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Unthinking Machines: Artificial intelligence needs a reboot, say experts

95 points by pldpld 15y ago

zwischenzug 15y ago
I notice Hofstader wasn't there. Maybe we could have a whip round and send them Fluid Analogies?
- alnayyir 15y ago
  >Maybe we could No.
iwwr 15y ago
As an aside, is there progress in a formal definition for the English language? Have other natural languages been formalized yet?
- woodson 15y ago
  The problem lies with the question 'What is the English language?' Is it what we speak? write? what we find on the web? in what social context? Given some constraints, then perhaps yes, you could write a formal grammar and have a parser accepting this language. Maybe my answer doesn't seem helpful, but my point is that language isn't a static, fixed and closed set.
  - gruseom 15y ago
    Is it what we speak? write? what we find on the web? in what social context? Given some constraints, then perhaps yes, you could write a formal grammar and have a parser How about defining it as what the two of us speak while having a beer in San Francisco? Is that constrained enough? If not, what constraints should be added until it becomes possble to write a parser?
- tel 15y ago
  Absolutely not. You might have a chance with something like Lobjan, but I doubt it. This is the heart of why statistical AI stole the show in language research.
- _delirium 15y ago
  Depending on what you mean by formal definition, "broad-coverage parsers" might be want you want to look into. It's the subset of NLP research that aims to be able to take in any sentence in $language, and produce some sort of structural parse of the sentence. Such a parser is essentially a "formal theory" of the language, for some definition of formal. Two of the more widely used parsers are the Stanford Parser and the Link Grammar Parser. Both use a hybrid of symbolic and statistical methods, starting with an underlying structural theory of grammar, and then training the specific parse decisions from a labeled data set.
CurtHagenlocher 15y ago
So, a bunch of symbolic logic vets are bitter that statistical techniques are producing better results than they were ever able to?
- _delirium 15y ago
  I don't see them as opposed to statistical techniques, at least in this interview, just a focus on methods as ends-in-themselves, rather than tools to be used. Plenty of statistical-AI people themselves hold similar views on that subject, and almost every machine-learning or AI conference has a panel or keynote that expresses dismay at what the field looks like (balkanized, too focused on methods rather than problems, too focused on incremental algorithm tweaks). For example, Leslie Pack Kaelbling's AAAI-2010 keynote had the thesis that we need to stop focusing on incremental algorithm improvements in tiny sub-areas of AI, tested on implausibly pure toy benchmark problems with a focus on optimality proofs, and start focusing more on putting things together into integrated systems that handle real-world problems, which will probably require more hybrid methods (like statistical-AI + logical-AI). Though she expressed it a bit more diplomatically, with the tone that we're not fulfilling the potential of the field with the current approach, and has actually done some concrete work following her own proposal. (Slides: http://people.csail.mit.edu/lpk/AAAI10LPK.pdf http://people.csail.mit.edu/lpk/AAAI10LPK.pdf)
  - jimbokun 15y ago
    One thing I find disconcerting, are more and more complex models that "work" in the sense of predicting what will happen, but the model is too complex to give humans insight into what is happening. Does that mean that the universe is just inherently too complicated for humans to understand, or do we need models that better lend themselves to explaining the phenomena they predict?
    - _delirium 15y ago
      That's been one disconnect between machine learning and some other applied areas as well. For example, vanilla decision trees aren't really seen as a state-of-the-art ML technique anymore, but they still get a ton of use in medicine, environmental science, and the social sciences, because they're seen as more interpretable, rather than a black-box predictor. There is a bit of work on interpretable ML aiming to bridge that gap, though.
onan_barbarian 15y ago
Patrick Winston, director of the AI Lab, rounded up the usual suspects in this article: early attempts to make money off AI, not getting scads of defense megabucks, and 'balkanization' into well-defined subspecialties such as neural networks or genetic algorithms. He didn't hit on the fundamental problem (to quote (allegedly) Brian Reid from his AI qualifier): "AI is bogus". If only we could resume pouring defense dollars into the money pit of Strong AI; each strong AI researcher could be given a metric butt-ton of money for vaguely defined projects like those pushed by Winston. From the article: "Winston said he believes researchers should instead focus on those things that make humans distinct from other primates, or even what made them distinct from Neanderthals. Once researchers think they have identified the things that make humans unique, he said, they should develop computational models of these properties, implementing them in real systems so they can discover the gaps in their models, and refine them as needed. Winston speculated that the magic ingredient that makes humans unique is our ability to create and understand stories using the faculties that support language: "Once you have stories, you have the kind of creativity that makes the species different to any other." With clear-cut and sensible goals like this, success cannot be far away now, can it?
- sp332 15y ago
  Do you really think Patrick Winston got to be the Director of the MIT AI Lab for 25 years with such handwavy crap? He knows what he's talking about, even if the article writer hasn't got a clue.
- a1k0n 15y ago
  That sounds sorta like the neocortex algorithm stuff Numenta is working on, and has been working on for several years. I don't think they've achieved anything beyond what's possible with more conventional approaches yet.
