Artificial Intelligence Is Stuck - <AUTHOR> https://www.nytimes.com/2017/07/29/opinion/sunday/artificial-intelligence-is-stuck-heres-how-to-move-it-forward.html <comment><USER3> I'd dispute that AI research is stuck or that his proposed answer along the lines of "An international A.I. mission focused on teaching machines to read" is a good one.

Research seems to be cracking along with AlphaGo, self driving cars and the like. Recently DeepMind have been doing interesting stuff with dreams[1], imagination[2] and body movement[3], the last one being a little reminiscent of his daughter inventing a way to exit her chair mentioned in the article.

Re government intervention it's not something like CERN where you need billions in capital and it's not an area where a big government project is likely to be the best use of capital.

[1] [https://www.bloomberg.com/news/articles/2016-11-17/google- de...](https://www.bloomberg.com/news/articles/2016-11-17/google-deepmind- gives-computer-dreams-to-improve-learning) [2] [http://www.wired.co.uk/article/googles-deepmind-creates- an-a...](http://www.wired.co.uk/article/googles-deepmind-creates-an-ai-with- imagination) [3] [https://www.theverge.com/tldr/2017/7/10/15946542/deepmind- pa...](https://www.theverge.com/tldr/2017/7/10/15946542/deepmind-parkour- agent-reinforcement-learning)

<comment><USER4> There's a LOT of non-Deepmind research as well, some things that have actually been published _before_ Deepmind.

Don't get me wrong, Deepmind puts out a _lot_ of great work, but do look at other research labs as well, especially the ones that aren't as well known. Deepmind markets their research really well, but there's a ton of other labs doing good work as well.

<comment><USER5> Any tips on how to find the more obscure stuff? [http://kurzweilai.net](http://kurzweilai.net) occasionally bubbles up really crazy stuff, but perhaps there is a journal or other curated resource you know of.

<comment><USER4> I'd recommend following Miles Brundage on Twitter, dude goes through a mind boggling number of papers and uncovers loads of hidden gems.

</comment></comment></comment></comment><comment><USER6> I've been down this road. I used to be interested in "embodiment" as a path to AI, but in the sense of things that could move around in the physical world, not fall down, and not bump into stuff. Low-end mammal level AI was the goal - mouse level. I made some progress in that area [1], hit the limits of available simulators, spent several years on improving physics engine technology, and eventually sold that off to a game middleware company.

Boston Dynamics took that much further. But it's the same approach I used - analysis of the dynamics, not AI. It's a complicated problem in dynamics, but it barely needs AI at all. Boston Dynamics, unfortunately, demonstrated that even if you spend $120 million, you're not at a minimum viable product that sells yet. Really cool legged robot prototypes, though.

This was all before machine learning took off. For a while I was looking at adaptive model-based control, which is a lot like machine learning. Machine learning seems to be getting good at what the front end sections of the visual and auditory cortexes do. This is real progress. But a whole organism is still out of reach.

There's a business case for focusing on language skills, but in a way, it's a distraction. The mammals all have close DNA compatibility, but only humans do language much. If we can get into the entry-level mammal range of AI, we should be getting close. I once said something like that to Rod Brooks when he was promoting Cog (a robot humanoid head with a lot of compute power), and he said "I don't want to go down in history as the man who developed the world's greatest robot mouse."

Reverse engineering biology is going very slowly. See "openworm.org", which is an effort to develop a good computerized model of the C. elegans nematode that runs in simulation. C. elegans has 302 neurons, the wiring diagram is known, and it still doesn't work. This shows how little we really know about nervous systems.

[1] [https://www.youtube.com/watch?v=kc5n0iTw- NU](https://www.youtube.com/watch?v=kc5n0iTw-NU)

<comment><USER7> What's the killer app for mouse level AI?

<comment><USER8> With the right priors - self-driving cars, for example.

</comment></comment></comment><comment><USER9> I sense the hand of an editor. Particularly regarding the title.

Embodiment seems to be a branch with low-hanging fruit, when it comes to advancing AGI. I think the economic structural problems are important, but it's possible to over-egg the details and for some lab to stumble on an experimental paradigm with features we didn't realise were implicated a priori. When it comes to other AIs, the idea that we are stuck for pragmatic/practical issues is a little silly.

I'm no expert, just a person with an arm-chair (and too much time on my hands), but I suspect that idealising the feature-space we work with can hide as many things as it reveals - it may turn out that the computational problems are so large because we are mostly attempting to solve them ex nihilo. That is, embedding in an environment plays as much a role in the process of intelligence as a neuronal structure does; genes and evolution provide a mode for translating environmental computation into neuronal computation. The vast scope of what we don't know about the role of glial cells for cognition (and the little that we do) makes me doubt that complex structures of binary mechanisms will be sufficient. But again, that's just my speculation, and perhaps lack of education.

<comment><USER10> > Embodiment seems to be a branch with low hanging fruit, when it comes to > advancing AGI.

If it were "low hanging" it would have been picked already. Reinforcement learning with AI agents is hard, especially in a dynamic environment with many types of objects.

I think the path towards AGI is to do simulation coupled with deep learning. Simulation would open the door to predicting non-trivial effects that cannot be learned by example because they are so rare that there are no training examples. We can generate artificial training examples to cover all the rare cases.

<comment><USER9> I'm sorry if 'low-hanging' comes off as disrespectful - I'm just guessing that aspects of embodiment, once understood, will be capable of fairly trivial description and reap large consecutive rewards. I remember your u/n from other posts, we seem to have similar interests but you are vastly more educated in the engineering of AI. What's your background, if you don't mind sharing here?

I am suspicious non-contingent aspects to cognition remain that simulation and deep learning don't necessarily grant, though they might well be sufficient. I'm not smart enough to be sure, and I'm stretching for a description: a child self-reared to adulthood in the wild won't display what we usually consider essential facets for 'humanlike' levels of intelligence or competence. We're hardly trying to build a caveman.

They lack whatever is crucial in socialisation -- the ability to make subtle differentiations between other agents' actions and motivations seems to endow self-awareness, and abstractions for successfully handling novel objects and ordering perception relevance. Successful generality to our degree seems to be better 'outsourced' rather than hard-coded into solo agents, at least in the natural examples. Though I understand that's not necessary, perhaps there are good reasons for it. I feel like the first AGI will actually look a lot more like "multiple similarly 'perspected' AIs interacting with one another leads to each carrying the G in AGI". Essentially I'm suggesting it's hard to have generality and relevance to our proficiency (or better) without a 'culture'.

What I'm thinking seems to boil down to inserting some of Piaget's ideas into the philosophy of AI, which might be a bit much, and I'm open to charges of bullshit.

<comment><USER10> I am just a hobbyist in machine learning. The current situation in AI is that we can only do limited aspects of perception: like vision and hearing, at a superficial level. We can recognize objects but we can't recognize relations between objects nearly as well. There is no global scene understanding yet. With text, we can do syntax and translation, but we can't do reasoning except on trivial cases. There is no power of abstraction yet, of transferring knowledge between domains, which is essential for advanced intelligence.

So, before we have an embodied agent, we need to solve the reasoning and abstraction part, and my money is on graph signal processing (a kind of neural nets) and simulators (also implemented as neural nets). We need to move from simple object recognition to reasoning and simulation on graphs of objects and relations.

</comment></comment></comment></comment><comment><USER11> > Not long ago, for example, while sitting with me in a cafe, my 3-year-old > daughter spontaneously realized that she could climb out of her chair in a > new way: backward, by sliding through the gap between the back and the seat > of the chair. My daughter had never seen anyone else disembark in quite this > way; she invented it on her own — and without the benefit of trial and > error, or the need for terabytes of labeled data.

I really hate the constant comparisons of AIs to babies. The author's 3 year old daughter has had 3 YEARS of sensory data obtained through moving and trying to fit through things. That is terabytes worth of data! I would expect an AI to be able to generalize once as well.

<comment><USER12> Yes terabytes.... Probably petabytes or more. And all of it is unlabeled data.

If you fed video and sensory data to a deep net for 3 years and somehow were able to come up with an activation function that modeled "survival", I still highly doubt that anything at all would come out that remotely resembles human intelligence. There's no way that i'm aware of to label reality in real time.

<comment><USER13> One way to label is by expectation. If the outcome is what you expected, then it gets one label. If it didn't it gets another label. Deepmind has had some success with this approach.

</comment></comment></comment><comment><USER14> An agent can be intelligent without it learning how to read human language. Look around, most organisms in our world communicate using extremely simple binary language or don't communicate verbally at all. Yet, they are intelligent enough to do very complicated tasks which current robots fail to do. Intelligence is an easier problem than language, and thus should be solved before language.

What a sigh of relief to read a refreshing take on the real progress of AI. Yes, it's stuck, and that's the real problem of AI, that we haven't been able to do anything significant after perception. However, unlike the author, I don't think the solution is to nationalize AI research (we're not close enough for that), but to fund more non-deeplearning research for 5-10 years, and then we might see some progress in non-perception tasks.

<comment><USER15> > Look around, most organisms in our world communicate using extremely simple > binary language or don't communicate verbally at all.

Yes.

> Yet, they are intelligent enough to do very complicated tasks which current > robots fail to do.

True.

> Intelligence is an easier problem than language, and thus should be solved > before language.

Wrong.

This is the classic mistake everybody makes, including people in Computer Science.

