Ask HN: Will there ever be a resurgence of interest in symbolic AI? - <AUTHOR> Symbolic AI fell by the wayside at the beginning of the AI winter. More recently, with powerful GPUs making ML and other statistical AI approaches feasible, symbolic AI has not seen anywhere near as much investment.<p>There are still companies I know of that do symbolic AI (such as https://www.cyc.com), but I very rarely hear of new research in the field. <comment><USER2> Employee of Cycorp here. Aside from the current ML hype-train (and the complementary unfashionability of symbolic AI), I think the reason symbolic AI doesn't get as much attention is that it's much more "manual" in a lot of ways. You get more intelligent results, but that's because more conscious human thought went into building the system. As opposed to ML, where you can pretty much just throw data at it (and today's internet companies have _a lot_ of data). Scaling such a system is obviously a major challenge. Currently we support loading "flat data" from DBs into Cyc - the general concepts are hand- crafted and then specific instances are drawn from large databases - and we hope that one day our natural language efforts will enable Cyc to assimilate new, more multifaceted information from the web on its own, but that's still a ways off.

I (and my company) believe in a hybrid approach; it will never be a good idea to use symbolic AI for getting structured data from speech audio or raw images, for example. But once you have those sentences, or those lists of objects, symbolic AI can do a better job of reasoning about them. Pairing ML and symbolics, they can cover each other's weaknesses.

<comment><USER3> What kind of experiments have you guys done that combine symbolic and statistical/ML methods? It sounds like an area ripe for research

<comment><USER2> I know we use ML to "grease the wheels" of inference; i.e., Cyc gains an intuition about what kinds of paths of reasoning to follow when searching for conclusions. I don't know of any higher-level hybridization experiments; I think we only have one ML person on staff and mostly our commercial efforts focus on accentuating what we can do that ML can't, so we haven't had the chance to do many projects where we combine the two as equals.

<comment><USER2> To clarify the above:

"Cyc gains an intuition about what kinds of paths of reasoning to follow when searching for conclusions"

The _possible_ paths come purely from symbolics. But that creates a massive tree of possibilities to explore, so ML is used simply to _prioritize_ among those subtrees.

<comment><USER4> Basically you are learning the heuristic? Do you have any public information on that? That something I have always wanted to work on and really think it could be a shortcut to AGI...

</comment></comment></comment></comment></comment><comment><USER5> I'm not an expert on this, but here's my current understanding:

Symbolic reasoning/AI is fantastic when you have the right concepts/words to describe a domain. Often, the hard ("intelligent") work of understanding a domain and distilling its concepts need to be done by humans. Once this is done, it should in principle be feasible to load this "DSL" into a symbolic reasoning system, to automate the process of deduction.

The challenge is, what happens when you don't have an appropriate distillation of a complex situation? In the late eighties and early nineties, Rodney Brooks and others [1] wrote a series of papers [2] pointing out how symbols (and the definiteness they entail) struggle with modeling the real world. There are some claimed relations to Heideggerian philosophy, but I don't grok that yet. The essential claim is that intelligence needs to be situated (in the particular domain) rather than symbolic (in an abstract domain). The "behavior driven" approach to robotics is stems from that cauldron.

[1]: Authors I'm aware of include Philip Agre, David Chapman, Pattie Maes, and Lucy Suchman.

[2]: For a sampling, see the following papers and related references: "Intelligence without reason", "Intelligence without representation", "Elephants don't play chess".

<comment><USER6> _The essential claim is that intelligence needs to be situated (in the particular domain) rather than symbolic (in an abstract domain)._

I think there is something (a lot) to this. Consider how much of our learning is experiential, and would be hard to put into a purely abstract symbol manipulating system. Taking "falling down" for example. We (past a certain age) _know_ what it means to "fall", because we _have_ fallen. We understand the idea of slipping, losing your balance, stumbling, and falling due to the pull of gravity. We know it hurts (at least potentially), we know that skinned elbows, knees, palms, etc. are a likely consequence, etc. And that experiential learning informs our use of the term "fall" in metaphors and analogies we use in other domains ("the market fell 200 points today, on news from China...") and so on.

This is one reason I like to make a distinction between "human level" intelligence and "human like" intelligence. Human level intelligence is, to my way of thinking, easier to achieve, and has arguably already been achieved depending on how you define intelligence. But human like intelligence, that features that understanding of the natural world, some of what we call "common sense", etc., seems like it would be very hard to achieve without an intelligence that experiences the world like we do.

Anyway, I'm probably way off on a tangent here, since I'm really talking about embodiment, which is related to, but not exact the same as, situated-ness. But that quote reminded me of this line of thinking for whatever reason.

<comment><USER7> I think the opposite is true. Humans think in terms of symbols to model the world around him. A child is born knowing nothing, a completely blank slate, and slowly he learns about his surroundings. He discovers he needs food, he needs to be protected and cared for. He discovers he doesnt like pain. If you talk to a 3 year old child you can have a fairly intelligent conversation about his parents, about his sense of security because this child has built a mental model of the world as a result of being trained by his parents. This kind of training requires context and crossreferencing of information which can only be done by inferencing. You cant train a child by flashing 10,000 pictures at him because pictures are not experience, even adults can be fooled by pictures which are only a 2D representation of 3D concepts of 3D space. So all these experiences that a small child has of knowing about the world come to him symbolically, these symbols model the world and give even a small child the ability to reason about external things and classify them. This is human level intelligence.

Human like intelligence is training a computer to recognize pixel patterns in images so it can make rules and inferences about what these images mean. This is human like intelligence as the resulting program can accomplish human like tasks of recognition without the need for context on what these images might mean. But there is no context involved about any kind of world, this is pure statistical training.

<comment><USER8> > Humans think in terms of symbols to model the world around him. A child is > born knowing nothing, a completely blank slate, and slowly he learns about > his surroundings.

Actually, the research has found that new born infants can perceive all sorts of things, like human faces and emotional communication. There is also a lot of inborn knowledge about social interactions and causality. The embodied cognition idea is looking at how we experience all that.

By the way, Kant demonstrated a couple of centuries ago that the blank slate idea was unworkable.

<comment><USER7> >Actually, the research has found that new born infants can perceive all sorts of things, like human faces and emotional communication.

yes, thats called sensory input.... a child deprived of sensory input when newborn can die because there is nothing there to show the baby of its existence, this the cause of crib death (notice that crib death is not called arm death because a baby doesnt die in the mothers arms)

>There is also a lot of inborn knowledge about social interactions and causality.

no, babies are not born with any knowledge at all of even the existence of society or beings. causality is learned from the result of human experience, causality is not known at birth

<comment><USER9> There's no reason to think the human brain learns things using purely statistical methods, and then turn around and try to argue that evolution cannot encode the same information into the structure of a baby using those exact same methods. Humans have lots of instinctual knowledge; geometry, facial recognition; kinesthetics, emotional processing; affinity for symbolic language and culture, just to name a few. What we don't have is knowledge of the specific details needed for socialization and survival.

</comment></comment></comment></comment></comment></comment><comment><USER10> Hybrid approaches have been getting some interesting results lately[0], and will probably continue to do so, but the approaches between statistical and symbolic AI are so different that these are essentially cross-disciplinary collaborations (and each hybrid system I've seen is essentially a one-off that occupies a unique local maxima).

I suspect that eventually there will be an "ImageNet Moment" of sorts starring a statistical/symbolic hybrid system and we'll see an explosion of interest in a family of architectures (but it hasn't happened yet).

[0] [http://news.mit.edu/2019/teaching-machines-to-reason- about-w...](http://news.mit.edu/2019/teaching-machines-to-reason-about-what- they-see-0402)

</comment><comment><USER11> Well, symbolic AI people also work on probabilistic reasoning. The production level example is ProbLog[1][2], used in genetics. There is even DeepProbLog[3], adding deep learning in the mix. The only problem both implemented in Python. I hope there will be alternatives in native languages. Scryer Prolog[4] might become this implementation one day (it is written in Rust). Another approach is to extend vanilla Prolog, like cplint[5] does.