- jimbokun 15y ago
  "Winston said he believes researchers should instead focus on those things that make humans distinct from other primates, or even what made them distinct from Neanderthals." Seems like an arbitrary focus. There are still a lot of things that non human primates and other animals can do that computers cannot. Why not focus on those things? Many aspects of human intelligence are obviously shared with other animals. Heck, there are probably aspects of the intelligence of some animals that exceed human intelligence. Also, why does he think computers can understand stories if they are incapable of, say, spatial reasoning, computer vision, moving around, manipulating their environment, non-verbal communication, using tools, planning, etc. etc. etc. etc. Even if understanding and telling stories is the ultimate goal, it doesn't necessarily mean that's the best immediate goal, given where we are now.
tansey 15y ago
Overall, I'll say that the general sentiment of this panel is half true. On the one hand, machine learning research has grown to such a large field that the signal to noise ratio has dropped dramatically. Lots of people try to squeak out another ICML or AAAI paper by making an incremental improvement that gets 94% accuracy instead of 92% on some set of benchmark tasks. This phenomenon is true across almost all academic disciplines, however, and is more an indictment of the "publish or perish" environment than anything else. On the other hand, some of the things these (famous) researchers are noting is complete FUD: >The answer is that there was a lot of progress in the 1960s and 1970s. Then something went wrong. Yep, things got hard. People early on thought that the difficulty of picking fruit would increase linearly over time. If they could pick all this low-hanging fruit in such a short span of time then surely in X years we'd be at point Y! Unfortunately, it turned out that the landscape was much steeper and fraught with local optima. As a machine learning researcher, I do try to focus on high-level problems that haven't be tackled before. My startup[1] is an example of that, and the extensions to it that I'm researching are as well. But does it really count as revolutionary from an academic sense? Probably not. The fact is that at this point, everyone has thought of something closely related to whatever you want to work on. Even if you've found an institution that enables you to explore freely, big impacts are really hard to come by these days. And when they do, older academics like the ones on this panel don't want to give credit because it's just another incremental improvement in their eyes. I suppose it's just frustrating to hear these guys sit at a panel and complain that AI researchers need to get to work-- they're AI researchers! Why aren't they doing anything? They have tenure and all the free time in the world. But they don't want to do that. They want to sit back and judge people while pointing to contributions they made forty years ago as proof that they can judge. Being critical of others' work while not producing anything of value is just mean-spirited. Put up or shut up. [1] http://effectcheck.com http://effectcheck.com
- Groxx 15y ago
  >Being critical of others' work while not producing anything of value is just mean-spirited. Put up or shut up. That's awfully close to "it takes one to know one", which is a fallacy at best. I can tell good tea from bad tea, but I've never grown it. Closer to the programming realm, any theoretical computer scientist will lament the huge delay between theory and practice - does this mean they don't have a valid complaint, simply because they aren't fixing everything? Specific to the piece: >Chomsky derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don't try to understand the meaning of that behavior. Chomsky compared such researchers to scientists who might study the dance made by a bee returning to the hive, and who could produce a statistically based simulation of such a dance without attempting to understand why the bee behaved that way. A valid complaint from a researcher, I think, and Chomsky has hardly "not produced anything of value". The first round of AI work in the 60s and 70s was a lot of theoretical work getting, as you mentioned, the low-hanging fruit in a new field. And then it turned out to be harder than the initial extreme success implied, and took more computational power. In more recent years, there have been business reasons to use relatively simple AI models to do fairly basic tasks - predicting your tastes in movies, for example. I think they're complaining about that focus, taking a known and just tweaking it until it works without doing any real foundational work, and they're not claiming that everybody in the field today is lazy. It's a complaint you see in every field that gains attention. The original researchers see a decreased focus on research, and little progress for how many people are in the field, and lament for the good old days when people thought instead of made money. A rose-tinted view, to be sure, but the viewers aren't wholly value-less for doing so.
jallmann 15y ago
"AI" nowadays is a mess. I could write a book on why I feel this way, but a lot of it has to do with the prevalence of narrowly defined, domain-specific algorithms that need to be heavily tuned to fit your usage parameters. Even then, you can't always be sure they'll work well. AI is not an easy problem, otherwise we'd have made more progress by now. And unfortunately, barring a major breakthrough, there won't ever be a "one size fits all" approach to AI (or at least a less fractured algorithm landscape). It's all pretty disillusioning, especially if you started out as a bushy-tailed CS undergrad with visions of a grand unified theory of artificial intelligence.
hooande 15y ago