Because if that were so our robots would _already_ be clambering backwards through chairs (per the metaphor in the article).

You have to think of deep evolutionary history. It took centuries to come up with advanced mathematics, so in some strange to humans sense, this isn't that hard. Same with language, it only took tens of thousands of years.

For Nature to learn how to develop a nervous system capable of flexibly interacting with the environment, culminating in our brains, took hundreds of millions of years.

This isn't an claim that we have to wait that long to re-engineer such powers, but it is to point out that if the possibility space for developing a nervous system was much larger than for the same organisms to learn language...

tldr; Walking is hard.

We have been conflating what is easy for us, with what is objectively easy, because we don't appreciate the Deep Time that Nature has been working with. I suspect we will develop EMs (brain emulations, a sort of short cut) before we understand what we are doing but I hope that is wrong.

<comment><USER16> If I am following you correctly, you are arguing that walking is a harder problem than language because it took much longer to evolve.

This seems to assume that a facility for language and advanced mathematics is independent of the existence of a nervous system capable of flexibly interacting with the environment, but it seems plausible, indeed probable, that language, consciousness and math depend heavily on the prior neural infrastructure, and their development was the most recent step in a process that has been going on since the evolution of the first synapse.

On the other hand, I am skeptical of the somewhat popular view that the key to generalized AI is to make robots that interact more thoroughly with their environment, and that they will then find their own way to language and consciousness. Partly, this is because I do not think that if you intentionally pursue the robotic goal, you will necessarily create the sort of infrastructure that is generalized enough to be the basis for the emergence of language.

<comment><USER15> > If I am following you correctly, you are arguing that walking is a harder > problem than language because it took much longer to evolve.

This is a thorny subject. So I am saying that in some objective way, walking is harder than language because Nature took millions/billions of years to traverse the solution space. Then... once we had a huge number of preconditions existing, then we had the development of language.

I am not saying that this means if it takes 10 years to develop language with some artificial means that it will take 100,000 years to develop walking.

What I am pointing to is that we ought to appreciate that if even blind natural selection took that long, then the possibility space to develop a nervous system must be much larger than we have anticipated.

As evidence of this: consider how (at least in popular culture, but also in comp sci in the old days) we developed chess playing computers and it was broadly assumed that breakthroughs in getting robots to walk and talk would soon follow through. That did not happen. It was a natural assumption but it was wrong.

> This seems to assume that a facility for language and advanced mathematics > is independent of the existence of a nervous system capable of flexibly > interacting with the environment, but it seems plausible, indeed probable, > that language, consciousness and math depend heavily on the prior neural > infrastructure, and their development was the most recent step in a process > that has been going on since the evolution of the first synapse.

I don't know the answer to that. On different days I think one or the other is true. On Day #1 I think Nature obviously required walking before talking, but we could develop them differently, just has we didn't need to develop better horses to produce cars. On Day #2 I think to myself there's a deeper sense in which you really do require walking before talking because otherwise why didn't Nature develop biological microlife which evolved communications ability long before it developed legs. So...

> On the other hand, I am skeptical of the somewhat popular view that the key > to generalized AI is to make robots that interact more thoroughly with their > environment, and that they will then find their own way to language and > consciousness.

We cannot be certain consciousness or intelligence are high probability events once you have life. We could be like those French artifact makers who made such exquisite mechanical toys for the aristocracy but ultimately got nowhere whereas the English inventors meddling with water and steam power really kicked off a revolution.

Who is Silicon Valley is genuinely looking at the fundamentals of A-Life or AI? OpenAI? MIRI? Stanford? DARPA?

</comment></comment></comment></comment><comment><USER17> I can't follow the author's logic. First he complains about the limited breadth of AI approaches (bottom up) and then he makes the case for more central coordination of research efforts.

Contrary to applied physics or medicine, AI doesn't require massive capital investment like building a particle accelerator or running clinical trials over years.

So if we already suffer from a lack of diversity, why should we ape the organizational structure of those fields?

<comment><USER18> The author's general problem is that he seems ignorant (or willfully ommiting) the expert systems period in AI (realized and popular in the 80s, academic foundations discovered in the 60s and 70s).

I agree with the article that GP AI is likely to ultimately be a fusion of bottom-up with top-down systems, and that expert systems seem to be getting short shift after their earlier failures while neural networks are possibly receiving overly optimistic expections.

To be fair, I believe this is the author: [https://en.m.wikipedia.org/wiki/Gary_Marcus](https://en.m.wikipedia.org/wiki/Gary_Marcus) , and he appears to have a cognitive neuroscience background as opposed to computational AI. So I wouldn't be surprised if he actually was unaware of 1960s-80s CS AI research.

<comment><USER19> Examples of top-down algorithms, in my opinion (since bottom-up and top-down are debatable concepts in many concrete situations), include:

\- Genetic algorithms

\- Q learning

In the sense that they learn general behavior first and then learn ever more little "tricks" to be used in particular situations. Both are more effective when combined with ANNs. But when they start they're only aware of very high level goals.

That said, I also have kids, and while they're bigger now, I would argue the idea that humans work top-down from the very beginning doesn't survive caring for a toddler for a few hours (babies can't really move, so they don't make particularly stupid decisions. Toddlers and up to teenagers make idiotic decisions that make sense from particular perspectives. For instance, they exhibit extreme short term decision making (like taking a huge risk of falling down just to get a little piece of candy).

Top-down decision making isn't just something that is eventual emergent behavior, it's learned behavior. Telling a toddler that to get candy he should go to the store, get flour, sugar and ... and follow this recipe doesn't work. They get distracted after 30 seconds. It's not that they're trying to fail, their mind just doesn't let them focus beyond a certain (short) amount of time. Adults have the same limit, just longer time, but they have learned to compensate for it. For instance using TODO lists, or project plans.

<comment><USER18> I've always had the suspicion that memoization (or lack thereof) is the primary reason really young children do and think a lot of the ways they do.

As adults, it seems like we don't actually experience every sensation of the world anymore. Most of the time it's already high level categorized (e.g. "apple") by the time it hits our conscious mind.

<comment><USER19> I agree, it's obvious that human intelligence is >99.9% a behavior copying algorithm. What we call rational thought is in reality restricted to conscious exercise and it is a learned skill. A trick, nothing more, and especially not a core part of our behavior. It is not that different, at a low level, from learning to juggle balls. Rational behavior, firstly, most people just don't have it at all, and secondly even in the people who do behave rationally occasionally, it is only when things are happening slowly enough and they're putting in constant effort toward maintaining that rational behavior, constantly second-guessing themselves and going back in memory every few minutes to evaluate your own actions and formulate a plan.

And if you've been to the third world (or just a large poor part of a large western city), you'll know this is true: billions of people have never learned to act rationally, and only few and far between will ever act rationally. You can do a thought exercise with these people and figure out with them what the rational action is, and the vast majority will simply act anyway.

</comment></comment></comment></comment></comment><comment><USER20> In 1988 Hubert L. Dreyfus and Stuart E. Dreyfus released a paperback version of their previously published "Mind over Machine" book, in which they mostly spend time debunking the myth that expert systems and rule-based programs are ever going to have "intelligence" on par with human brain.

The book is an interesting read in itself, but what I found remarkable is that in the 1988 release they added a "preface to paperback edition" in which they used a couple of pages to give their views on artificial neural networks, which (though not new) was gaining some steam at the time. The conclusions they reached are as relevant now as they were 3 decades ago.

There have been no new breakthroughs in this area. Most of the research being done is in application of what we have known for decades in specific areas, with minor insights into tweaks and uses of combinations of algorithms to better solve specific problems. The big differences between then and now are: (1) technology is more accessible - data is easier to collect, store and output via many input/output methods; and (2) the hardware is significantly faster - we can now go through more data, make algorithms run faster, and appear to perform better.

This inevitably brought a lot of hype, including many predicting human-like artificial intelligence not too far away. But maybe those with experience in 60s and 70s in the field in USA and Japan can draw a parallel between what's happening now and what has happened few times in the past in this area:

\- companies perform neat promising demos with unrealistic implicit or explicit promises

\- investors pour money in

\- media hype ensues

\- after awhile - no new breakthroughs: still can't turn ANN or expert system into a human brain

\- outcome is improvements in limited use cases

\- hype dies down, but we can repeat the cycle after improvements in hardware

Edit: formatting

<comment><USER19> > There have been no new breakthroughs in this area. Most of the research > being done is in application of what we have known for decades in specific > areas, with minor insights into tweaks and uses of combinations ...

There are 2 huge problems with that:

1) nobody is trying to "embody" an intelligence with any sort of research project behind it. Nobody's even trying to create an artificial individual using neural networks. There are several obvious ways to do this, so that's not really the problem.

Therefore I claim that your implied conclusion, that it isn't possible with neural networks somewhere between premature and wrong.

2) What if the difference between an ANN and our brain is a difference of scale and ... nothing more ? We still do not have the scale in hardware to get anywhere near the human brain, and just so we're clear, the differences are still huge.

Human neocortex (which is roughly what decides on actions to take): 100 billion neurons

Human cortex (which is everything that directs a human action directly. Neocortex decides to throw spear and the target, cortex aims, directs muscle forces, moves the body and compensates for any disturbance like say uneven terrain): another 20 billion neurons.

