kinetics of pathological protein aggregation involved in neurodegenerative diseases. J. Chem. Phys. 154:125101. https://doi.org/10.1063/5.0031650.
Wang, Z.J., Hohman, F. & Chau, D.H. (2023). WIZMAP: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings. arXiv:2306.09328v1. https://github.com/poloclub/wizmap.
Wyss, A. & Hidalgo, A. (2023). Modeling COVID-19 Using a Modified SVIR Compartmental Model and LSTM-Estimated Parameters. Mathematics. 11:1436. https://doi.org/10.3390/math11061436. | 2307.02502#99 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem. | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | [
{
"id": "1601.00257"
},
{
"id": "2306.15626"
},
{
"id": "2306.09328"
},
{
"id": "2306.15794"
},
{
"id": "2107.05151"
}
] |
2307.01135 | 0 | # ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience
Ruiyun (Rayna) Xu, Yue (Katherine) Feng, and Hailiang Chen*
July 2023
# Abstract
The advent of ChatGPT, a large language model-powered chatbot, has prompted questions about its potential implications for traditional search engines. In this study, we investigate the differences in user behavior when employing search engines and chatbot tools for information-seeking tasks. We carry out a randomized online experiment, dividing participants into two groups: one using a ChatGPT-like tool and the other using a Google Search-like tool. Our findings reveal that the ChatGPT group consistently spends less time on all tasks, with no significant difference in overall task performance between the groups. Notably, ChatGPT levels user search performance across different education levels and excels in answering straightforward questions and providing general solutions but falls short in fact-checking tasks. Users perceive ChatGPTâs responses as having higher information quality compared to Google Search, despite displaying a similar level of trust in both tools. Furthermore, participants using ChatGPT report significantly better user experiences in terms of usefulness, enjoyment, and satisfaction, while perceived ease of use remains comparable between the two tools. However, ChatGPT may also lead to overreliance and generate or replicate misinformation, yielding inconsistent results. Our study offers valuable insights for search engine management and highlights opportunities for integrating chatbot technologies into | 2307.01135#0 | ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience | The advent of ChatGPT, a large language model-powered chatbot, has prompted
questions about its potential implications for traditional search engines. In
this study, we investigate the differences in user behavior when employing
search engines and chatbot tools for information-seeking tasks. We carry out a
randomized online experiment, dividing participants into two groups: one using
a ChatGPT-like tool and the other using a Google Search-like tool. Our findings
reveal that the ChatGPT group consistently spends less time on all tasks, with
no significant difference in overall task performance between the groups.
Notably, ChatGPT levels user search performance across different education
levels and excels in answering straightforward questions and providing general
solutions but falls short in fact-checking tasks. Users perceive ChatGPT's
responses as having higher information quality compared to Google Search,
despite displaying a similar level of trust in both tools. Furthermore,
participants using ChatGPT report significantly better user experiences in
terms of usefulness, enjoyment, and satisfaction, while perceived ease of use
remains comparable between the two tools. However, ChatGPT may also lead to
overreliance and generate or replicate misinformation, yielding inconsistent
results. Our study offers valuable insights for search engine management and
highlights opportunities for integrating chatbot technologies into search
engine designs. | http://arxiv.org/pdf/2307.01135 | Ruiyun Xu, Yue Feng, Hailiang Chen | cs.AI, cs.HC, cs.IR | 30 pages, 5 figures, 2 tables | null | cs.AI | 20230703 | 20230703 | [
{
"id": "2304.07619"
},
{
"id": "2303.17564"
},
{
"id": "2303.10130"
}
] |
2307.01142 | 0 | 3 2 0 2
l u J 3 ] C H . s c [
1 v 2 4 1 1 0 . 7 0 3 2 : v i X r a
Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances Andrew Tran Temple University Philadelphia, PA, USA [email protected]
Ziheng Huang University of CaliforniaâSan Diego La Jolla, CA, USA [email protected]
Seth Bernstein Temple University Philadelphia, PA, USA [email protected]
Dan Mogil Temple University Philadelphia, PA, USA [email protected]