[1] [https://dtai.cs.kuleuven.be/problog/](https://dtai.cs.kuleuven.be/problog/)

[2] [https://bitbucket.org/problog/problog](https://bitbucket.org/problog/problog)

[3] [https://bitbucket.org/problog/deepproblog](https://bitbucket.org/problog/deepproblog)

[4] [https://github.com/mthom/scryer-prolog](https://github.com/mthom/scryer- prolog)

[5] [https://github.com/friguzzi/cplint](https://github.com/friguzzi/cplint)

</comment><comment><USER12> Symbolic AI and ML/DL AI are two entirely different technologies with different capabilities and applications, that both happen to be called "AI" for mostly cultural reasons. The success of one is probably unrelated to the success or failure of another. In most ways, symbolic AI has "faded" simply in that we now take most of its capabilities for granted; e.g., it never strikes you as odd that Google Maps can use your phone CPU to instantly plot a course for a cross-country roadtrip if you so desire, but that sort of thing was a major research project way back when.

In contrast, ML/DL AI is still shiny and new and we have a much less clear grasp of what its ultimate capabilities are, which makes it a ripe target for research.

<comment><USER13> Very much agree. To expand on this, check out Stuart Russell and Peter Norvigs intro book on AI. It supports your comment as there's an entire section (chapter?) on path planning and the like.

</comment></comment><comment><USER14> I expect hybrid deep learning and symbolic AI systems to be highly relevant. My background which is what I base the following opinions on: I spent the 1980s mostly doing symbolic AI except for 2 years of neural networks (wrote first version of SAIC Ansim neural network library, supplied the code for a bomb detector we did for the FAA, on DARPA neural net advisory panel for a year). For the last 6 years, just about 100% all-in working with deep learning.

My strong hunch is that deep learning results will continue to be very impressive and that with improved tooling basic applications of deep learning will become fairly much automated, so the millions of people training to be deep learning practitioners may have short careers; there will always be room for the top researchers but I expect model architecture search, even faster hardware, and AIs to build models (AdaNet, etc.) will replace what is now a lot of manual effort.

For hybrid systems, I have implemented enough code in Racket Scheme to run pre-trained Keras dense models (code in my public github repos) and for a new side project I am using BERT, etc. pre-trained models wrapped with a REST interface, and my application code in Common Lisp has wrappers to make the REST calls so I am treating each deep learning model as a callable function.

<comment><USER15> _millions of people training to be deep learning practitioners may have short careers_

Have database admins disappeared? How about front end devs?

<comment><USER16> I don't know if database admins have disappeared. But we have never needed one to take care of our DynamoDB tables and our "serverless" Aurora databases.

Even though I'm pretty sure AWS needs a lot of them; although not one (or more) for each and every single one of their customers.

<comment><USER17> I sure your queries are horrible :p DBA's are generally not just scoped to keeping the RDBMS alive.

<comment><USER16> Haha ^^ My SQL skills are indeed quite limited. But most of my stuff relies on DynamoDB, mostly in a strictly key-value fashion.

Still; queries are a lot less to learn and much easier to fix when I do them wrong then also queries + taking care of (maintaining, scaling, fixing, backing up,...) the database itself.

</comment></comment></comment></comment></comment><comment><USER18> A lot of what's been going on in the PL community would have been called "symbolic AI" in the 80's. Program synthesis, symbolic execution, test generation, many forms of verification --- all involving some kind of SAT or constraint-solving.

<comment><USER19> What is the PL community?

<comment><USER20> Programming Languages

</comment></comment></comment><comment><USER21> Please check out the Genesis Group at MIT's CSAIL. Or Patrick Winston's Strong Story Hypothesis. Or Bob Berwick. Many at MIT are still working through the 80's winter, without the confirmation bias of Minsky and Papert's Perceptrons with all the computation power and none of the theory (now called neural nets). Or any of the papers here: [https://courses.csail.mit.edu/6.803/schedule.html](https://courses.csail.mit.edu/6.803/schedule.html)

Or the work of Paul Werbos, the inventor of backpropagation, was heavily influenced by -- though itself perhaps outside the cannon of -- strictly symbolic approaches

</comment><comment><USER22> Let's see.

Databases. (Isn't Terry Winograd's SHDRLU conversation that kind of conversation that you have with the SQL monitor?) Compilers. (e.g. programming languages use theories developed to understnad human languages) Business Rules Engines. SAT/SMT Solvers. Theorem proving.

There is sorta this unfair thing that once something becomes possible and practical it isn't called A.I. anymore.

<comment><USER4> Well when we apply materials developed for space flight to kitchen equipment, it stops being called space tech.

Space tech is when you try to go to space. AI is when you aim at AGI.

</comment></comment><comment><USER23> The big win in symbolic AI has been in theorem proving. In spaces which do have a formal structure underneath, that works well. In the real world, not so much.

</comment><comment><USER24> >> There are still companies I know of that do symbolic AI (such as [https://www.cyc.com](https://www.cyc.com)), but I very rarely hear of new research in the field.

I can't answer your main question but, as a practical matter, if you don't hear of new research in the field it probably means you're not tuned in to the right channels, which is to say, the proceedings of the main AI conferences that cover a broad range of subjects: AAAI, IJCAI, IROS, ICRA, plus the more specialised ones, like AAMAS, ICAPS, UAI, KR, JELIA, etc. Any interestting research in symbolic AI is going to be there.

If you get your news from the tech press and the internet, you won't hear of any of that stuff and won't even know it's going on, because, let's face it- the large tech companies are championing a very specific kind of AI (statistical machine learning with deep neural nets) and, well, they have the airwaves, they have the hype engines and their noise is drowning out all other information on the same channels.

For the record, work on symbolic AI is still going on. For instance, the subject area of my PhD research is in Inductive Logic Programming, a branch of symbolic, logic-based machine learning. This is not just active, but going strong with a recent explosion on research in learning Answer Set Programs and the work of my group, on a new ILP technique, Meta-Interpreterive Learning. If we're inventing new stuff, we're still alive and well.

</comment><comment><USER25> I think the biggest problem with symbolic AI systems is that you can only program them with "facts" that have bubbled up into consciousness. Most of human behavior is unconscious. Statistical AI tends to observe what people _do_ instead, which is a better representation of how they will actually behave in the real world. Statistical AI trained on self-reported data (i.e. asking people what they think instead of observing what they do) has many of the same problems as symbolic AI.

This is also the biggest weakness of statistical AI, and why so many people are mad at companies that employ it. If you train on what people do, you also capture all the behavior that people wish they didn't do. Thus you get all the racism, sexual fetishes, discord, unpopular views, irrational views, tribalism, and general stupidity that folks would prefer to pretend doesn't exist, but shows up all the time to an objective observer of humanity.

</comment><comment><USER26> Neural networks have been around for a much longer time than they have been popular/practical/commercially-viable. It just so happens that they can be accelerated using dedicated floating point computing hardware --something GPUs are very good at.

I often think about symbolic AI, and how it relates to Boolean satisfiability. This is an integer problem. We don't seem to have a similar technology to GPUs that would be transferable to the problem domain. If we had that, maybe things would be different. I looked into this a bit, and Microsoft seems to have put some resources into a SAT computing ASIC.

To get the same kind of progress in symbolic AI, perhaps we need massively parallel/scalable SAT solving hardware. The gaming industry gave us the initial floating point hardware; maybe the cryptocurrency industry will gift us with analogous integer hardware that could push symbolic AI further.