Various neurons on the muscles and in the central nervous system directly: a few million (mostly on the heart and womb. Yes, also in men, who do have a womb it's just shriveled and inactive). They're extremely critical, but don't change the count very much.

AlphaGo 19x19x48, times 4 I think. About 70000 neurons, and that does sound like the correct number for recent large-scale networks.

A human neuron takes inputs from ~10000 other neurons, on average. A state-of- the-art ANN neuron takes input from ~100, and since it's Google and they've got datacenters, AlphaGo was ~400.

So the state of the art networks we have are on par with animal intelligence of the level of a lobster, ant and honeybee. I think it is wholly unremarkable and understandable that these networks do not exhibit human-level AGI.

What is remarkable is what they can do. They can analyze species from pictures better than human specialists (and orders of magnitude better than normal humans). They can speak. They can answer questions about a text. They can ... etc.

Give it a few orders of magnitude and there will be nothing these networks don't beat humans on.

<comment><USER21> Hmm is this not the same argument that the classical AI gave with a few more rules and it will be alive!

</comment></comment></comment><comment><USER22> The author is just spouting off on a topic he doesn't understand. It's just a rehashing of Chomsky's hatred of statistical NLP. He pulls off the neat trick of approximating knowledge of artificial intelligence by hoodwinking the New York Times, but he doesn't have insight into the topic he's talking about.

<comment><USER23> Can you describe what you mean by "he doesn't understand"? His background is in psychology and neuro science so his view on intelligence is probably quite different than person coming from computer science.

For me he puts words onto something I've felt recently, that what we're doing is cool and all, but just doesn't feel like the right way to approach it. We're just putting loads of data and computing power into something that produces results that looks intelligent, but digging deeper bares no resemblence to what a neuro scientist would call intelligent..

<comment><USER22> I'm riffing on his writing --

> Even Google Translate, which pulls off the neat trick of approximating > translations by statistically associating sentences across languages, > doesn't understand a word of what it is translating.

This is just another incarnation of "AI is the thing we haven't done." He's parroting Chomsky's disdain for statistical models and John Searle's fundamental misunderstanding of AI. For the former, Norvig has a fair rundown of Chomsky's complaints ([http://norvig.com/chomsky.html](http://norvig.com/chomsky.html)).

> bears no resemblance to what a neuroscientist would call intelligent

TensorFlow gets results. The neuroscientist can claim it's a P-zombie, but they need to point to some criteria for accepting something as intelligence. Otherwise we're just moving goalposts.

<comment><USER16> >> Even Google Translate, which pulls off the neat trick of approximating translations by statistically associating sentences across languages, doesn't understand a word of what it is translating.

>This is just another incarnation of "AI is the thing we haven't done."

I don't think so - it appears to be an objectively correct assessment of the current state of the art.

> Otherwise we're just moving goalposts.

The first movement of the goalposts was to call '80s technology AI. Now they are drifting back to where they started.

On the other hand, I am surprised by the claim that AI is stuck; my outsider's impression is that progress has accelerated. Perhaps the impression of being stuck comes from more people realizing how difficult a problem it is.

<comment><USER24> >>> Even Google Translate, which pulls off the neat trick of approximating translations by statistically associating sentences across languages, doesn't understand a word of what it is translating. >> This is just another incarnation of "AI is the thing we haven't done." > I don't think so - <USER2> appears to be an objectively correct assessment of the current state of the art.

How deep an understanding is required to meet the threshold? The skepticism feels like "no true Scotsman" applied to the definition of _understanding_.

I observe the following in young children when exposed to a new word:

0\. First exposure to totally new word used in a sentence with more familiar words.

1\. Brief pause

2\. Mimic pronunciation 1-2 times

3\. Process for minutes, hours, or days.

4\. Use the word in a less than 100% correct way

5a. Maybe hear the phrase repeated back with the error "corrected" (hello internet)

5b. Maybe hear more usage of the word in passing from others (with varying degrees of "correctness")

6\. Recurse for life.

At what point did the person _understand_ the word? How is AI translation substantially different?

I'm not sure _I_ understand any word in a way that would satisfy AI skeptics.

<comment><USER16> This is a discussion of the current state of affairs, not, for example, a Searle-like claim that understanding can not and will never be achieved. To substantiate a claim of 'no true Scotsman', I think you have to present an actual case where you think a machine has achieved understanding, but which is being unreasonably dismissed.

Ironically, your last sentence has 'no true Scotsman'-like reasoning, along the lines of 'no true AI sceptic would fairly evaluate a claim of machine understanding.'

BTW, I am not a skeptic of the potential of AI, though I am skeptical of some claims being made.

</comment></comment></comment></comment></comment></comment><comment><USER25> AI is currently about giving us an equivalent of a pocket knife for some tasks that are viewed as being in the "intelligence domain". That's all. Nobody really thinks we are anywhere close to general intelligence. Or like the famous saying "computers are bicycles for brain", current AI is about adding a small electric engine to those bicycles to make them easier for everyone.

<comment><USER26> > Nobody really thinks we are anywhere close to general intelligence

The kind of people that attend AGI conferences do.

</comment></comment><comment><USER27> > Even the trendy technique of "deep learning," which uses artificial neural > networks to discern complex statistical correlations in huge amounts of > data, often comes up short.

That doesn't seem to be very surprising given the limited complexity compared to say a fly's brain. Artificial NNs manage to work because they are highly specialized to a specific task.

</comment><comment><USER28> The problem we've always had with AI was that most people were trying to engineer it rather than reverse engineer it. Every time there would be a major advance the computational neuroscientists would say: "we knew that, you should have come talked to us 15 years ago." There's some work out there on this, but it's more basic research on how to use developmental and genetic and evolutionary algorithms to grow neural networks. Most AI researchers try to skip this step but it's what's holding back progress.

<comment><USER15> What is a good book on genetic algorithms?

<comment><USER28> It's been about 10 years since I left the field. I believe Rodney Brooks did some work on this. Eggenberger was also doing some interesting work about 15 years ago--not sure where that is today. The computational demands for evolutionary & genetic algorithms are significant but there's no free lunch.

</comment></comment></comment><comment><USER29> > An international A.I. mission focused on teaching machines to read could > genuinely change the world for the better — the more so if it made A.I. a > public good, rather than the property of a privileged few.

> author: Gary Marcus is a professor of psychology and neural science at New > York University.

Not sure what he has in mind. There are already a lot of smart people building Q&A systems. We need tests to establish if a system can read. Once you have those then you can throw a competition up on Kaggle with a big purse.

<comment><USER30> No systems can really understand what they read or translate yet. They are basically sophisticated pattern matching systems.

Check out Winograd Schema: [https://en.wikipedia.org/wiki/Winograd_Schema_Challenge](https://en.wikipedia.org/wiki/Winograd_Schema_Challenge)

Overview by an expert: [http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/...](http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html)

An example: The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.

When you switch between "fear" and "violence", the meaning of 'they' change. There are many more examples like this.

The best performance in the first round of the 2016 challenge was 58% by a neural network based system. Random guessing would yield 44% (some questions had more than 2 choices). Human performance was 90.89% with a standard deviation of 7.6%.

Here are the challenge problems used in the first round: [http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/...](http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/PDPChallenge2016.xml)

Human Subject Test Performance: [http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/...](http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS2016SubjectTests.pdf)

<comment><USER31> It's important to note that Winograd Schemas don't really test if the system understands _those_ sentences, they essentially test the system has appropriate "common sense" knowledge/experience about how our world and society works, i.e., it tests whether the system understands whatever _other_ data sources are usable to find out about this topic.

To give the proper answer in the example you use, a human (or a system) needs to know how such permits are issued and what are the common reasons for refusing such permits. As such, a sufficiently sophisticated pattern matching system is perfectly sufficient to answer such questions - there's a simple pattern difference that fearing violence causes you to refuse permits but advocating violence causes you to get refused. It's worth thinking about where do humans learn this? For the Winograd schemas like putting a trophy in suitcase, it's the basic childhood experience of putting stuff in boxes that we all share, but a machine won't (unless it's raised as a child-robot). For schemas like this one, it's understanding how our society works learned by participating in our society for years, which we all share, but a machine won't (unless we allow machines to participate in our society). I.e. it's not so much a measure of _intelligence_ as a measure of shared background experiences. A human from a hunter-gatherer tribe wouldn't be able to answer the councilman-permit schema, but that doesn't mean he/she isn't intelligent.

The difficulty there is caused mainly by the need to have domain-specific knowledge in a wide range of domains - we will perceive systems as "dumb" unless they share the same background knowledge that most humans have gained by being part of our society and basic schooling, and since the machines won't do that (yet), we're looking for "unnatural" ways of getting common sense knowledge without the direct experimentation and participation that we do.

</comment></comment></comment><comment><USER29> Click-baity. AI tech isn't stuck. There are many forthcoming breakthroughs, particularly in medicine, which should really benefit humanity. Radiology is poised to let CNNs make radiologists a lot more efficient. We just need to build the labeled datasets.

If we invest heavily in some AI tech, let it be to produce huge medical datasets. The software and hardware is ready. We're only lacking sufficient data to make more diagnoses with super-human accuracy.

<comment><USER32> _> There are many forthcoming breakthroughs... We just need to build..._

Not sure how you don't see the irony. This has probably been said thousands of times for many scientific areas throughout history. Example:

There are forthcoming breakthroughs in humanity being an interstellar civilization. We just need to build faster-than-light engines and terraforming equipment. Nothing major, right?