2 5 e Static prompt Template-based prompt Free-form prompt eB âOrdering an item from a menuâ "Selecting from a buffet of optionsâ "Making a special request to the chefâ § a 2 @ critica D Constructive Is my statement of purpose too long? E Caer Feedback Type 2 | #Give me feedback starting with something # Give me <critical> <suggestions> # <Is my statement of purpose too long?>: $| positive, followed by actionable criticism for the text below: *insert text* £| that can help me improve the text below: *insert text* e *insert text* | 2307.01142#0 | Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances | To help users do complex work, researchers have developed techniques to
integrate AI and human intelligence into user interfaces (UIs). With the recent
introduction of large language models (LLMs), which can generate text in
response to a natural language prompt, there are new opportunities to consider
how to integrate LLMs into UIs. We present Prompt Middleware, a framework for
generating prompts for LLMs based on UI affordances. These include prompts that
are predefined by experts (static prompts), generated from templates with
fill-in options in the UI (template-based prompts), or created from scratch
(free-form prompts). We demonstrate this framework with FeedbackBuffet, a
writing assistant that automatically generates feedback based on a user's text
input. Inspired by prior research showing how templates can help non-experts
perform more like experts, FeedbackBuffet leverages template-based prompt
middleware to enable feedback seekers to specify the types of feedback they
want to receive as options in a UI. These options are composed using a template
to form a feedback request prompt to GPT-3. We conclude with a discussion about
how Prompt Middleware can help developers integrate LLMs into UIs. | http://arxiv.org/pdf/2307.01142 | Stephen MacNeil, Andrew Tran, Joanne Kim, Ziheng Huang, Seth Bernstein, Dan Mogil | cs.HC | null | null | cs.HC | 20230703 | 20230703 | [] |
2307.01135 | 1 | # search engine designs.
Keywords: ChatGPT, generative AI, Google, search engines, chatbot, online experiment
* Xu is affiliated with Department of Information Systems and Analytics, Farmer School of Business, Miami University, Oxford, Ohio, USA. Feng is affiliated with Department of Management and Marketing, Faculty of Business, The Hong Kong Polytechnic University, Hong Kong. Chen is affiliated with Artificial Intelligence Research Institute, Faculty of Business and Economics, The University of Hong Kong, Hong Kong. Email: [email protected], [email protected], and [email protected]. All authors contributed equally.
# 1. Introduction | 2307.01135#1 | ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience | The advent of ChatGPT, a large language model-powered chatbot, has prompted
questions about its potential implications for traditional search engines. In
this study, we investigate the differences in user behavior when employing
search engines and chatbot tools for information-seeking tasks. We carry out a
randomized online experiment, dividing participants into two groups: one using
a ChatGPT-like tool and the other using a Google Search-like tool. Our findings
reveal that the ChatGPT group consistently spends less time on all tasks, with
no significant difference in overall task performance between the groups.
Notably, ChatGPT levels user search performance across different education
levels and excels in answering straightforward questions and providing general
solutions but falls short in fact-checking tasks. Users perceive ChatGPT's
responses as having higher information quality compared to Google Search,
despite displaying a similar level of trust in both tools. Furthermore,
participants using ChatGPT report significantly better user experiences in
terms of usefulness, enjoyment, and satisfaction, while perceived ease of use
remains comparable between the two tools. However, ChatGPT may also lead to
overreliance and generate or replicate misinformation, yielding inconsistent
results. Our study offers valuable insights for search engine management and
highlights opportunities for integrating chatbot technologies into search
engine designs. | http://arxiv.org/pdf/2307.01135 | Ruiyun Xu, Yue Feng, Hailiang Chen | cs.AI, cs.HC, cs.IR | 30 pages, 5 figures, 2 tables | null | cs.AI | 20230703 | 20230703 | [
{
"id": "2304.07619"
},
{
"id": "2303.17564"
},
{
"id": "2303.10130"
}
] |
2307.01142 | 1 | Figure 1: Three methods to connect user interface components to large language models. 1) static prompts are predefined prompts that can be selected directly from the UI, 2) template-based prompts generate prompts based on selected options in the UI, 3) free-form prompts provide a direct way of interacting with prompts.