</comment><comment><USER27> I keep waiting for symbolic AI to really get traction in the area of software verification. Either a great language/ compiler that can really prove it has no bugs, or a testing approach that takes a binary and proves that it has no bugs of a certain kind. What we've been getting, though, is evidence that the space of possibilities quickly outpaces our capacity to search it, and so logical methods don't compare well to statistical approaches. I would never close the door on it though, since programming is basically thinking symbolically. It's possible that as the industry gets more concerned with privacy and security that the class of applications that _need_ this kind of provability will drive adoption at the current state of the art.

<comment><USER28> Ever heard of the halting problem?

<comment><USER29> The halting problem only says that a program cannot decide every possible program, it does not say much about the programs you are likely to write day to day for the vast majority of programmers.

</comment></comment></comment><comment><USER30> What do you guys think of the conceptual spaces approach of Peter Gärdenfors?

See eg here:

[https://mitpress.mit.edu/contributors/peter- gardenfors](https://mitpress.mit.edu/contributors/peter-gardenfors)

[https://www.youtube.com/watch?v=Y3_zlm9DrYk](https://www.youtube.com/watch?v=Y3_zlm9DrYk)

From reading some papers, it seems his approach is a third way beyond symbolic and connectionist.

Indeed the title of that lecture is "The Geometry of Thinking: Comparing Conceptual Spaces to Symbolic and Connectionist Representations of Information"

Would you say conceptual spaces is a third way and how does it apply to the topic discussed in this thread?

<comment><USER31> I'd say it's symbolic, but not combinatorial. Tldr: it looks a lot closer to symbolic than to connectionist, but it seems a promising new approach within symbolic methods.

What we call symbolic AI usually makes inference by exploring the space of possibilities generated by recombining the basic symbols of a (fixed) domain language.

Gärdenfors approach has a lot of this, in that it has a symbolic representation of data, a well-defined set of symbols that stand for objects in the observed domain (animals, in the example given); additionally, each symbol has a numerical value which represents how much of each property the object possesses.

This is somewhat similar to the knowledge systems of the 70s and 80s for incomplete, approximate rule-bad reasoning. But those were problematic because it was very difficult to do reasoning with their numerical values. When combining facts within the database, the respective combinations of their numerical values often had nonsensical meanings. The algebras used in those systems were not a good fit.

If Gärdenfors is right and concepts can be treated as mathematical spaces with convex regions, his approach could solve some major problems of those systems that made them impractical, and maybe bring them to prominence again.

</comment></comment><comment><USER32> People tend to do a hard distinction between symbolic AI and machine learning, but actually some machine learning algorithms are based on symbols (eg. decision trees and association rules build logical rules). I recommend Pedro Domingos book, The master algorithm, in which he describes the "5 machine learning tribes" (one of them is referred as "the symbolists") and advocates for a unification of different machine learning algorithms. He even proposes a particular instance of algorithm that would fulfill these criteria: Markov logic networks. He has developed an implementation, called Alchemy ([https://alchemy.cs.washington.edu/](https://alchemy.cs.washington.edu/)).

If by symbolic AI we mean GOFAI, expert systems etc, I don't think that there will be ever a resurgence. But if by symbolic AI we mean machine learning algorithms that are somehow based on symbolic reasoning, I do think that there will be a resurgence. In particular, this resurgence will start when: a) Deep learning arrives to its limit (ie. research gets stuck) and/or b) Someone finds a scalable and SOTA-ish way to integrate symbols into gradient based algorithms.

</comment><comment><USER33> Random forests produce the same kind of decision trees that used to be hand- crafted, but admittedly, the ones they generate look distinctly "non-human"

</comment><comment><USER34> Neural representations are messy, this is both a strength and a weakness. It is a strength because it allows you to easily interpolate in the latent space of the representations in ways that might not be reflected by the training data or any rule-set that a human could come up with. This underlies the power of neural networks to generalize.

Symbolic representations are clean, this is both a strength and a weakness. You might have perfectly separated categories but the real world frequently presents inputs that break taxonomies.

We invented symbols like letters and numbers to reduce the complexity of the real world. Language and mathematics are lossy representations but also incredibly useful models.

Given the value that symbols and symbolic methods have for us I have little doubt that they will be an integral part of efficient AI systems in the future. You could train a neural world model on the ballistic properties of a rocket, but if it's orders of magnitude more efficient why not learn to calculate instead?

</comment><comment><USER6> It's really hard to make predictions... especially about the future.[1] But to the extent that I have anything to say about this, I'll offer this:

1\. For all the accomplishments made with Deep Learning and other "more modern" techniques (scare quotes because deep learning is ultimately rooted in ideas that date back to the 1950's), one thing they don't really do (much of) is what we would call "reasoning". I think it's an open question whether or not "reasoning" (for the sake of argument, let's say that I really mean "logical reasoning" here) can be an emergent aspect of the kinds of processes that happen in artificial neural networks. Perhaps if the network is sufficiently wide and deep? After all, it appears that the human brain is "just neurons, synapses, etc." and we manage to figure out logic. But so far our simulated neural networks are orders of magnitude smaller than a real brain.

2\. To my mind, it makes sense to try and "shortcut" the development of aspects of intelligence that _might_ emerge from a sufficiently broad/deep ANN, by "wiring in" modules that know how to do, for example, first order logic or $OTHER_THING. But we should be able to combine those modules with other techniques, like those based on Deep Learning, Reinforcement Learning, etc. to make hybrid systems that use the best of both worlds.

3\. The position stated in (2) above is neither baseless speculation / crankery, nor is it universally accepted. In a recent interview with Lex Fridman, researcher Ian Goodfellow seemed to express some support for the idea of that kind of "hybrid" approach. Conversely, in an interview in Martin Ford's book _Architects of Intelligence_ , Geoffrey Hinton seemed pretty dismissive of the idea. So even some of the leading researchers in the world today are divided on this point.

4\. My take is that neither "old skool" symbolic AI (GOFAI) nor Deep Learning is sufficient to achieve "real AI" (whatever that means), at least in the short-term. I think there will be a place for a resurgence of interest in symbolic AI, in the context of hybrid systems. See what Goodfellow says in the linked interview, about how linking a "knowledge base" with a neural network could possibly yield interesting results.

5\. As to whether or not "all of intelligence" including reasoning/logic could simply emerge from a sufficiently broad/deep ANN... we only just have the computing power available to train/run ANN's that are many orders of magnitude smaller than actual brains. Given that, I think looking for some kind of "shortcut" makes sense. And if we want a "brain" with the number of neurons and synapses of a human brain, that takes forever to train, we already know how to do that. We just need a man, a woman, and 9 months.

[1]: [https://quoteinvestigator.com/2013/10/20/no- predict/](https://quoteinvestigator.com/2013/10/20/no-predict/)

[2]: [https://www.youtube.com/watch?v=Z6rxFNMGdn0&feature=youtu.be...](https://www.youtube.com/watch?v=Z6rxFNMGdn0&feature=youtu.be&t=1457)

[3]: [http://book.mfordfuture.com/](http://book.mfordfuture.com/)

<comment><USER35> _if we want a "brain" with the number of neurons and synapses of a human brain, that takes forever to train, we already know how to do that. We just need a man, a woman, and 9 months._

Geoff Hinton comments on a Reddit AMA that "The brain has about 10^14 synapses and we only live for about 10^9 seconds. So we have a lot more parameters than data. This motivates the idea that we must do a lot of unsupervised learning since the perceptual input (including proprioception) is the only place we can get 10^5 dimensions of constraint per second."

That sounds to me like humans don't take "forever to train" and definitely don't learn from "big data" compared to the size of data we feed into a small machine neural network. Brains must already have a lot of shortcuts built-in.