Is anyone on hn working on technology that is similar to a human (or even a rat) in it's ability to learn and form hypotheses? I've only known one or two people who actually tried it, and it usually didn't last long. Personally I feel that most of the benefits that come from "strong ai" can be duplicated with basic statistical analysis. In the spirit of Peter Norvig's "more data beats better algorithms", I think we might all be better served by making an effort to gather and structure as much data about the world as possible. It's not as sexy as creating an artificial sentient being, but over time I think the results would be similar.
- lincolnq 15y ago
  Two from the last couple years: http://en.wikipedia.org/wiki/Adam_%28robot%29 http://en.wikipedia.org/wiki/Adam_%28robot%29 http://creativemachines.cornell.edu/eureqa http://creativemachines.cornell.edu/eureqa The Singularity Institute is working on making such a thing safe: http://singinst.org http://singinst.org
fleitz 15y ago
Pure AI doesn't need a reboot, they just need to start solving practical problems, if you look at what Google does it's essentially AI. The problem with AI as such is that it over promises and under delivers while there is tremendous benefit possible to society with the existing research that has been conducted. The essential problem are there are few vertically integrated companies that can turn AI R&D into commercially successful products which can further fuel more AI R&D. AI needs to move out of subsidized R&D and into productization similar to how Bell labs worked. I actually think this is a much bigger problem that extends to most sciences. There is a lot of scientific research out there that is being poorly monetized.
- Maro 15y ago
  According to the panelists, what Google does (statistical inference) is not pure AI.
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stralep 15y ago
Winston speculated that the magic ingredient that makes humans unique is our ability to create and understand stories using the faculties that support language: "Once you have stories, you have the kind of creativity that makes the species different to any other." Any idea to where is this coming from? Any related articles?
davidhollander 15y ago
>Winston said he believes researchers should instead focus on those things that make humans distinct from other primates, or even what made them distinct from Neanderthals. According to cognitive science, this is capacity for analogical reasoning. Compared to the other self-aware, social, tool using animals exhibiting emotion such as dolphins, elephants, and the great apes, what sets humans apart is our massive capacity for analogical reasoning that leaves dolphins in a distant second. In other words, humans can not only think in terms of relations, but how relations relate to one another much easier than any other animal.
- lars 15y ago
  And for automatic analogical reasoning, we've had the Structure Mapping Engine [1] since the 80's. I don't think focusing on what makes humans unique is necessarily the way to go, I think a better focus would be on what makes animals in general unique, compared to our computers. Being consciousness and being able to move around in the environment are two things that your average cat is still way better at than any computer. [1] http://en.wikipedia.org/wiki/Structure_mapping_engine http://en.wikipedia.org/wiki/Structure_mapping_engine
  - jimbokun 15y ago
    My cat also has a much better mental model of me and how I will respond to her stimuli than any computer does.
Detrus 15y ago
There was a similar theme two years ago http://www.popsci.com/technology/article/2009-12/scientists-collaborate-rebuild-artificial-intelligence-ground http://www.popsci.com/technology/article/2009-12/scientists-... I think the main problem is computers are still too slow, so it's difficult for individual researchers to experiment. Saw a paper a year ago about deep belief networks on GPUs, seems like the field is not even taking advantage of current hardware. You need several modern GPUs to run the equivalent of a bee brain in reasonable time.
- lars 15y ago
  > I think the main problem is computers are still too slow I don't think this is right. Depending on your neuron model, you'd probably be able to simulate as many neurons as is in the human brain if you threw it against, say, Google's server farm. The brain is modular, so you should be able to parallelize it relatively easily. The reason no one has done that yet is that we don't yet know enough about how to connect those neurons the way they are connected in the brain. (The Blue Brain project aim to have this figured out by 2019 though.)
  - Detrus 15y ago
    Oh yea Google's server farm has just about the guesstimated computational power of a human brain, 20 petaflops. Supercomputers can simulate a rat or bee brain with a few teraflops. I was talking about regular personal computers that individual researches can use to experiment. Supercomputer time is expensive and there are lots of annoying technical problems. They also use a lot of poorly optimized software. I figure another 5 years, a few doublings in GPU speed, new OpenCL/CUDA based neural net software and we'll fit a bee brain in one PC. Memristors are expected by 2015 http://spectrum.ieee.org/robotics/artificial-intelligence/moneta-a-mind-made-from-memristors/0 http://spectrum.ieee.org/robotics/artificial-intelligence/mo... they'll help run such brains in real time. Also these old charts http://www.transhumanist.com/volume1/moravec.htm http://www.transhumanist.com/volume1/moravec.htm comparing brains to computers need to be adjusted because glial cells also participate in signal processing. So we need a few teraflops of padding.
karolisd 15y ago
To create anything resembling human intelligence, you'd have to create it the way human intelligence was created. Imagine you are God and you have the tools to create a universe and it's laws. And you want humans, but you can't just make them. The universe you create will need to have the right laws of physics such that a self-replicating molecule (essentially a program, right?) will arise. And that the molecules can mix with each other and new combinations can arise. And you need molecules that can form bubbles so that cells can happen. And you need a bunch of elements for all sorts of things. Then the world needs to apply the right selective forces for the whole evolutionary journey to happen. And if you do it right, you'll end up with human like intelligence, an intelligence motivated by something, survival and reproduction.