# KEYWORDS large language models, prompt middleware, prompt programming
1 ABSTRACT To help users do complex work, researchers have developed tech- niques to integrate AI and human intelligence into user interfaces (UIs). With the recent introduction of large language models (LLMs), which can generate text in response to a natural language prompt, there are new opportunities to consider how to integrate LLMs into UIs. We present Prompt Middleware, a framework for generating prompts for LLMs based on UI affordances. These include prompts that are predefined by experts (static prompts), generated from templates with fill-in options in the UI (template-based prompts), or created from scratch (free-form prompts). We demonstrate this framework with FeedbackBuffet, a writing assistant that automati- cally generates feedback based on a userâs text input. Inspired by | 2307.01142#1 | Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances | To help users do complex work, researchers have developed techniques to
integrate AI and human intelligence into user interfaces (UIs). With the recent
introduction of large language models (LLMs), which can generate text in
response to a natural language prompt, there are new opportunities to consider
how to integrate LLMs into UIs. We present Prompt Middleware, a framework for
generating prompts for LLMs based on UI affordances. These include prompts that
are predefined by experts (static prompts), generated from templates with
fill-in options in the UI (template-based prompts), or created from scratch
(free-form prompts). We demonstrate this framework with FeedbackBuffet, a
writing assistant that automatically generates feedback based on a user's text
input. Inspired by prior research showing how templates can help non-experts
perform more like experts, FeedbackBuffet leverages template-based prompt
middleware to enable feedback seekers to specify the types of feedback they
want to receive as options in a UI. These options are composed using a template
to form a feedback request prompt to GPT-3. We conclude with a discussion about
how Prompt Middleware can help developers integrate LLMs into UIs. | http://arxiv.org/pdf/2307.01142 | Stephen MacNeil, Andrew Tran, Joanne Kim, Ziheng Huang, Seth Bernstein, Dan Mogil | cs.HC | null | null | cs.HC | 20230703 | 20230703 | [] |
2307.01135 | 2 | # 1. Introduction
In November 2022, OpenAI launched ChatGPT, a chatbot based on the Generative Pre-trained Transformer (GPT) large language model. ChatGPTâs rapid rise in popularity underscores the transformative potential of generative AI in various industries and applications. In February 2023, Microsoft integrated ChatGPT into its Bing search engine, combining chat and search functionalities in a unique manner (Microsoft 2023). Following this integration, Bing experienced a significant traffic increase of 15.8% from February to March, while Googleâs traffic declined by nearly 1% during the same period (Reuters 2023). Considering that each 1% of search advertising market share represents $2 billion in annual revenue (Yahoo! Finance 2023), this notable shift raises concerns about the impact of ChatGPT-like products on traditional search engines and the future of search and information discovery. | 2307.01135#2 | ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience | The advent of ChatGPT, a large language model-powered chatbot, has prompted
questions about its potential implications for traditional search engines. In
this study, we investigate the differences in user behavior when employing
search engines and chatbot tools for information-seeking tasks. We carry out a
randomized online experiment, dividing participants into two groups: one using
a ChatGPT-like tool and the other using a Google Search-like tool. Our findings
reveal that the ChatGPT group consistently spends less time on all tasks, with
no significant difference in overall task performance between the groups.
Notably, ChatGPT levels user search performance across different education
levels and excels in answering straightforward questions and providing general
solutions but falls short in fact-checking tasks. Users perceive ChatGPT's
responses as having higher information quality compared to Google Search,
despite displaying a similar level of trust in both tools. Furthermore,
participants using ChatGPT report significantly better user experiences in
terms of usefulness, enjoyment, and satisfaction, while perceived ease of use
remains comparable between the two tools. However, ChatGPT may also lead to
overreliance and generate or replicate misinformation, yielding inconsistent
results. Our study offers valuable insights for search engine management and
highlights opportunities for integrating chatbot technologies into search
engine designs. | http://arxiv.org/pdf/2307.01135 | Ruiyun Xu, Yue Feng, Hailiang Chen | cs.AI, cs.HC, cs.IR | 30 pages, 5 figures, 2 tables | null | cs.AI | 20230703 | 20230703 | [
{
"id": "2304.07619"
},
{
"id": "2303.17564"
},
{
"id": "2303.10130"
}
] |
2307.01142 | 2 | Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). UIST 2022, Oct 20âNov 2, 2022, Bend, Oregon © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-XXXX-X/18/06.