(comment is from [https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama...](https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/clyjogf/) )

<comment><USER6> _humans don 't take "forever to train"_

I was just being glib about that. "Forever" is just hyperbole, but the 10+ some odd years it takes to go from birth to useful for most intellectual tasks is a pretty long-time in relative terms.

 _Brains must already have a lot of shortcuts built-in._

Oh absolutely. My point is just that there's no reason for us to _not_ pursue "shortcuts" \- as opposed to trying to build an ANN that's big enough to essentially replicate the actual mechanics of a real brain.

To extend this overall point though... it may be that as we learn newer/better algorithms and techniques we find out that you can actually make an ANN that would, for example, learn to do logical reasoning. And it might do so without need to use anywhere near the number of neurons and synapses that a real brain uses. But until such a time as it becomes apparent that this is likely, I think it's a good idea to continue researching "hybrid" systems that hard-wire in elements like various forms of symbolic/logical reasoning and anything else that we at least sorta/kinda understand.

<comment><USER36> We are often deceived by the fact that Human infants are optimised for plasticity (I know this is arguable - but it's a reasonable theory) and for their brain to get through a bipeds birth canal (and subsequently grow). Look at lambs in contrast (I've been on a sheep farm in Scotland for a couple of weeks so I've had the opportunity!) Lambs stand up about 3 to 10 minutes after birth (or there is a problem). They walk virtually immediately after that, they find the sheep's udder and take autonomous action to suckle within an hour (normally) and follow their mothers across a field, stream, up a hill over bridges as soon as they can walk. Within a week they are building social relations with other sheep and lambs and within three weeks they are charging round fields playing games that appear pretty complex in terms of different defined places to run up to and back and so on.

This kind of rapid cognitive development argues strongly (IMO) against the kind of experimental/experiential training that a tabula-rasa nn approach would indicate.

Human plasticity and logical reasoning are the apex of other processes and approaches, I think that because we have so much access (personally through introspection and socially via children) to models of theses processes, and the results are so spectacular and intrinsically impressive.

I used to go to the SAB conferences in the 90's, they're still going, but somewhat diminished I think. This was where the "Sussex School" of AI had it's largest expression - Phil Husbands, Maggie Boden and John Maynard Smith all spoke about the bridges between animal cognition and self organising systems. I am pretty sure that they were all barking up the wrong tree (he he he) but there was and is a lot of mileage in the approach.

</comment></comment></comment></comment><comment><USER37> AlphaGo is a hybrid system, using deep reinforcement learning and Monte Carlo tree search. Tree search dates back to Shannon, before neural networks. AlphaGo is a triumph of symbolic as well as statistical AI.

</comment><comment><USER38> One thing I haven't seen mentioned here yet: symbolic planning is still a pretty large area. It's not really visible if you look at what people are starting AI startups around, but there are a bunch of large companies that use symbolic planning systems, and it's also an active research area, even if not the most in-vogue one at the moment.

I have no inside information on why they're interested, but it's also intriguing that DARPA continues to pour money into planning system R&D.

</comment><comment><USER39> "fell by the wayside at the beginning of the AI winter"

I believe the various aspects of the Semantic Web are a continuation of symbolic AI. My two cents as a complete outsider on the topic.

<comment><USER40> They are, but the successful part of the Semantic Web is almost entirely limited to open-source datasets (grouped under the "Linked Data", or "Linked Open Data" initiative). That's pretty much the only part of the web that actually has an incentive to release their info in machine-readable format - everyone else would rather control the UX end-to-end and keep users dependent on their proprietary websites or apps.

</comment></comment><comment><USER41> We aren't planning on serious research in the space but it is becoming increasingly obvious that an expert system is the right approach for our business going forward fwiw

</comment><comment><USER42> A resurgence in interest might be already underway - check for example this very recent work from Joshua Tenenbaum's lab (MIT):

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding [https://arxiv.org/abs/1810.02338](https://arxiv.org/abs/1810.02338)

It actually brews on ideas that prof.Tenenbaum has been presenting and discussing over the past few years.

</comment><comment><USER43> I wonder about the potential of fusing subsymbolic with symbolic systems, continually learning and updating a set of feature vectors to serve as a dictionary, translating between the subsymbolic and symbolic parts of an integrated learning framework. I think of that as analoguous to how the older, more intuitive parts of the brain and the language-based, reflective, linear, reasoning parts work together.

</comment><comment><USER44> I feel that AI in the form of machine learning has the higher ground because, as an engineer, you have an attack on the problem that gets you moving instead of contemplating whatifs of symbolic sci-fi.

There is also the question if ML is not simply moving us into a ditch of optimum locality. I think it does.

I'd love to see symbolic AI take a foothold only to have it explain back its rationalizations (which ML can't).

<comment><USER31> I have this hypothesis that, once the field of ML stabilizes around a mature industry, we'll start seeing people using symbolic tools to generate explanations of the concepts learnt by the deep learning networks.

</comment></comment><comment><USER45> I have a long background in Ai (robotics, PDP, expert systems, symbolic math, vision, planning).

There appear to be two classes of knowledge. Pattern knowledge, such as riding a bicycle, which we tend to learn in ways similar to the current machine learning trend. In some ways, this is "deductive knowledge". On the other hand, Explicit knowledge, such a learning to reason about proofs, which we tend to learn by teaching is symbolic. In some ways, this is "inductive knowledge.

The current machine learning trend leans heavily on Pattern knowledge. I don't believe it will extend into the Explicit knowledge domain. I fear that once this distinction becomes important it will be seen as a "limit of AI", leading to yet another AI winter. I tried to bring this up in the Open AI Gym ([https://gym.openai.com/](https://gym.openai.com/)) but it went nowhere.

My experience leads me to hold the very unpopular opinion that AI requires a self-modifying system. Computers differ from calculators because they can modify their own behavior. I'm of the opinion that there is an even deeper kind of self-modification that is important for general AI. The physical realization of this in animals is due to the ability to grow new brain connections based on experience. One side-effect is that two identical self- modifying systems placed in different contexts will evolve differently. (A trivial example would be the notion of a "table" which is a wood structure to one system and a spreadsheet to the other system). Since they evolve different symbolic meanings they can't "copy their knowledge" but have to transfer it by "teaching".

Self-modification allows for adaptation based on internal feedback rather than external patterns (e.g. imagination). It allows a kind of hardware implementation of "genetic algorithms" ([https://en.wikipedia.org/wiki/Genetic_algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm)). It allows "Explicit knowledge" to be "compiled" into "Pattern knowledge". This effect can be seen when you learn a skill like music or knitting. After being taught a manual skill you eventually "get it into your fingers", likely by self-modification, growing neural pathways.

Of all of the approches I've seen I think Jeff Hawkins of Numenta ([https://www.amazon.com/Intelligence-Understanding- Creation-I...](https://www.amazon.com/Intelligence-Understanding-Creation- Intelligent-Machines-ebook/dp/B003J4VE5Y)) is on the right track. However, he needs to extend his theories to handle self-modification in order to get past the "pattern knowledge" behavior.

<comment><USER46> >> "Pattern knowledge, such as riding a bicycle, which we tend to learn in ways similar to the current machine learning trend. In some ways, this is "deductive knowledge". "

Deduction is given a rule and cause, find (deduce) the effect, whereas Induction is given cause and effect, induce the rule. Isn't machine learning more inductive (given observations and outcome, induce the decision function)?

</comment></comment><comment><USER47> Aren't Mathematica and automated proving systems succesful cases where symbolic AI happen?

</comment><comment><USER48> There already has been, though it's nascent. Check the proceedings of AAAI 2019 or any of the more recent non-NIPS conferences for details.

</comment><comment><USER49> Depends whether it could be used to improve performance on existing tasks.