<comment><USER29> > There are forthcoming breakthroughs in humanity being an interstellar > civilization. We just need to build faster-than-light engines and > terraforming equipment. Nothing major, right?

Building a dataset is easy and not something you would compare to faster-than- light engines. Believe it or not, some major breakthroughs _are_ held back by simple lack of funding, and lack of awareness.

To make a dataset you need to pay radiologists to label enough data for the system to do its job well. This could be thousands, or hundreds of thousands of images. It is _technically speaking_ very doable, but also very expensive. Then there are data privacy issues stopping you from sharing data. These are social issues, not engineering issues.

<comment><USER32> Your reply, while overall correct, still underestimates the problem of "how do we invent AI?". Data-sets shouldn't be "tuned" or "refined" (tems I see in practically every "AI" article); if they need to be remade then the consumer is not only not AI -- it's not even a clever NN implementation.

Forgive my cynicism if you can, but in my eyes you guys just support what might make you money one day (or already does) and thus aren't objective. You're like the parents that are completely blind to their child's defects due to paternal / maternal hormones.

There's no AI on this planet. There are not even _beginnings_ of an AI. Deep learning is practically a statistically biased classification algorithm and not much else.

To me the term AI is being abused. I want AI to exist, but I am seeing every indication that the area is falling victim to capitalistic interests and this won't change anytime soon.

<comment><USER29> > Your reply, while overall correct, still underestimates the problem of "how > do we invent AI?"

I'm not talking about building a real AI.

I actually agree with you that we're nowhere near developing that. Not sure where you got any other idea from me.

I'm saying there are some machine learning problems that could be served by some simple data entry. This could save lives, including yours and mine, via advanced cancer detection [1]

You're right that since I studied data science, I'm incentivized to advertise its usefulness. But, I studied data science because I believe it is a growing part of our future.

You can try it yourself too. There are many tutorials online. Making use of machine learning gets easier every year.

[1] [http://money.cnn.com/2015/03/12/technology/enlitic- technolog...](http://money.cnn.com/2015/03/12/technology/enlitic- technology/index.html)

<comment><USER32> I would gladly take the time, if only I had some. :(

Thank you for your kind answer.

</comment></comment></comment></comment></comment></comment><comment><USER33> Saved you a click: huge government program to teach ai to read

<comment><USER33> well, someone broke off the clickbait from the headline... It used to be: "Artificial Intelligence is Stuck. Here's How to Move It Forward."

</comment></comment><comment><USER34> Is the Author arguing there will be another AI Winter?

I have a simple theory ( I am not sure if there is a proper term for it ) that goes and solve all the above problem mentioned.

When ever a technology that is capable of producing some form of economical value, it will continue to improve and tackle what ever hurdle or barrier you think it has.

In all of the previous AI era, research were funded by government or large company like iBM. But none of those has ever made a impact or profits that value more then you have invested in. Expert System never caught on.

And this is why everyone is excited, for the first time ever we have AI ( Machine Learning ) producing useful results in a MUCH MORE cost effective way. And these saving will means companies are investing back into AI research for further improvement and benefits. The whole AI research has created a self sustained cycle that we know, at least for the next 5 - 10 years will not be lacking any fundings.

</comment><comment><USER35> "Stuck"? Cmon, imagenet winning deep nets are 5 (five) years old. Give it time.

</comment><comment><USER36> I agree that AI is hyped, but I believe the problem is that we absolutely don't care about 50 years of neuroscience research. We know so little about the brain, but already much more than back when "artificial neurons" were modeled. The only company that I believe is on the right track is Numenta, they focus on reverse-engineering the neocortex. They have a living theory that is updated every time there is a research breakthrough.

</comment><comment><USER37> AI definitely isn't stuck, unless you define it solely as creating artificial general intelligence. The problem there is that we don't understand general intelligence very well at all.

Of course the fixed graphical models we use have their own problems. For instance, we can't even effectively model a neural network with a variable number of inputs.

<comment><USER38> I'm not sure why you think neural networks can't handle variable numbers of inputs. Recurrent networks that ingest whole sequences have been around for a long time, and other structures have their own network topologies. Support for things other than classic RNNs is more limited, but e.g. TensorFlow Fold ([https://github.com/tensorflow/fold](https://github.com/tensorflow/fold)) was specifically designed for that.

<comment><USER37> RNNs are a good model for things that are naturally sequential with limited state transfer. They are not so elegant for things with no defined ordering and a large amount of shared state.

<comment><USER38> Can you give an example of a problem where you have "no defined ordering and a large amount of shared state"? What kind of model is typically used in that domain?

<comment><USER37> One problem I've been considering is triangle surface meshes. The data is variable in size, with no defined start or end point, where points distant on the surface may share a high amount of mutual information (through symmetry, etc).

One approach I've thought about is applying kernel methods. You can compose kernels, so they scale up cleanly regardless of variations in the input dimension. The sum or product of kernels between each node in the input graph and some basis set is itself a kernel. If your kernels describe covariance between observations (i.e. Gaussian processes) then additional input dimensions have a constraining effect, rather than causing evidence inflation for larger inputs as a typical neural network might.

</comment></comment></comment></comment></comment><comment><USER39> The article is calling us to teach AI to read words and phrases and look for the meaning, not just a statistical correlation.

I put dictionary data into Pingtype English to try to parse phrases instead of just words. e.g. "pick [something] up". The purpose is to do word-for-word translation to Chinese as an educational tool. It's not perfect, but the dictionary is editable. You can contact me if you want to discuss new ways of extending the features (e.g. data from UrbanDictionary, movie subtitles, etc).

[http://pingtype.github.io/english.html](http://pingtype.github.io/english.html)

I also want to correct the author that CERN does not have billions of dollars of funding. There's only about 5000 staff, and the other 10,000 people working there are funded by universities elsewhere who send them to CERN to do the research.

<comment><USER40> The CERN budget is roughly a billion Euro per year.

<comment><USER41> some numbers from [https://press.cern/facts-and-figures/budget- overview](https://press.cern/facts-and-figures/budget-overview)

in 2016, 1127.2 million CHF or 1,163,675,662.22 USD

<comment><USER42> A billion dollars' research into AI would be more beneficial than another billion spent on CERN.

The Standard Model covers well over 99% of known physics already. Other moneys are being wasted paying students and professors to study string theory w/o any experiments possible.

Let's develop some true AI and let it close the gap. Two birds, one stone. [And maybe we can find out how we do analogies, at the same time!]

</comment></comment></comment></comment><comment><USER43> Most of the AI progress in last years is just tuning pattern recognition algorithms. We can not expect these algorithms to produce results like humans, because humans have a lot of information not just from percieving the world, but their patterns of thinking are also vastly dependend on the underlying structure of brain, that has developed over milions of years of evolution.

If there is a cliff, toddlers are scared of being nearby. They definetely don't have the ability to "imagine=simulate" the consequences of falling over the cliff. The fear is in the structure of neurons of brain.

If you feed classifier algorithm with images of black dogs and white swans and then want to classify black swan. Both classifying it as dog(because of color) or swan(because of shape) are right. The difference is only in bias, which features do you prefer.

<comment><USER44> You must watch the documentary "The secret life of babies". There is a scene where they put several babies on a "cliff" situation (with a glass in the same level to prevent them falling), they will confidently march into the glass - they do several other experiments that prove babies are fearless.

[https://www.netflix.com/title/80009352](https://www.netflix.com/title/80009352)

<comment><USER42> Babies are not born afraid of the cliff (i.e., it isn't hardwired into the infant brain); they develop fear of it at about 9 months' age, once their depth perception has developed.

</comment></comment></comment><comment><USER45> It's great to read someone challenging the hype, but this analysis is a bit too negative. I'd point out that AlphaGo did something a little bit like the author's toddler learning to squeeze through the back of her chair. AG learned its new moves by playing against itself. I still wonder how they avoided over- fitting here. Imagine two twins who loved to play and learn about go, and played constantly against one another, and in the process discover new plays they both believe to be unbeatable. You'd expect some of their new moves to be weak when actually tested against other players. But AG's new moves really were strong.

Admittedly, games with simple scores are the only scenarios where this really kicks in. But then again, the stock market could fit this model.

</comment><comment><USER46> This article is actually pretty accurate in that it identifies that neither academy nor industry is well suited to solving AGI.

Suppose that a real solution to AGI will actually take 10 years to solve with minimal milestone achievements along the way. In other words, until you have the complete system figured out, it'll be hard to see the results.

In academia, most people are ultimately focused on getting their paper published.

In industry, most people are ultimately focused on making a profit.

In both cases, people would get off track long before they reached the full solution.

Lastly, the principles behind which a real AGI operates are likely so abstract that everyone reading this will likely be long dead by the time humans stumble upon them.

The only way we can short cut this process is by looking at the solution (ie the way Numenta is doing it).

</comment><comment><USER42> No need to involve other nations: the US military is moving ahead on its own:

[http://www.insidesources.com/nsa-chief-without-ai-cyber- is-a...](http://www.insidesources.com/nsa-chief-without-ai-cyber-is-a-losing- strategy/)

From the 2016 article:

"Artificial intelligence will play a big role in the future of U.S. strategy in cyberspace, according to National Security Agency Director Adm. Michael Rogers, who told Congress Tuesday that relying primarily on human intelligence 'is a losing strategy.'"