6ren 15y ago
> Chomsky derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don't try to understand the meaning of that behavior [He's not deriding these, but] statistical methods can be used to infer models: you have a series of models, and you measure how well each one models the data, and you include a measure of the complexity of the model (e.g. the choices (information) needed to specify that model). The model requiring the least information wins (related to Occam's Razor).
- woodson 15y ago
  Yeah, but a relevant measure of complexity is not that easy to come by. And by what measure do you judge which is more 'valid'? For example, AIC, BIC etc. may have a strong effect on which model 'wins'. You are right of course, but I'm wary of making claims based on inferences from model comparisons without stating such limitations.
  - 6ren 15y ago
    Yes, measure of model complexity is a problem. A solution is to use a program for a Turing machine as the model. The length of the program represents the complexity (which measures the choices that form the program). There's different formulations of a turing machine (different languages for programming one), but since any turing machine can be simulated by any other turing machine with a program, the length of that program becomes the error (that represents the difference in complexity introduced by your selection of some specific Turing machine). If you are comparing models using the same turing machine, then this constant difference doesn't matter. a-b = (a+x) - (b+x) One practical solution is that more data swamps minor differences between the specification language used to describe models. More data is better.
brendano 15y ago
Everyone on the panel is quite senior -- the comments have the flavor of "darn young researchers these days." It might be interesting to hear from AI researchers under 40 about this. Second, on the question of statistics and language; there's an excellent Fernando Pereira essay which addresses, among other things, Chomsky's old opposition to statistical theories: http://www.cis.upenn.edu/~pereira/papers/rsoc.pdf http://www.cis.upenn.edu/~pereira/papers/rsoc.pdf
- _delirium 15y ago
  That's an excellent paper; thanks for the pointer. I think it correctly notices that a lot of both sides have been talking past each other lately, because the old 50s/60s oppositions aren't quite there anymore. In some ways, Chomsky's critique is obsolete, but that's in part precisely because Chomskyian ideas have now been taken up in the statistical community, in ideas like grammar induction. ML is now willing to study grammar models higher up the Chomsky hierarchy than the simple Markov-chain models they investigated in the 50s, which does owe something to Chomsky's studies in formal grammar (like the existence of the Chomsky hierarchy), even if Chomsky himself has been slow to notice that they have indeed taken up some of his ideas.
dmfdmf 15y ago
AI is an epistemological problem. What stalled AI is the lack of a comprehensive theory of concepts and theory of induction. All the traits cited in the article that distinguish us from the animals are derivatives of reason. Whenever these supposed intellectuals get around to realizing this fact they can all eat crow and thank Ayn Rand for solving the problem of concepts and leaving significant clues to the solution to the problem of induction.
- moultano 15y ago
  >thank Ayn Rand for solving the problem of concepts and leaving significant clues to the solution to the problem of induction. Please explain.
  - dmfdmf 15y ago
    There are two fundamental epistemological issues with respect to reason. i) how do we form concepts and what (in reality) do they refer to and ii) how do we reason from specific observations to general conclusions and validate our conclusions (i.e. not deduction but induction). Both of these problems were known (and unsolved) by the Greeks. Ayn Rand (in my judgment) solved the first problem, the problem of universals. How do we form concepts (classes) and how are they connected to the actual things subsumed by the concept. Her answer was that concepts are formed on the principle of measurement omission, which means the objects that form a class posses some trait(s) which is present but unspecified. This is what she called the "some but any" principle. She said this is the same principle behind algebra in that a variable "x" represents some quantity but may have any quantity but remains unspecified. She explains all of it in her "Introduction to Objectivist Epistemology" As far as induction, she never tackled that but she did say that she thought the basic principles of induction are probably buried in higher (calculus or above) mathematics. She was being tutored in mathematics at the time of her death, hunting for the principle of induction. In my view the principles of induction can be found in the work of E.T. Jaynes on (Bayesian) probabililty theory and the logic of science. My own view is that AI is stuck because we don't yet have the basic principles of how reason works, so we can't program a computer to do it. N.B. Most people know of Ayn Rand because of her advocacy of selfishness in ethics and capitalism in politics and economics. What they don't realize is that her views in these higher level branches follow from her deep thinking in the more fundamental levels of epistemology and metaphysics.
forensic 15y ago
If the status quo is a problem at all, then it's a problem with all of modern academia. The problem will only be solved by better ways of selecting and supporting academics. Fix how stuff is funded and you fix the issue.
indrax 15y ago
"Read the Sequences."