</comment><comment><USER50> Like neural nets managing symbolic systems managing neural nets, or something?</comment>


Self-Driving Cars Must Meet 15 Benchmarks in U.S. Guidance - <AUTHOR> http://www.bloomberg.com/news/articles/2016-09-20/self-driving-cars-must-meet-15-benchmarks-in-new-u-s-guidance <comment><USER2> From the regulations: _" Fall back strategies should take into account that—despite laws and regulations to the contrary—human drivers may be inattentive, under the influence of alcohol or other substances, drowsy, or physically impaired in some other manner."_

NHTSA, which, after all, studies crashes, is being very realistic.

Here's the "we're looking at you, Tesla" moment:

 _" Guidance for Lower Levels of Automated Vehicle Systems"_

 _" Furthermore, manufacturers and other entities should place significant emphasis on assessing the risk of driver complacency and misuse of Level 2 systems, and develop effective countermeasures to assist drivers in properly using the system as the manufacturer expects. Complacency has been defined as, "... [when an operator] over- relies on and excessively trusts the automation, and subsequently fails to exercise his or her vigilance and/or supervisory duties" (Parasuraman, 1997). SAE Level 2 systems differ from HAV systems in that the driver is expected to remain continuously involved in the driving task, primarily to monitor appropriate operation of the system and to take over immediate control when necessary, with or without warning from the system. However, like HAV systems, SAE Level 2 systems perform sustained longitudinal and lateral control simultaneously within their intended design domain. Manufacturers and other entities should assume that the technical distinction between the levels of automation (e.g., between Level 2 and Level 3) may not be clear to all users or to the general public. And, systems' expectations of drivers and those drivers' actual understanding of the critical importance of their "supervisory" role may be materially different."_

There's more clarity here on levels of automation. For NHTSA Level 1 (typically auto-brake only) and 2 (auto-brake and lane keeping) vehicles, the driver is responsible, and the vehicle manufacturer is responsible for keeping the driver actively involved. For NHTSA Level 3 (Google's current state), 4 (auto driving under almost all conditions) and 5 (no manual controls at all), the vehicle manufacturer is responsible and the driver is not required to pay constant attention. NHTSA is making a big distinction between 1-2 and 3-5.

This is a major policy decision. Automatic driving will not be reached incrementally. Either the vehicle enforces hands-on-wheel and paying attention, or the automation has to be good enough that the driver doesn't have to pay attention at all. There's a bright line now between manual and automatic. NHTSA gets it.

<comment><USER3> I don't understand this anti-autonomy cheerleading. It's like people on HN live in a parallel universe where there have been a bunch of deaths from cars running Autopilot, whereas in the world I live in, it seems to be somewhat safer than a human alone. Like, people can mess up either way, but they seem to be less likely to do so when the car is also looking out for them. What am I missing?

<comment><USER4> You have to compare the one death using autopilot to one death of people driving Teslas without autopilot. Musk tried to compare it against the universe of drivers (Teslas, kids driving crappy cars, etc), which was a complete false comparison.

So the reason it was a big deal is because it was a huge fatality. Tesla drivers are generally a pretty safe bunch. Statistically, if autopilot hadn't been engaged, that death would not have occurred. Autopilot makes Tesla drivers _less_ safe, not more safe.

Also, the government is doing self driving industry a huge favor. These fatalities could screw over the whole industry if they get out of hand. Musk is giving self driving a bad name.

<comment><USER2> Two deaths using Tesla's autopilot. [1]

[1] [https://www.youtube.com/watch?v=fc0yYJ8-Dyo](https://www.youtube.com/watch?v=fc0yYJ8-Dyo)

<comment><USER5> Why was this not widely reported?

<comment><USER6> Last I heard, nobody has been able to verify if the car was actually in autopilot mode. However, the emergency braking also clearly failed, if the police report that no attempt to stop was made is true.

<comment><USER5> In regard to safety its equally important to report doubtful cases as they may be a sign of something occuring.

</comment></comment></comment></comment></comment></comment></comment><comment><USER7> It wasn't mentioned in the bloomberg article, but the 15 areas covered are:

      • Data Recording and Sharing
      • Privacy
      • System Safety
      • Vehicle Cybersecurity
      • Human Machine Interface
      • Crashworthiness
      • Consumer Education and Training
      • Registration and Certi cation
      • Post-Crash Behavior
      • Federal, State and Local Laws
      • Ethical Considerations
      • Operational Design Domain (operating in rain, etc)
      • Object and Event Detection and Response
      • Fall Back (Minimal Risk Condition)
      • Validation Methods

Not sure if they're specifically ordered, but it seems positive that Data recording and Privacy are up at the top.

<comment><USER8> This seems suspiciously like government getting something right... with regards to a quickly-evolving new technology market...

has this ever happened before?

<comment><USER9> Can you think of a concrete example where the government got it wrong?[1] For the sake of argument, the federal government in the last 50 years? Maybe the encryption export ban. Or the CDA, but that was quickly reversed and the part that's left (Section 230) was really instrumental in the rise of the modern web.

[1] And I don't mean _wrong_ as in "NSA spying" because you disagree with the policy. I mean like, "regulations mandated everyone use Beta tapes and laser disk even though they quickly became obsolete."

<comment><USER10> Some HIPAA regulations that pre-date the rise of shared virtual servers in "the cloud" are quite outdated and cause quite a bit of trouble for no real benefit.

<comment><USER11> > Some HIPAA regulations that pre-date the rise of shared virtual servers in > "the cloud" are quite outdated and cause quite a bit of trouble for no real > benefit.

What HIPAA regulations are you talking about? Other than HITECH guidance (which can sort-of be seen as a "HIPAA regulation"), HIPAA regulations don't generally specify technologies at all, and I can't think of any that I would describe as outdated or troublesome due to the rise of shared virtual servers and "the cloud", whether they predate it or not.

<comment><USER10> The biggest thing is that we can't run software with unencrypted PHI on physical hardware that is simultaneously running other people's code. In practical terms this means that we have to pay AWS some $ to get dedicated instances and also we can't use ELBs in the standard (easy) way. There are some other things as well.

<comment><USER12> That seems like a fairly reasonable thing given you're talking about encrypted PHI... it's some extra $ for a considerable reduction to overall attack surface when processing the most sensitive type of personal data.

I don't think this meets OP's definition of "wrong".

<comment><USER10> In practical terms I don't agree that the threat of someone doing all of the following things is worth worrying about (in comparison to many other more likely failures):

1) determine what physical hardware in aws the target is running code on

2) somehow get the aws virtual machine manager to let the attacker run their malicious code on the same hardware

3) somehow pierce the protections of the virtual machine to read memory being used by the target application

4) figure out how the data is stored in memory in order to make sense of anything that was read

<comment><USER11> > In practical terms I don't agree that the threat of someone doing all of the > following things is worth worrying about

In AWS case, this is an AWS rule about when they will sign a HIPAA BAA, even though there is no HIPAA regulation that specifically prohibits the arrangement at issue. AWS clearly thinks it _is_ worth worrying about.

When you run your own public cloud, you can determine what risks are worth accepting potential liability for.

<comment><USER10> Yes, I agree that Amazon is behaving perfectly rationally given the legal environment. My point is that the legal environment has been designed in an un-optimal way from a technical perspective. Identifying such a situation was rayiner's request.

<comment><USER11> > Yes, I agree that Amazon is behaving perfectly rationally given the legal > environment.

I'm not commenting on Amazon's rationality (I haven't actually evaluated the security concerns that would determine that.)

> My point is that the legal environment has been designed in an un-optimal > way from a technical perspective.

And you haven't pointed to anything in the legal environment that is suboptimal from a technical perspective. You haven't even pointed to anything in the legal environment _at all_.

Amazon (as a BAA) has certain administrative responsibilities for putting administrative and technical safeguards in place to prevent breaches, and certain obligations and liabilities in the case of breaches. HIPAA and related laws and regulations _do not_ specify the specific administrative or technical safeguards, though they do specify areas that must be addressed.