The lineage of military intelligence systems using AI is (necessarily, historically) heavily biased toward language-based AGI ("old AI") rather than neural networks. The NN are there of course, but IMO the impressive work is in the AGI.

</comment><comment><USER47> What do you do when this software discovers it's rewarding to compete with you for physical space?

The problem with AGI is it'll implicitly have to model the entities it interacts with and that may present two challanges:

1) Developing robust strategies for managing an AGI discovering a greater reward response from defecting than for cooperating with people / developing strategies for managing scenarios in which a quorum of AGIs discover it's rewarding to collude to the detriment of humans / cellular life.

2) The tractability of maintaining one language model per entity across channels.

The actual implementation could be done by plugging a handful of related techniques we've developed over the past couple of years together though.

</comment><comment><USER48> Why does everyone assume human intelligence is computable? Seems we should be checking that assumption at this point since we've made so little progress, and a definitive answer is much more valuable than this ongoing speculation.

<comment><USER49> Philosophers have been saying this for 60 years Hubert Dreyfus' 1972 book "What Computers Can't Do" is a notable example.

<comment><USER48> It's alright, but I don't recall the book articulating actions computers cannot do. It seemed to still leave open the possibility that we can automate all human work. What we need is a precise task that humans can do with ease but we can prove is impossible for any computational device whatsoever.

<comment><USER49> My understanding was that he thinks we can potentially automate all human work, just not with computers. A precise task isn't necessary: AI researchers are simply mistaken about the nature of intelligence.

<comment><USER48> Dreyfus doesn't know his comp sci. Everything automatable is computable. The interesting question is whether human intelligence is automatable.

<comment><USER49> >everything automatable is computable

why do you suppose this?

<comment><USER48> Everything physical is computable.

<comment><USER49> So you have to prove that all human work is physical when some of it doesn't seem to be physical at all (creative work).

<comment><USER48> Yes the non-physical work is not automatable.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER50> I'm not sure about "stuck", or that a huge international affair like ITER is a good solution. We could have maybe AI with our maybe fusion for $70 billion in 40 years ;)

But watching my 1yo learn to toddle around and navigate does show just how limited current AI is. With tons of training and battery, we can coax a computer to barely do what my 1yo does on a belly of cherrios and a few hours of trial and error.

There's lots of great stuff and some terrifying stuff happening in AI and I don't doubt more to come, but watching kids learn puts it in perspective for me.

</comment><comment><USER51> We humans don't know how to learn. We don't know how learning works. We simply work work work work until we know whatever we set out to know, we don't learn how we learned it, but are happy that we simply know it and leave it at that.

Therefore teaching someone/something else how to learn will be almost inherently impossible, because we don't understand it ourselves (yet?)

And if we do learn how to learn, why would we need an AI to do it for us?

<comment><USER52> We barely know which regions of the brain recognize shapes, let alone exactly how it does so, yet we've built machines that can do it.

<comment><USER53> Take any NP-hard problem at its simplest form. Whatever half assed heuristic you use chances are the solution becomes optimal (if it is even half good.) The more complex the problem the more the heuristics fall apart. For the smaller ones we can simply brute force it. The same thing happens here. Compared to something like face recognition, recognizing simple shapes is much simpler. So even the not so great techniques (comparatively) work well enough. They fall apart when we use the same methods for the complex stuff.

A great example would be a greedy algorithm. It works for some problems but doesn't for some others. Take a simple enough problem and you get optimal solution. Push the algorithm to its limits and you don't even get a good solution. You don't have to understand how the best algorithms for a task work to come up with a greedy algorithm.

</comment></comment></comment><comment><USER54> We (humanity) have made huge progress to understand images in terms of content and emotions of people. Imagenet is truly a gift to the world. However, that has brought us only a small but important step forward. Clearly expectation has to catch up to reality. However, all these solutions are becoming quickly more accessible to the laymen bringing another boost to operational efficiencies for companies worldwide.

<comment><USER29> > Clearly expectation has to catch up to reality.

Woah, you feel expectations are behind reality? I feel there's a lot of news lately predicting AGI.

<comment><USER55> I actually do feel that expectations are behind reality, at least amongst those who are just barely too smart for their own good. I still see comments daily on HN or Reddit that promote the narrative that there is no AGI, people only work on ML, and all ML is a narrow party trick. And I think that is a _terrible_ characterization of what, e.g., the computational neuroscientists are doing. Peruse some of the research happening at MIT and Stanford right now, and I don't see how anyone can cling to the "it's just ML" canned response.

<comment><USER32> Maybe because, gods forbid, people judge by real-world results and not by the words of a bunch of narrow specialists patting themselves on the back?

The author's points still stand. Robots do fall over trying to open doors and they don't invent new ways to climb a chair. This is a fact. The terrible characterization you speak of is well-founded in observable reality. That is a fact as well.

<comment><USER55> If this is your attitude towards long-term academic research, there's little hope I could expand your awareness of what the state of the art in AI actually is. I'm reminded of a point Yudkowsky made 10 years ago:

[http://lesswrong.com/lw/kj/no_one_knows_what_science_doesnt_...](http://lesswrong.com/lw/kj/no_one_knows_what_science_doesnt_know/)

<comment><USER32> I am sure you misunderstand me but I gladly take the blame for it. I am all for people doing experiments just for the heck of it _and being paid for it_ \-- we as a race need a lot more leisure and discovery time. We're being robbed of leisure and discovery time more and more with each passing year, we always owe somebody money, there's always something else that is urgent to do, and in the end we never get to just slack for a year or two, especially after a burnout -- something that was deemed very normal even only 50 years ago. This is an awful period of human history and one I am sure will be remembered with great deal of shame one day. But let me not digress a lot...

I am 100% behind science, experimentation, and even silly / goofy discoveries whose usefulness might come centuries later (or never; I am okay with that). Please don't get me wrong. _We need much more of that as a race_.

I will also immediately agree that I am oblivious to what is happening in the AI area. But can you blame my cynicism? Everybody, their dog, and its butler are now claiming to do "AI innovation" and in the end 99% of them just swallow investment dollars, figure out a lucrative exit, and some even _repeat_ that a year or two later. Naturally, people get worn out and start putting snarky remarks when they hear the now-meaningless term "AI" \-- I am one of them, and I don't feel bad about it. I believe the sarcastic attitude is well justified.

Everybody keeps praising certain, _very specifically tuned_ , NNs when they do certain very specific tasks. Fine. I will grant you that I can't code the algorithms needed to surpass human doctors in recognizing latent cancer or any kind of early signs of a dangerous disease. This is true. But the current way of doing things is like "input heckton of data, go to lunch, expect magic when you return". It definitely feels like it, even if I know that it's not factually true.

NNs show bias. Seems like nobody cares, they're like "yeah we know it's a problem, we'll get to it" and yet there are NNs that very likely already deny black families loans due to the inherent bias in the datasets they've been fed with. The concept of implementing a truly explainable AI seems to be very new _when it had to be there right from the start and shouldn 't have ever been missing_; what are you people even thinking?! A driverless car makes a strange decision and what, "the NN worked perfectly"?! Bah.

To me, "AI" advocates are very content to deny very real issues that exist _RIGHT NOW_ and that makes me cynical about that branch of science since you guys always seem to try and sprint into the future while blindfolding yourself about things that need attention here and now.

I admit I got off on a tangent. In any case, these are my collective thoughts on the topic.

</comment></comment></comment></comment></comment></comment><comment><USER56> We are using the wrong computational paradigm. We have to abandon bits and go back to analog computing in the form of analog photonic computing that gives you fast Calculus. This is painfully obvious in the case of neural networks, which run faster on an analog computer and are also easier to program.

<comment><USER57> I think you are out of date. Rectification is pretty good at removing a lot of the vanishing gradient issues that nn's used to face, and the overwhelming power of modern digital computers (50k cores is common) make this all moot as far as I can see.

<comment><USER56> Nope, they aren't even in the same category. E.g. Notice that there is a cpu size limit because of heat. Do you know what doesn't overheat? A photonic computer. You can build a cpu the size of a house. Also how does the number of cores constitute "overwhelming power".

<comment><USER57> :o) well, due to the wavelength of light a photonic cpu has to be 50->100 times the size of a current gen electronic one.

When I were a young 'un we had one core, and it ran at 25Mhz and about 130 of us shared it. Now I have 50,000 cores that run at 2 Ghz and five people share it. Things aren't quite directly comparable but the speed up is at least 100,000x I am overwhelmed by this, things that would have taken 1000 days; approximately 3 years, can be achieved in ten or twenty minutes. In reality the use of these infrastructures has enabled (in neural net land) the development of techniques that improve performance by several more orders of magnitude - so things that would have taken several years are now done in a minute or so. I believe that there is plenty more headroom to be had.

<comment><USER56> Where is that estimate coming from? I mean year there has been progress by why do you think photonic would be even better?

<comment><USER57> 1989->2017

Photonics are promising QC technologies - especially Phonons, but we are a long way off!

</comment></comment></comment></comment></comment></comment><comment><USER58> Reddit discussion: [https://www.reddit.com/r/artificial/comments/6qcx6t/research...](https://www.reddit.com/r/artificial/comments/6qcx6t/research_labs_in_academia_or_big_tech_companies/)

</comment><comment><USER59> So a three year old finding an unanticipated way to slip out of its chair is evidence that it is smart, but a neural net finding an unanticipated common pattern to all school bus images in its training set is evidence to the contrary?