prior research showing how templates can help non-experts per- form more like experts, FeedbackBuffet leverages template-based prompt middleware to enable feedback seekers to specify the types of feedback they want to receive as options in a UI. These options are composed using a template to form a feedback request prompt to GPT-3. We conclude with a discussion about how Prompt Mid- dleware can help developers integrate LLMs into UIs. | 2307.01142#2 | Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances | To help users do complex work, researchers have developed techniques to
integrate AI and human intelligence into user interfaces (UIs). With the recent
introduction of large language models (LLMs), which can generate text in
response to a natural language prompt, there are new opportunities to consider
how to integrate LLMs into UIs. We present Prompt Middleware, a framework for
generating prompts for LLMs based on UI affordances. These include prompts that
are predefined by experts (static prompts), generated from templates with
fill-in options in the UI (template-based prompts), or created from scratch
(free-form prompts). We demonstrate this framework with FeedbackBuffet, a
writing assistant that automatically generates feedback based on a user's text
input. Inspired by prior research showing how templates can help non-experts
perform more like experts, FeedbackBuffet leverages template-based prompt
middleware to enable feedback seekers to specify the types of feedback they
want to receive as options in a UI. These options are composed using a template
to form a feedback request prompt to GPT-3. We conclude with a discussion about
how Prompt Middleware can help developers integrate LLMs into UIs. | http://arxiv.org/pdf/2307.01142 | Stephen MacNeil, Andrew Tran, Joanne Kim, Ziheng Huang, Seth Bernstein, Dan Mogil | cs.HC | null | null | cs.HC | 20230703 | 20230703 | [] |
2307.01135 | 3 | Traditional search engines and ChatGPT-like systems differ in their information retrieval approaches. Google, the worldâs leading search engine, relies on keyword search and matching, presenting users with a list of relevant links. In contrast, ChatGPT employs a conversation-based approach, enabling users to pose queries in natural language. While Googleâs speed is impressive, users must filter through search results individually, which can be time-consuming. ChatGPT, however, aims to understand user intent and provide organized responses in complete sentences, offering a more user-friendly and intuitive search experience. Nevertheless, ChatGPT has potential drawbacks, such as slower response times and the possibility of false or misleading information, in contrast to traditional search engines that offer faster response times and more controlled results. As the landscape of search and information discovery evolves, questions remain
unanswered regarding the performance and user experience of ChatGPT-like systems compared to traditional search engines. It is crucial to examine how ChatGPTâs conversational nature affects
1 | 2307.01135#3 | ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience | The advent of ChatGPT, a large language model-powered chatbot, has prompted
questions about its potential implications for traditional search engines. In
this study, we investigate the differences in user behavior when employing
search engines and chatbot tools for information-seeking tasks. We carry out a
randomized online experiment, dividing participants into two groups: one using
a ChatGPT-like tool and the other using a Google Search-like tool. Our findings
reveal that the ChatGPT group consistently spends less time on all tasks, with
no significant difference in overall task performance between the groups.
Notably, ChatGPT levels user search performance across different education
levels and excels in answering straightforward questions and providing general
solutions but falls short in fact-checking tasks. Users perceive ChatGPT's
responses as having higher information quality compared to Google Search,
despite displaying a similar level of trust in both tools. Furthermore,
participants using ChatGPT report significantly better user experiences in
terms of usefulness, enjoyment, and satisfaction, while perceived ease of use
remains comparable between the two tools. However, ChatGPT may also lead to
overreliance and generate or replicate misinformation, yielding inconsistent
results. Our study offers valuable insights for search engine management and
highlights opportunities for integrating chatbot technologies into search
engine designs. | http://arxiv.org/pdf/2307.01135 | Ruiyun Xu, Yue Feng, Hailiang Chen | cs.AI, cs.HC, cs.IR | 30 pages, 5 figures, 2 tables | null | cs.AI | 20230703 | 20230703 | [
{
"id": "2304.07619"
},
{
"id": "2303.17564"
},
{
"id": "2303.10130"
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.