Amazon has decided that the particular technical arrangement you prefer is too high of a risk, but you haven't pointed out anything that indicates that this is the result of an outdated regulation that results in poor technical choices rather than technology-neutral regulation and a reasonable evaluation of the security concerns of the particular technical arrangement you would prefer.

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER13> I'm astounded that it seems like these regulations are going to be sensible and promote the technology. It's a good thing that these are going into place, since autonomous vehicles should definitely not be legislated on a state-by- state basis.

<comment><USER14> > I'm astounded that it seems like these regulations are going to be > sensible...

Was that hyperbole? I would say the majority of regulations (at least in OECD countries) are sensible, and many that are not are intended to be, are outdated, or are politicized.

<comment><USER15> Many people automatically assume a government can't make up sensible regulations. There are a lot of them in the US. It's a meme you hear all the time, especially in a POTUS election year.

<comment><USER16> I think it might be deeper than that. I don't feel that the US government, on it's own, is incapable of drafting up reasonable legislation. The problem is that the US government is 100% for sale to the highest bidder, and corruption runs deep (we just call it "campaign contributions" as if that makes it better). If sensible regulation is proposed, it'll last 30 seconds before the good senator from [some self-driving car company's home state] has turned it into a document crafted to drive business to his "contributor".

This isn't a political statement as it cuts across both parties, which renders it all the more insidious.

<comment><USER17> Surely this is based on 0 personal direct experience with the people that write these kinds of regulations.

I have worked with engineers that write technical regulations. They are generally focused on doing a good job at the task at hand. To think some mid level person that is hired into a normal job and never meets a politician in their career cares about campaign contributions is asinine.

What do you think the people at NASA and NAVSEA and NIST do all day?

</comment></comment></comment></comment></comment><comment><USER12> Pages 14 - 30+ of the embedded report (pages 16 - of the PDF) are particularly interesting and promising, especially the portions about transparency around privacy and ethics issues.

The report recommends that "Manufacturers and other entities should develop tests _and verification methods_...". Does anyone know whether verification here means software verification, or does it mean something else in this context?

Edit: Just noticed that I got to the PDF via elicash's comment and not via the linked article. Here's a link to the PDF: [https://www.transportation.gov/sites/dot.gov/files/docs/AV%2...](https://www.transportation.gov/sites/dot.gov/files/docs/AV%20policy%20guidance%20PDF.pdf)

<comment><AUTHOR> The report makes reference to "Assessment of Safety Standards for Automotive Electronic Control Systems" by NHTSA, which itself reviews ISO 26262, MIL- STD-882E, DO-178C, the FMVSS, AUTOSAR, and MISRA C.

In this context, they mean verification and validation in the systems engineering sense. Software would be included in that it is a part of the whole system.

<comment><USER18> I have a hard time understanding the current AV SW stack.

On one hand, at the low level, sensor, motor control, etc you likely have traditional hard real time/MISRA C code, but on the higher layers you probably things like DNN, image recognition, which are much less deterministic.

So I am not sure how do you reconcile these two worlds, and prove it is safe and always work in timely manner.

It seems the only sound approach would be to validate the whole system on a real road.

<comment><USER19> Now you understand the job of systems engineering :)

Verify components, validate the entire system is the typical approach.

<comment><USER18> The point I was trying to make is that if you have actuators running MISRA C that are going to be driven by something written in Tensorflow, does it still makes sense to have a requirement to use MISRA C in the first place for the low level part ?

<comment><AUTHOR> I'd be very wary of using complex SOUP like TensorFlow, even if brought under my quality system. I think a good answer here is that once one goes under design control the subset of functionality needed should be implemented in- house under the organization's SDLC.

<comment><USER20> Of course these things are meant to be used (1) to train the system, (2) as a player in the prototype. Exactly like in the old school ML-based systems: you train in Matlab or CudaConvNet, and then you load the trained classifier into the custom-made player highly tuned to your hardware and problem domain.

</comment></comment></comment></comment></comment></comment></comment><comment><USER21> Info here: [https://www.transportation.gov/AV](https://www.transportation.gov/AV) (including noon Eastern livestream)

</comment><comment><USER22> This is excellent news! Guidelines to follow implies that if the manufacturers can meet these guidelines then they could plausibly have a somewhat legal basis for putting self driving cars on the roads.

</comment><comment><AUTHOR> N.B., this policy is mainly concerned with Highly Automated Vehicles (HAVs), which are defined as SAE Level 3 ("capable of monitoring the driving environment") and above.

edit: as to SAE Level 2, it has this (and more) to say:

> Furthermore, manufacturers and other entities should place significant > emphasis on assessing the risk of driver complacency and misuse of Level 2 > systems, and develop effective countermeasures to assist drivers in properly > using the system as the manufacturer expects. Complacency has been defined > as, "... [when an operator] over-relies on and excessively trusts the > automation, and subsequently fails to exercise his or her vigilance and/or > supervisory duties" (Parasuraman, 1997).

also,

> Manufacturers and other entities should assume that the technical > distinction between the levels of automation (e.g., between Level 2 and > Level 3) may not be clear to all users or to the general public.

</comment><comment><USER23> I'm surprised that self-driving technology is focusing on replacing the driver as an autonomous actor, processing visual and radar/lidar signals in order to know about its surroundings. I've always thought we'd get further faster by having automobiles also talk to other vehicles nearby, and design roads to support the computer driven vehicles.

Two examples are:

1) If the vehicle is talking to the cars in front of it, it can know they are braking before it senses that visually. Also, the vehicles can speed up in a gridlock scenario more in unison, like a train.

2) On the interstate, markers in the pavement can be specifically designed for computer sensors rather than human eyeballs. Also, cars can draft together to save fuel.

<comment><USER24> While networked cars are interesting, there is also a massive security issue here.

Hackers will easily figure out a way to spoof the communication, and could play with traffic.

There are mitigations for most issues, but it's a complex topic.

Just imagine some scenarios:

-) Spoof an emergency break advisory that causes tailing cars to also do an emergency break. (could be mitigated by first observing that cars in front are actually slowing down before breaking)

-) Spoof a command from a smart traffic light at an intersection to stop immediately for police / other emergency traffic. (need to check if traffic light is actually red)

-) Spoof speed restrictions issued by a smart highway traffic jam prevention system.

-) A system for police to force a car to stop immediately and pull over, eliminating car chases. Just spoof this signal and stop anyone you want. (mitigate by checking if there is a police car trailing you, and ignore otherwise).

And so on...

A way around would be to maintain a national database with public keys for each registered vehicle, and make cars only accept those keys. But that would be hard to maintain and still hackers could just get a hold of some PK.

In the end, the driving system will always have to correlate such car 2 car communication with observations it makes itself.

And an autonomous system can react almost immediately anyway. So coordination doesen't give you all that much.

\-- There are some useful ideas though, like:

-) Traffic lights can announce an ideal speed for a route, taking into account traffic and traffic light timings, so you can optimize throughput and minimize fuel consumption

<comment><USER25> All good points. Seems like you could get around it by using these other-car communications as noisy signals and weighting evidence against the world as the car sees it, e.g., if you get a spoofed emergency brake advisory, and the car's own percepts suggest there's no reason to brake, the resultant action may be to not brake. The signal from the other car[s] becomes just another feature.

<comment><USER26> Considering the millions of miles driven each day, even if the networked signal wasn't heavily weighted, a spoofed emergency brake advisor signal could still do significant damage.

</comment></comment></comment></comment><comment><USER27> Live streaming just started here: [https://www.transportation.gov/AV](https://www.transportation.gov/AV)

<comment><USER27> Just ended. And here is Obama's call for safe automated vehicles: [https://www.post-gazette.com/opinion/Op- Ed/2016/09/19/Barack...](https://www.post-gazette.com/opinion/Op- Ed/2016/09/19/Barack-Obama-Self-driving-yes-but-also- safe/stories/201609200027)

</comment></comment><comment><USER28> I'm really excited by US govt outlining what it would take to make a legal self driving car.