- keefe 15y ago
  so many words... this is probably a better place to start http://singinst.org/singularityfaq http://singinst.org/singularityfaq
knowtheory 15y ago
You can tell this article was written by someone who doesn't follow artificial intelligence and neural networks. How? Because people in the field of neural networks and AI would never claim that Minsky "pioneered neural networks". To the contrary (and as Minsky's wikipedia article – i'm sure the source of this claim – obliquely notes), Minsky's pessimism about the abilities of neural network computing lead to the abandonment of artificial neural networks as a major research topic. That alone should make one skeptical about this author's depth of knowledge about artificial intelligence. Beyond that, this article and the quotes therein, are just flat out incorrect. There are people who are attempting to analyze behavior, model it, and build systems that mimic this behavior. They're called cognitive scientists. This approach is taken by linguists, psychologists and philosophers all. But this stuff is incredibly difficult to analyze, let alone model correctly. It annoys me to hear the opinions of the panelists reduced to "oh gee, why isn't anyone doing more holistic research". When i read the actual quotes by Minsky, Partee and Chomsky, i hear the three things i expected to hear, and that each academic has been saying for years. 1) Chomsky, an old school linguist, doesn't like systems that we can't introspect and verify as correctly modeling human behavior. 2) Partee, who is responsible for recognizing the power and importance of Montague Semantics and linguistic pragmatics, states that AI requires world/state modeling that is equivalent in complexity to that required for robust natural language processing (a position i agree with) 3) Minsky thinks nobody is trying hard enough, and that the constraints put on researchers from actual implementation has lead us down a blind alley. Lastly, Sydney Brenner complains that neuroscientists can't see the forest for the trees. I guess he's not familiar with all the research in cognitive psychology, trying to model cognitive facilities like memory, language use, decision making, attention switching and more. That we haven't "solved" AI or made thinking machines is a misleading claim that is contrary to all of the awesome stuff that humans have built in the past 10 years. Look at all of the stuff that Google has built and tell me that we don't have thinking machines that can understand (or if you'd like to be more circumspect, predict) what we want. Tell me that Watson wasn't a marvel of not just engineering but modeling intelligence. The major editorial thrust of this article is an incorrect platitude, which isn't supported by reality or the assertions and claims made by the panelists (whom i each respect for the work they have contributed to the broader field of cognitive science), and it annoys me that this claptrap pastiche is being passed on as journalism. We have made progress, and we will continue to make progress.
rsaarelm 15y ago
Not sure just how important the humans being different from other animals thing is. We've got a billion years of evolution of multicellular life, working up to the brain of the not-quite-human primates, and something like two million years in which humans developed their unique traits. Wouldn't be my first guess that all the heavy lifting happened in the last couple million years instead of the other 998 million.
- lincolnq 15y ago
  Here, read this: http://yudkowsky.net/singularity/power http://yudkowsky.net/singularity/power I agree with you for the most part. But somehow, what makes us different from apes was some kind of keystone. Whatever evolved in the last two million years in our brains made a HUGE difference in our effectiveness. Perhaps if we understand it we'll understand something fundamental about intelligence.
  - rsaarelm 15y ago
    My take on that is that mammalian intelligence was already a massively powerful thing, and just needed to stumble into some sort of trick to make it more, I don't know, recursive? to start the emerging human dominance. I'd bet money that going from a genuinely wolf-level AI we understand from first principles to a human-level AI is much easier than going from AIBO to the level of an actual wolf. The problem with intuiting about this is that assessing animal intelligence is a lot harder for us than assessing human intelligence. It's Moravec's paradox all over again, the thing that seems so simple to us we used to think of it as an absence of skill rather than a skill, behaving like an animal, might be the hardest thing in making an AI.
empiricus 15y ago
In a couple of years we will start seeing a lot of computer vision applications - and robots will be one of them. This is because the computing power has just got at the needed level. Once you have vision you can really start applying the other old AI stuff like planning, etc. Of course this is not Artificial General Intelligence, but it is a step forward and it will greatly improve the visibility of current AI.
olalonde 15y ago
Weird, I submitted the same article yesterday and got only 5 up votes: http://news.ycombinator.com/item?id=2525463 http://news.ycombinator.com/item?id=2525463.
- ColinWright 15y ago
  Getting an item noticed on HN is now largely pot luck. It is correlated with quality, but it's also correlated with time of day, day of the week, number of current readers, and it's still got a huge component of luck. If you don't get four or five upvotes in the first 30 to 90 minutes, it sinks without trace. In part this is because of the huge volume. In part the volume is because the focus is as wide as it always was, but the audience has increased enormously. Many potential solutions have been discussed, but in general, no one has produced a convincing analysis accompanied by concrete solutions that will clearly work. Currently it's a case of - live with it. http://news.ycombinator.com/item?id=2022547 http://news.ycombinator.com/item?id=2022547