</comment><comment><USER60> The international resource mission is called open access to papers and open source AI software. See things like Tensorflow, Open AI, etc.

</comment><comment><USER61> I didn't read this article for the simple reason that NY Times is not the place to learn any insight about AI like this. They are article churning machine that serves political propaganda and useful local news and analysis. Anything science based, not really their thing.

</comment><comment><USER62> The article is riddled with errors that undermine its own thesis.

It starts badly:

 _> Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go_

This is not a secret, let alone a dirty one. Even 5 minutes casual research into the state of AI will reveal what it can do and what it can't.

It says:

 _> Such systems can neither comprehend what is going on in complex visual scenes ("Who is chasing whom and why?") nor follow simple instructions ("Read this story and summarize what it means")._

In fact comprehension of (very) simple stories is now more or less a solved problem. I wrote about performance on the bAbI tests here:

[https://blog.plan99.net/the-science-of-westworld- ec624585e47](https://blog.plan99.net/the-science-of-westworld-ec624585e47)

Summarisation of stories is also something with good recent results:

[https://research.googleblog.com/2016/08/text- summarization-w...](https://research.googleblog.com/2016/08/text- summarization-with-tensorflow.html)

Summarisation of arbitrary video is harder but given that object and path extraction already works well, it doesn't seem very implausible that we'll see some good research results in video summarisation systems within a few years. Extrapolation from what's happening to hypothesised explanations is a lot harder but not hard to imagine it being possible given the direction research is going.

 _> My daughter had never seen anyone else disembark in quite this way; she invented it on her own. Presumably, my daughter relied on an implicit theory of how her body moves, along with an implicit theory of physics — how one complex object travels through the aperture of another. I challenge any robot to do the same._

Challenge accepted:

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

And for the imagination component:

[http://www.wired.co.uk/article/googles-deepmind-creates- an-a...](http://www.wired.co.uk/article/googles-deepmind-creates-an-ai-with- imagination)

 _> To get computers to think like humans, we need a new A.I. paradigm_

That's not clear at all, given recent research. It is an odd statement from someone who has worked in AI. But then as the author is not a computer scientist, perhaps not that odd.

Modern neural networks are so similar to how humans think that psychological techniques are being used to understand and "debug" them:

[https://deepmind.com/blog/cognitive- psychology/](https://deepmind.com/blog/cognitive-psychology/)

I'm not sure how "think like humans" can be easily defined, but using strategies developed to understand human thinking on robots seems like a good starting point. Making mistakes similar to what you'd expect humans to make is also a good sign.

 _> But it is no use when it comes to top-down knowledge. If my daughter sees her reflection in a bowl of water, she knows the image is illusory; she knows she is not actually in the bowl_

She does now. But it takes time for babies to learn how to interpret mirrors.

[http://www.thoughtfulparent.com/2009/10/child-psychology- cla...](http://www.thoughtfulparent.com/2009/10/child-psychology-classics- mirror-test.html)

Animals usually never learn this, though a few very intelligent species can.

I don't see any obvious theoretical reason why image recognition engines shouldn't be able to understand mirrors, given sufficient research.

 _> Corporate labs like those of Google and Facebook have the resources to tackle big questions, but in a world of quarterly reports and bottom lines, they tend to concentrate on narrow problems like optimizing advertisement placement or automatically screening videos for offensive content._

Another bizarre statement given the author's background. Google and Facebook have been investing massively in very long term AI research and building many things along the way of no direct commercial value, like AIs that play games. I don't see Google's public AI research focusing on the cited problems, although it would not surprise me if there are parallel efforts to apply research breakthroughs in these areas.

 _> An international A.I. mission focused on teaching machines to read could genuinely change the world for the better — the more so if it made A.I. a public good, rather than the property of a privileged few._

And here we have it ladies and gentlemen .... the reason the article is so filled with factually false and logically dubious statements. It is an advocacy piece for new social policy: a vast new government research investment in academia, in which presumably Mr Marcus would like to be employed (rather than at Uber).

Besides, even this last paragraph is disingenuous. There does not seem to be any risk of AI becoming "the property of the few". In fact the large corporate research labs are doing fantastically well at publishing research papers and making the results of their work publicly available and useful ... in fact given the relative quality of corporate vs academic open source releases I'd say they're doing better than academia is. It's hard to imagine universities producing something as robust and well documented as TensorFlow.

</comment><comment><USER63> \- Pursuing low-hanging applied-solutions fruit under the guise of a grand mission statement of (AGI) [which is now all the rage], results in one becoming (stuck). You maybe can fool investors and the lay with such madness, but you can't fool yourself nor the matter at hand.

\- Not staffing or structuring like you understand or respect what General Intelligence is results in a narrow and specialized mindset among your employee base that produces narrow and specialized solutions.

It's called a local minimum. It's where you land when you don't focus on the bigger picture.

> How to move forward? There's several techniques for that. I don't see them > being used. Which either means they don't understand they're stuck or they > know they're stuck and don't care. Why would the latter mindset be willfully > chosen?

Current models and methods for training AI require huge data sets and computational power to be effective. Who currently maintains such resources? Whose fueling and molding the perception and direction of current efforts? See the conflict of interest?

Furthermore, given how convoluted the approaches/math are, it lends itself to specialized individuals...PhDs. A match made in heaven that allows the market to be narrowed and segmented to a specialized group of people. The problem with this is : It results in narrow and Weak AI.

> Not knowing you're stuck Enough people have made sound arguments. You either > grasp them and change or, given how comfortable you are, stay the course. It > could be, even with a PhD and clout, that you're just not that intelligent > enough to grasp the sound arguments... But, if this is the case, do you > really think you're going to solve general intelligence?

Those that (truly) seek to move forward have been moving forward with (AGI).

> Those attempting to preserve old business models with a fresh top layer > coating of the new. > Specialist who refuse to respect anything beyond their > group's chosen methods and thus respect the scope of AGI. > Well-funded > groups who exclusively hire from a narrow scope and narrow specialized focus > > VC groups that only invest in low hanging fruit applied engineering > ventures > VC groups that don't give those in (true) pursuit of this funding > Will just get let behind with the new wave. It's the same as it's always > been. You maybe can fool yourself and others. However, you can't fool the > laws of nature and the universe.

Enough people have spoken. Enough hints have been given. Enough people have taken and borrowed concepts of the small fry and called it their own only to find themselves lost in what it meant.

Enough time has elapsed. If you're not acting and steering your resources in accordance with the new, you just get left behind grasping the old.

Same as its always been... (True) disruption.

It's on the horizon. It's coming. So, keep your eyes peeled.

</comment><comment><USER64> _Some of the best image-recognition systems, for example, can successfully distinguish dog breeds, yet remain capable of major blunders, like mistaking a simple pattern of yellow and black stripes for a school bus._

That's exactly the problem. Robots lack sanity checks because they lack real understanding. If you cannot recognize an object that is far away, you are instantly aware of your inability to identify this object. A computer just runs its code over it and outputs complete garbage, and this nonsense then enters the system and does who knows what damage.

Plausibility checks are incredibly complex! If you are in central Europe and you are not in a zoo and you see a leopard fur pattern, it's probably not the living animal! And so on.

<comment> GuB-42 Image recognition system just recognize images. They essentially do the first pass of what your brain can do.

You too can mistake yellow and black stripes for a school bus or see an actual leopard in Poland. That's when you put what you've seen in context that you rule out the idea. And if you really want to see something in a picture, you will, especially with faces.

It is no different with computers. You train your algorithm so see school buses exclusively and it will see school buses everywhere. Conversely can also teach it context, for example by taking account of the webpage hosting the image.

Computers algorithms usually have a confidence rating too. They can tell "definitely a school bus (99%)" or "looks vaguely like a school bus (30%), but it may also be a wasp (10%)", so they can be aware of their own flaws. In fact, confidence intervals are often a key part of machine learning.

</comment></comment><comment><USER65> Pattern recognition and feedback loops aren't enough. Artificial Intelligence require Artificial Emotions.

<comment><USER66> What makes you think emotions are anything else than pattern matching and feedback loops?

<comment><USER65> Emotions are "learned heuristics".

</comment></comment></comment><comment><USER67> My first impulse is to think "oh another socialist rant from the NYT"...

And so is my second.</comment>


Ask HN: How do you keep project knowledge public? - <AUTHOR>

One common aspect to many successful teams I see is that they use some sort of knowledge sharing and open discussion platform instead of a forum.<p>What forum/wiki/project tracking software do you use to share professional knowledge? <comment><USER2> We use Redmine [http://www.redmine.org/](http://www.redmine.org/) \- works well, nicely integrated with wiki, issues, repository browsing. Wikipedia overview: [http://en.wikipedia.org/wiki/Redmine](http://en.wikipedia.org/wiki/Redmine)

Chiliproject [https://www.chiliproject.org/](https://www.chiliproject.org/) is a fork of Redmine. I have done a test install but not used it seriously.

I've used Trac [http://trac.edgewall.org/](http://trac.edgewall.org/) also. I like Redmine better because it has (or had, have not checked lately) better multiple-project support.