I'm also hoping that one of the options is to upgrade an old car to a self driving car with an open source kit that you can buy and install it via a certified mechanic.

I think that would be an interesting future I'd like to be part of.

</comment><comment><USER29> Brad Templeton, who's been working on self-driving cars for a few years now, analyzed this in [http://ideas.4brad.com/critique-nhtsas-newly-released- recomm...](http://ideas.4brad.com/critique-nhtsas-newly-released- recommendations-states-and-regulations). He says, "Broadly, this is very much the wrong direction... the progress of robocar development in the USA may be seriously negatively affected."

This is a big deal.

<comment><USER30> > I have written that having 50 sets of rules may not be that bad an idea > because jurisdictional competition can allow legal innovation and having > software load new parameters as you drive over a border is not that hard.

I'm not sure I would put much weight behind what he has to say.

</comment></comment><comment><USER27> Finally posted my initial comments on the verification implications of all this here: [https://blog.foretellix.com/2016/09/21/verification- implicat...](https://blog.foretellix.com/2016/09/21/verification-implications- of-the-new-us-av-policy/)

</comment><comment><USER31> It's nice that these regulations sound sensitive and not heavy handed. I'm wondering whether they are needed at all though. They've been formulated in heavy collaboration with the market leaders Uber, Google etc. Is there a risk they will help shut out upstarts, similarly to how the FDA makes drug development astronomically expensive?

</comment><comment><USER32> I'm cynically imagining that since it's a collection of big automakers, it is pretty easy for them to affect policy.

It looks like consumers and automakers are both wanting driverless cars so putting any enevitable regulations quickly benefit both parties.

</comment><comment><USER33> I am commenting here as a token of appreciation for Etendue and also so that I can bookmark this awesome share by him. Thanks.

</comment><comment><USER34> So what are the 15 bench marks?

</comment><comment><USER35> What is it about self-driving cars that has HN readers so incredibly excited? Sorry if it's a little off-topic.

The reason I ask is there are plenty of other countries in the world where cars just aren't that important, let's take Netherlands for example. If you have automatic cars, society here is not just going to be that excited AFAIK. Public transport here is great and most people cycle everywhere, because it's fun, easy and good exercise. Not to mention a lot of people are employed as drivers.

Same for many Asian countries where population density is high, people just don't have the money/room for cars. Scooters are the way to go because of traffic congestion.

Besides, don't people enjoy driving? I don't own a car but when I get behind the wheel, it's a lot of fun. Will people really be able to handle the car doing the speed limit?

I understand technologically it's pretty interesting, but we've had commercial airliners that fly themselves (mostly) for a long time, same for ships and drones and we don't marvel over those things all the time, though I agree they are great innovations.

So apart from the tech what is the actual excitement about?

\- Concern for those who will lose their jobs.

\- Concern for others safety.

\- Privacy concerns.

\- Excitement about the safety benefits.

\- Economic opportunity.

\- Fundraising hype.

\- All of the above?

As a Silicon Valley outsider sometimes I read HN and it feels like some context is missing. Sure it's going to change industries, but is this really good progress, necessary progress, or just the next _thing we 're told we need_? I mean can a self-driving car really replace a delivery person yet, a person who can do things like leave packages with a neighbor and build relationships, trust etc?

Sorry if this is a little off-topic, but I'm genuinely curious because it's hard to understand, to me as an outsider, it really looks like some kind of ride-sharing turf war hype battle more than anything else?

I dare to say it, but it's the same for machine learning, a lot of it is fascinating, interesting, exiting tech, but how many product recommendations does one need? How good do my _friend recommendations_ have to be? How smart does Siri need to be? Will a patient really feel better without being treated by a human? Are we really going to trust these things handling nuclear warheads?

Maybe I live a strange life and have unusual views, but I just don't really see the need most of these things when so many problems could be solved using other means. Using this stuff to help people is great, but how much of this effort is actually being put towards that end?

If I'm a little naive, apologies. I'm not having a go but these are just honest questions I often find myself asking when reading HN lately. Agreed this might not be the place to ask but I'm prepared to wear the down votes :)

<comment><USER36> In my opinion, you mentioned the core reason why you find the excitement baffling, i.e. that you don't drive often but you find it exciting when you do.

Now imagine the scenario for most of the US, a public-transport-hostile country for the most part, where millions upon millions of people burn their precious lives waiting in traffic and sucking in traffic fumes. In my mind, this is one of the most appalling wastes of human potential that has ever existed. Sure, some try to make lemonade out of lemons by educating/informing themselves as they see fit but by and large, it is a huge waste. Not to mention the many thousands of people who die every year in car accidents during the daily commute.

So from my point of view, the self driving car is a thrilling concept: the ability to disengage from a useless, pointless, and hopeless daily grind and engage in something that I want to do, whether it be work, reading, watching a movie, etc. is cool. The closest I have come to this dream in my transit- unfriendly Texas city is the one job where I had an opportunity to take the train/bus into downtown: while this made my daily commute very long, I loved it because it freed me up from the drudge of driving.

Some might ask that perhaps I just hate driving. That is not true. I love taking road trips or autocrossing when I can. But to equate the daily commute with enjoyment is a bridge too far, in my opinion. Banish it, I say, banish it.

<comment><USER35> Thanks for the response and totally agree with the sitting in traffic, wasted time thing ,but with all due respect it still seems inefficient, wouldn't public transport and telecommuting be better options in a connected world?

How will there still be no traffic jams, or the car will be like an office? In that case why not just work from home and come to work for meetings here and there? Might flexible / less work hours help?

I mean people will still be driving around in vehicle which often makes people motion sick if not paying attention to surroundings , cars requires a lot of energy, take up space etc.

I used to travel to work via train, it was 1.5 hours one way, it was highly productive time for me, but for some reason trains don't seem to make people as motion sick?

I guess one other thing to note is that in Australia, where I'm from originally, some see people think of others using public transport or biking as kind of _peasants_ or feel it's inferior, that might be part of it too ? They're also the kind of people who often like to drive fast and own expensive cars as a status thing, so I'm still note sure it's going to take?

<comment><USER37> It's incredibly frustrating that people are getting excited for autonomous cars' ability to free them from the drudgery of driving when this has been a solved problem for a long time with public transit.

Unfortunately anti-public transit special interest groups have discredited public transit initiatives all over, and fighting this has been incredibly difficult.

On your last point people definitely do see owning expensive cars as a status item and for this reason I think it's valid to question to what degree and speed will autonomous car sharing networks replace individually owned cars.

<comment><USER38> I fully expect self driving cars to arrive before BART goes all the way down the peninsula. It's total crap, but I'd be surprised if it wasn't true. Same for more frequent/faster/more reliable caltrain with room to sit or bring your bike.

On one side, you have a solution that requires a whole bunch of groups to align. On the other, you have an individual decision (buying a car). That's why I am excited. If it looked like it was on the horizon, I'd be just as excited for great public transit.

</comment></comment></comment></comment></comment><comment><USER39> Woohoo, lets get the lawyers involved!

</comment><comment><USER40> The only regulation that really matters is making car manufacturers liable for accidents and they would have to pay a fine from $100,000 for the smallest accident (per car) up to $10 million per accident.

When the manufacturers "can't explain" how the accident happened (after an audit was performed), they should be fined the maximum $10 million amount.

Why? Because for one assuming it's just a glitch and "they don't know" about it, then they should pay for incompetence. And two, if the car was hacked by a nation state, then their security sucks, and they should again pay the maximum amount so they have the maximum incentive to ensure digital security of self- driving cars.