Our "front office" folks use Alfresco [http://www.alfresco.com/](http://www.alfresco.com/) but their use of it is almost exclusively as a Microsoft Word storage shed.</comment>


California bill to add housing in single-family home areas blocked by lawmakers - <AUTHOR> https://www.latimes.com/politics/la-pol-ca-housing-single-family-zoning-senate-bill-50-dead-20190516-story.html <comment><USER2> It would be more accurate to say it was blocked by NIMBYs...</comment>


To PGP/MIME or not to PGP/MIME - <AUTHOR> https://www.mailpile.is/blog/2014-11-21_To_PGP_MIME_Or_Not.html

<comment><USER2> I just started looking into PGP (well, gnupg to be honest).

The plan is to both use gpg for communication/email, but also for backups (duplicity), secret stuff/passwords (pass) and authentication (ssh).

For that I'm (not affiliated) using a yubikey neo¹ for now. Advantages:

\- Very easy to set up (works for signing, encrypting, authentication already)

\- Contains the key material on the 'smart card'

In addition my main key is ~elsewhere~: After loading three subkeys on the yubikey I saved and removed my main key from the keychain. This _should_ solve the problem of the airgap in this article? A reasonable way to get access to your data/mail without giving access to your secret key?

Things I am confused about/haven't thought through yet/reasons to not switch to use it completely yet:

\- I'm confused about the key backup solutions here. Keys generated on the smart card cannot be backed up as far as I can see. I have a decent idea of how I'm going to handle my master key (both where and how I'll store it safely), but .. the rest is still not quite clear to me

\- Mobile. So, if my day to day keys are on this smart card, I cannot read those mails on my mobile. Which .. might be a good thing when we talk about security, but a PITA for my day to day usage. No idea how or if I can solve this one

But back to the article: If you're in the "whistle-blowers, activists, spies" category, is the airgap (physically) really required or would a smart card give you the same protection?

And the

>Mail clients implementing some sort of "Email Manifest protocol" could agree to move critical headers out of the main message and into the encrypted manifest as often as possible

line seems overly optimistic in my opinion. Repeat after me: Microsoft Outlook. And .. the introduction is basically PGP/Mime doesn't work everywhere and/or needs plugins first (-> Bad user experience). Is a brand new proposal that wouldn't work anywhere for quite some time really the answer?

1: I got an older release, so no U2F for me, but this is - among other things - a 'smart card'

<comment> mike-cardwell > If you're in the "whistle-blowers, activists, spies" category, is the airgap > (physically) really required or would a smart card give you the same > protection?

Smartcard doesn't protect you if your Internet facing machine is compromised. With a separate air gapped machine, you could print out the cipher text from the Internet connected machine, then scan it in/OCR it into the non-Internet connected machine and decrypt it there. Then you could encrypt a response on the non-Internet connected machine, read the cipher text back in to the Internet connected machine via scanning+OCR and send it out.

<comment><USER3> ...Assuming your input stack on your non-internet connected machine is uncompromisable.

In practice, this is decidedly not the case. OCR is complex. Your attack surface is not exactly tiny.

<comment><USER4> _Good_ OCR is complex. "Here's a jpeg with 80 lines of 80 characters (from a set of 64 ascii characters chosen for maximum descernability); do your best and I'll run that through a ECC that cuts down to a 32KB chunk." is rather less so. Also, there's no reason your OCR program needs any permissions other than reading a single jpeg and writing a single text file.

<comment><USER3> You can make the same argument about a non-airgapped computer, though.

I don't see any advantage of this over (say) it connected via a dead-simple serial connection.

</comment></comment></comment></comment></comment><comment><USER5> This does make sense and is backwards compatible, though what about HTML messages? Will you add the simple to/from clutter to the top of the html content, too?

Instead of trying to extend the standard, why not just nest an unencrypted email in an encrypted email? I'm thinking along the lines of:

1\. Someone writes their message into the client and attaches some files.

2\. The client generates an unencrypted, standard multi-part email message as message.eml.

3\. The client then pgp encrypts message.eml to message.eml.pgp.

4\. The client then generates a new email with message.eml.gpg as an attachment and sends it.

5\. The recipient receives the message and sees one attachment message.eml.gpg.

Most native clients can open arbitrary .eml files as email messages. This makes it much more convenient to interact (reply/forward/etc.) with this technique, and it is also HTML message compatible. Also, if you have a PGP plugin for your native client, it would decrypt the attachment, and you could just double click it to open that message to read/reply/forward/etc.

As far as web clients, most can't decrypt pgp messages so you'd have to download the message.eml.pgp. And I guess you'd also have to have something that can read .eml files. However, it should be fairly easy for pgp-enabled web clients like Mailpile to open these messages since they have to already have a message format parser.

Anyway, thanks for the article!

<comment><AUTHOR> Thanks for reading, and thanks for the feedback!

HTML mail is certainly a problem - editing HTML in that way is not nearly as easily done (nor reversed) as plain text. This is a loose end in the current proposal. Like many techies, I'm somewhat guilty of wishing HTML mail would just go away... However, in all fairness, HTML mail is not often used with PGP today and is somewhat of a "luxury" item, not a critical part of how people communicate.

I did consider embedding a full message/rfc822 part instead, and that is also what Werner (the GnuPG author) prefers as well. If PGP/MIME had been implemented that way in the first place, and clients had evolved with that in mind, I'd probably just be happily using that.

Unfortunately, the way message/rfc2822 attachments are handled by current mail clients, is not nearly streamlined enough that I'd be happy making people jump through those hoops for every single message.

I'm trying to find a sweet spot of working well enough with existing PGP-aware clients, better with PGP-unaware clients, and addressing the limitations of PGP/MIME at the same time. It's a bit of a balancing act and so far the easy/elegant solutions don't seem to pass muster when used in the real world. But I'd love to be proven wrong about that.

</comment></comment><comment><USER6> Good article. I wonder why Mailpile are targeting difficult use-cases such as activists, journalists, etc. It feels like every PGP provider aims for this, but OTR encryption tools aim for a much easier use case: average users who just want to hide their private conversations from big data algorithms which then sell their secrets publicly. OTR is much easier as a result, but it can't do email (that I know of). :-(

In this article mailpile worry about users who need an airgap. What worries me is whether creating features for airgap users makes anti-features for users like me who just use PGP when mailing with my parents, my wife, and some friends. We just want to avoid our secrets being part of "Big Data" and as a side benefit we resist passive surveillance. Mailvelope (which is "easy") is complicated enough for my parents, they would never add the complexity of an airgap. We just want easy encryption, even if it isn't totally NSA-proof.

<comment><AUTHOR> Fair question. :-)

I mentioned those users as one group that was impacted, not as the be-all-end- all target audience. If you re-read the relevant section you'll see I spend more words worrying about Mailvelope and Google E2E users. They face the same issues.

</comment></comment><comment><USER7> Looks like a fine start. Compressing chunks together seems pretty dangerous; the ZIP file part is only safe if it doesn't compress.

<comment><AUTHOR> There have been some security flaws related to compressing data before encrypting it - I don't know if those apply here. But if they do, well, compression is actually optional in the ZIP standard. This would need to be checked carefully if this moves beyond the discussion stage.

The purpose of using an archive was primarily to have a standard container for transmitting filenames and metadata, which recipients won't need to install extra tools to work with.

<comment><USER8> If you use ZIP, some implementations will compress, and you'll get security vulnerabilities. And even if your implementation rejects compressed ZIP files, other implementations won't. Better to use a container that doesn't support compression and never will; for instance, tar.

PGP/GPG has compression support built-in, for that matter; I wonder if anyone has looked for vulnerabilities like CRIME or BREACH in it?

<comment><AUTHOR> I suggested ZIP simply because it's so widely supported, it's built into the stdlibs of many higher level languages and most operating systems have GUI tools to browse the contents. I don't know of any other containers that are as widely supported, but I am all ears.

If it's a container that requires the recipient install 3rd party tools, then we may as well skip that part of the proposal entirely.

</comment></comment></comment></comment><comment><USER9> Meta comment: the website is mostly unreadable with JS disabled because the fixed-position head is covering most of the page, but fine when removing the "position: fixed" from the "navigation" class. (And your target audience will generally be one that prefers to have JS off, I'd think.)

<comment><AUTHOR> Thanks, I'll file this as an issue on our site's tracker.

<comment><USER10> Another issue I noticed: In the FAQ the line spacing is way too small.

Firefox, Windows 8.

[http://imgur.com/x8zIXFT](http://imgur.com/x8zIXFT)

<comment><AUTHOR> Thanks! That one already had an issue filed: [https://github.com/mailpile/www.mailpile.is/issues/9](https://github.com/mailpile/www.mailpile.is/issues/9)

;-)

</comment></comment></comment></comment><comment><USER11> If we could use a forward secure PGP, I think that could be worth breaking PGP backwards compatibility. Isn't the TextSecure protocol kind of that anyway? Why can't MailPile use that with an e-mail interface? It's forward secure, it's asynchronous and you can authenticate users. What more do we need?

<comment><AUTHOR> Mailpile is an e-mail client, first, other things second. Maybe we'll support other protocols, such as TextSecure, at some point. I hope so, actually! :-)

Note that PFS generally applies to a communication channel, not the way a message is encrypted and signed for long-term storage. TextSecure decrypts messages and stores them using a different algorithm locally - there's no forward secrecy if someone steals your local TextSecure message store.