Where third-party self-driving systems are involved (MobilEye, etc), the liability/fine should be split 50-50 between the car maker and the system vendor.

Give car makers these "incentives" and the other regulations are more or less pointless (other than establishing common V2V and V2I standards and whatnot). Then you'll see just how hard they scramble to make their systems safe.

EDIT: And here we go. Remote hack of Tesla Model S.

[https://blog.kaspersky.com/tesla-remote- hack/13027/](https://blog.kaspersky.com/tesla-remote-hack/13027/)

We're only at the very beginning of self-driving cars. What happens when there are 100 million self-driving cars on the road? Will their security be as terrible as it is on our PCs?

People should get scared a lot faster about this stuff, before all car makers start writing their software and then refuse to write it from scratch again and just tack on to the poorly written systems new security features in the future as a response to such hacking.

<comment><USER41> > Then you'll see just how hard they scramble to make their systems safe.

Chances are they'd opt-out of the opportunity altogether and wait for someone else to take the heat.

<comment><USER42> Those who opt out are those we don't want setting the bar in this domain.

If it becomes acceptable for security and safety to be secondary to "getting the cars on the road ASAP and capturing as much of the market as possible" we -- as in the consumers -- will pay the price.

I understand that some of the innovations and progress will only come when we get the cars on the road at scale, but we should still build a giant -- exactly how the comment or suggested -- to loom over the shoulders of these car companies.</comment></comment></comment>


Email should be personal - Uniboxapp Public Beta - <AUTHOR> https://www.uniboxapp.com

<comment><USER2> I wish the landing page gave a description and/or list of features of unibox and what it is trying to solve in particular.

Aside from that, the UI looks very clean. I signed up nonetheless.

<comment><AUTHOR> It is a new take on email. I just bumped into them and realised what they are doing is way different than what others are doing.

In my opinion, I would prefer looking at at a more user centric email client like they have.

Not too sure how things would work when there is a group of people talking.

Now that I wrote this, it looks so much like the Messages.app</comment></comment>


Ask HN: Why did your startup fail? - <AUTHOR>

Failure is a subjective term, but why did your startup not meet expectations?  Many of us have had ventures that did not go quite like we expected, and I'm curious why.
Being in Dallas, I often hear things from failed entrepreneurs like not being in SV, and the access to capital, doomed their venture.  Not that I completely agree with this, but I find this geographic excuse interesting.
I personally have had ventures fail due to one was a bad idea, and the other was with the wrong business partner.  I'd be interested in your story or at the very least your reason. <comment><USER2> Startups fail for a variety of reasons, PG listed some mistakes that Startups make in 2006[1] which is still actually a good read/relevant today (I disagree with the Single Founder point though).

However, there are problems which you generally see when a startup fails:

1\. Poor Management Team \- Can't effectively execute, have a poor strategy and a poor management team 'breeds' a poor team (A players hire A players etc). A good management team will avoid the other issues below:

2\. Market Problem - No Market or a Market not large enough. (May have been too soon etc)

3\. Product Problems - Creating a poor product, so noone uses it.

4\. Business Model Problem - Acquiring a Customer costs more than Long-term Value

5\. Poor Execution

6\. Building A Company around Technology not the Solution - This goes back to point 2 but your users don't care about your stack. Seriously you users do not care about your stack they care if you have a solution to their problem. If you do then you have a potential company but if you focus on building it around technology instead of giving them the solution, you're going to run into a lot of problems.

7\. Lack Of Focus \- Not saying 'NO' enough

8\. Running Out Of Money \- Most people raise to get to the next round of funding. That is _wrong_! If you are raising money etc then you should raise enough money to achieve a set number of milestones which attracts another round of investment from existing/new investors.

[1] <http://www.paulgraham.com/startupmistakes.html>

<comment><AUTHOR> I'm familiar with the generic reasons why most startups fail. But the question was targeted to why did YOU fail.

</comment></comment><comment><USER3> Not Yet Shut Down. But thinking so.

I started a website called "mistrics". This was designed to help students to document their college projects and work together as a team from multiple locations. Also getting right guidance from teachers. BUT looks like no body is interested to help students. Basically in India. College admin and teachers have decided to not accept technology especially if it is free.

What i learned is there is time gap between technology in India and rest of the world. We in India are not going to change our legacy rules (In education, not at all).

</comment><comment><USER4> I will be shutting down my health website. This decision was made last night.

It never became anything profitable. It mires me in controversy. It makes me a target of personal attacks. I am pretty thick skinned and I understand the reasons why people are skeptical of my story, but it simply isn't possible to develop the site in a feedback vacuum. The personal attacks I get in no way help me figure out what I need to talk about or how I need to talk about it. But they make it abundantly clear that it is potentially dangerous to tell my story. I did not spend twelve years getting myself well just to inspire some stressed out mom to shoot me because her child isn't getting well.

I will be focusing on lighter topics with more hope of being monetized. Hopefully I can figure out how to shut up about my medical situation. Shutting up isn't one of my strengths but I remain deeply in debt and struggle to get enough to eat every month. I need to figure out how to make money. Trying to make the world a better place has never gotten me anything but pissed on and very few people care at all that getting myself well has cost me so much. I cannot get anyone to understand that this is far cheaper and more effective than conventional treatments, the problem is a) it all came out of my pocket rather than being covered by insurance and b) the single biggest thing I still need is an online income so I do not have to go back to a regular job. A regular job winds up keeping me so sick that it is a net negative income. I have been better off jobless and homeless but my creditors are becoming a bigger and bigger problem.

Anyway, I am sure you don't actually care and didn't really want to know. Fwiw: cystic fibrosis currently costs the U.S. about $3 billion (with a B) annually for medical care for a mere 30,000 people. Much of that no doubt comes at taxpayer expense. My debts after getting both me and my son with cf well are a few $10k. One time expense. My condition is now managed with diet and lifestyle. Neither of us takes any medication. But I am a former homemaker. I cannot determine a means to monetize educating people about diet and lifestyle changes which cost a fraction of their current medical bills. And I currently need to focus on making money and leave the creation of medical miracles to someone else, someone with a spiffier title than "former homemaker" I imagine.

Thank you for the excuse to write something which will hopefully be cathartic and help me to move on and focus on making money.

<comment><AUTHOR> I'm sorry to hear about your struggles. One of the benefits of tough times is that it really makes you appreciate the good times. Best of luck...

<comment><USER4> I am not really in a mood for platitudes. It seems pretty callous, all things considered, for you to say such a thing to me. I imagine I will regret posting the announcement. I normally have no regrets. But I seem to have a lot of them here lately.

Edit: Have an upvote anyway. I know you mean well. But it really is the wrong kind of thing to say.

<comment><AUTHOR> Call it what you want. It was a genuine gesture for your well-being. If I offended you - that was not the intent.</comment></comment></comment></comment>


USA: Legalization of marijuana caused a reduction of rapes and thefts [pdf] - <AUTHOR> http://ftp.iza.org/dp10522.pdf <comment><USER2> This is a really interesting topic. Rape, unlike other types of crime, suffers from reporting deficiencies, specifically many victims choose not to report it out of fear of social backlash. I wonder if this is playing any part in observed changes.

</comment><comment><AUTHOR> \+ reduced consumption of other drugs and both ordinary and binge alcohol</comment>


Brands to startups: In case you want our money, here's how to get it - <AUTHOR> http://pandodaily.com/2012/10/18/brands-to-startups-in-case-you-want-our-money-heres-how-to-get-it/

<comment><AUTHOR> This is an interesting article. In an ad driven startup ecosystem, we forget that landing a brand relationship is hard and takes a long time. Even mastodons like Facebook and Twitter need a huge amount of time to land those deals. I am questioning what chances do startups have in this environment? Are we destined to use adwords or ad serving products?</comment>