Sadly, the only such separation we have in the world of e-mail (at the moment) is the TLS wrapper around SMTP/IMAP/POP3, which can and should use PFS ciphers. But that doesn't help if the server itself is malicious. That's one of the reasons I'm keen to try and cut out the middle man when possible, and deliver "directly" from client to client. See our SMTorP discussion for one idea of how we might eventually do that: [https://github.com/mailpile/Mailpile/wiki/SMTorP](https://github.com/mailpile/Mailpile/wiki/SMTorP)</comment></comment>


Indoor Positioning System Tracks Your Phone Using Sound Waves - <AUTHOR> http://motherboard.vice.com/read/guoguo-is-a-new-indoor-smart-phone-localization-acoustic-gps <comment><USER2> Interesting idea - beacons provide 3d tracking. There used to be a product that tracked a 3d pen using three long microphone tubes in a Cartesian arrangement. The pen sparked, the microphones received the 'tick', and the relative timing determined the pen position. My brother used it back in the 90's to measure finger position on cadavers in a 'hand research' lab (research used to create more useful artificial limbs).

This article gave me a notion: how about a 'sonar' app for your phone? Transmits tics and times their return, maybe plays a tone to give you an idea of distance to obstacle. For the blind?

<comment><USER3> Interesting idea! A quick google search shows there is at least one Android app, but they have the trouble of supporting multiple Android devices (some of which don't have the necessary hardware).

My assumption would be that potential users need an app that would work 99% of the time, but this might be a very interesting side project none the less.

<comment><USER4> Yeah this app has been around forever, iirc I used it on the original G1! (Or N1? It was a long time ago.) One issue they have is that for optimal accuracy, you need raw access to the multiple microphones but the manufacturer uses the mics to perform audio scrubbing before the data gets to the app.

<comment><USER5> Can you point to app, please?

<comment><USER4> [https://play.google.com/store/apps/details?id=com.dicon.sona...](https://play.google.com/store/apps/details?id=com.dicon.sonar.ad)

Last updated in 2012...

</comment></comment></comment></comment></comment><comment><USER6> There are several companies trying to do indoor gps with blue tooth low energy beacons.

[http://indoo.rs/](http://indoo.rs/) and yc company [http://estimote.com/](http://estimote.com/) also see Apple's iBeacon.

i have never been able to get these devices to work well enough for gps type apps. so glad to see someone is trying a new approach.

<comment><USER7> We are also, [https://flair.co](https://flair.co), but more room occupancy than navigation. Navigation and trilateration with any accuracy is incredibly difficult.

<comment><USER8> I'd be curious to hear what the challenges are. Is it simply that signal strength is too noisy to be used reliably? (I.e., interference, multipath, attenuation when the subject walks behind furniture, etc.)

I was attending a theatre show a few days ago, and thought that the sound system could deliver a better experience if the positions of mics on stage could be tracked as cast moves around, and the mixing adjusted accordingly. Naturally, BLE was the first thing that came to mind.

<comment><USER7> There are a lot of things. The device's antenna pattern and orientation are a big part. Metal in/around any of the devices. Signal/Noise. etc. You can try to improve with a lot of calibration but it will also be subject to noise from every audience members bluetooth radio too.

</comment></comment></comment></comment><comment><USER9> Wasn't this in the Batman movie?

</comment><comment><USER10> This has existed for at least a decade [http://cricket.csail.mit.edu/](http://cricket.csail.mit.edu/)

<comment><USER7> And here [http://www.mccormick.northwestern.edu/news/articles/archive/...](http://www.mccormick.northwestern.edu/news/articles/archive/2009-2012/article_935.html)</comment></comment>


How to build a DIY web scraper in any language - <AUTHOR> https://levelup.gitconnected.com/how-to-build-a-diy-web-scraper-in-any-language-1104ac0713cd <comment><AUTHOR> I've been told I should've shared the friend link to remove the paywall (my bad), here it is: [https://levelup.gitconnected.com/how-to-build-a-diy-web- scra...](https://levelup.gitconnected.com/how-to-build-a-diy-web-scraper-in- any- language-1104ac0713cd?source=friends_link&sk=2f8c74855d114d460f93e0070e6553ca)

</comment><comment><USER2> Make crawlers not scrapers.

<comment><USER3> Make open APIs, not scrapers.

<comment><USER4> The problem is that most companies/developers still aren't "Open API first"... So, people must build scrapers.</comment></comment></comment>


Ask HN: Wanted: Elementary but surprising group theory result ... - <AUTHOR>

Recently I was asked for an example from group theory to show its utility. Basically, I'm looking for something slightly interesting or slightly unexpected or rather useful that is otherwise not obvious, but falls out of some elementary group theory.
My first two responses were Lagrange's Theorem, that the size of a subgroup divides the size of the whole group (so the number of rotations of a cube divides the number of permutations of six objects), and the fact that a group of even order has a non-trivial element whose square is the identity.
But I was hoping for something a bit deeper than either of these.
Any suggestions?
Thanks.
PS: I know this is not about hackers or entrepreneurs or startups, but I also know people here like a bit of math.  Sometimes. <comment><USER2> How about the result concerning finding the roots of polynomials; that there are increasingly complex exact and explicit formulae as you go from linear to quartic, but it's impossible to find such formulae for quintics or higher.

I don't know if that fits your needs: it's elementary to state, but not exactly elementary to prove.

<comment><AUTHOR> Lovely result, but as you expect, not really sufficiently elementary. Also, while mathematicians find impossibility proofs among the most useful results, non-mathematicians are usually more impressed by results that show something unexpected is possible.

But I'll keep it in mind.

</comment></comment><comment><USER3> Something slightly interesting might be the Rubik's cube group. By considering the Rubik's cube a group under the rotation operation, then we can essentially completely solve the Rubik's cube puzzle.

If you are familiar with the Quaternions, take a look at the 3D rotation group SO(3). This group apparently has applications in computing where one needs a way to perform rotations in three dimensions. According to the Wikipedia article, [https://secure.wikimedia.org/wikipedia/en/wiki/Quaternions_a...](https://secure.wikimedia.org/wikipedia/en/wiki/Quaternions_and_spatial_rotation), this method of representation has certain important advantages over other methods, including being numerically stable and avoiding a problem called gimbal lock, where rotation about one of the possible axes is no longer possible.

<comment><AUTHOR> Well, the idea of using commutators in the Rubik cube is what leads to a nice, elementary method of solving it, but it's not really a theorem or result from group theory that's telling us something we didn't already know. Plus I suspect many people would regard this as just a toy, a form of "play for play's sake" and hence still irrelevant.

The fact that the quaternions form a field (hence two kinds of group, one of which is interesting) where the multiplication doesn't commute, and then that can be used to model rotations is again interesting, and can be asserted to be useful (and it is) but it's still not really what I'm looking for.

But keep the ideas coming. There's got to be a good one out there somewhere - isn't there?

</comment></comment><comment><USER4> I'd submit Euler's theorem, <http://en.wikipedia.org/wiki/Euler%27s_theorem>. This result is the heart of the Rabin-Miller primality test, which is what makes finding large (probable) primes easy, making RSA encryption practical.

<comment><AUTHOR> Hmm. I do use that result in the context of number theory, and I suppose I could bend it to use it as an example of groups in abstract being used in practice.

I'll have to think about that, but it does show promise. The question is, how can I present it as a result that's specifically from group theory being used in number theory? Does that make sense? I'd like to be able to demonstrate something that's specifically group theory, and I'm finding it hard to extract the abstract, in this case.

But it shows promise.

</comment></comment><comment><USER5> What about (not) Burnside's lemma:

If $G$ acts on a set $X$ and $|X^g|$ denotes the number of elements of $X$ fixed by $g$, then the number of orbits $|X/G|$ is given by

$|X/G| = \frac {1} {|G|} \sum_{g \in G} |X^g|.$

The proof is simple, yet you can count an amazing number of things with it.

<comment><AUTHOR> Ah, now that's more interesting. I'm not sure I can make that readily accessible, but if I consider a specific example (yet to be invented) then maybe I can make it "obviously useful" while still being an abstract result.

I'll have to think about that. Boffo. Good one. Thanks.</comment></comment>


ISO 8601 - <AUTHOR> http://en.wikipedia.org/wiki/ISO_8601

<comment><USER2> That (to me then unknown) fact that the anglo-american world counts the weeks of the year differently from Europe has once required a very expensive upgrade to one of my web-apps and a lot of very unhappy clients.

In most software the default is the anglo-american count, you need to explicitly ask for the ISO 8601 count.</comment>


4 ways to stop wasting your users time - <AUTHOR> http://vincentjordan.com/2015/01/4-ways-to-stop-wasting-your-users-time/ <comment><USER2> You can also use Yaware time tracking software, that will help to understand what activities are most time consuming. [http://yaware.com/](http://yaware.com/)</comment>


Starting an IT Blog - <AUTHOR> I would like to have an IT blog, but I don't know what to start with. Would you advice please how to make your blog more popular? <comment><USER2> If you're serious, start by writing headline for at about 50 posts you would write.

One you do that, I think you'll have a good idea if you want to continue, what you're blog is really about, and who your audience might be.

If you're going to be casual about it, just start posting.

</comment><comment><USER3> 1) Wordpress.com, medium.com, tumblr.com, posthaven.com

2) Write good content.

</comment><comment><USER4> There are some services helping to improve writing tips like [http://buy- researchpapers.net](http://buy-researchpapers.net). Try it!</comment>