95 points by pldpld 15y ago

zwischenzug 15y ago

I notice Hofstader wasn't there. Maybe we could have a whip round and send them Fluid Analogies?

alnayyir 15y ago
>Maybe we could No.

iwwr 15y ago

As an aside, is there progress in a formal definition for the English language? Have other natural languages been formalized yet?

woodson 15y ago
The problem lies with the question 'What is the English language?' Is it what we speak? write? what we find on the web? in what social context? Given some constraints, then perhaps yes, you could write a formal grammar and have a parser accepting this language. Maybe my answer doesn't seem helpful, but my point is that language isn't a static, fixed and closed set.
- gruseom 15y ago
  Is it what we speak? write? what we find on the web? in what social context? Given some constraints, then perhaps yes, you could write a formal grammar and have a parser How about defining it as what the two of us speak while having a beer in San Francisco? Is that constrained enough? If not, what constraints should be added until it becomes possble to write a parser?
tel 15y ago
Absolutely not. You might have a chance with something like Lobjan, but I doubt it. This is the heart of why statistical AI stole the show in language research.
_delirium 15y ago
Depending on what you mean by formal definition, "broad-coverage parsers" might be want you want to look into. It's the subset of NLP research that aims to be able to take in any sentence in $language, and produce some sort of structural parse of the sentence. Such a parser is essentially a "formal theory" of the language, for some definition of formal. Two of the more widely used parsers are the Stanford Parser and the Link Grammar Parser. Both use a hybrid of symbolic and statistical methods, starting with an underlying structural theory of grammar, and then training the specific parse decisions from a labeled data set.

CurtHagenlocher 15y ago

So, a bunch of symbolic logic vets are bitter that statistical techniques are producing better results than they were ever able to?

_delirium 15y ago
I don't see them as opposed to statistical techniques, at least in this interview, just a focus on methods as ends-in-themselves, rather than tools to be used. Plenty of statistical-AI people themselves hold similar views on that subject, and almost every machine-learning or AI conference has a panel or keynote that expresses dismay at what the field looks like (balkanized, too focused on methods rather than problems, too focused on incremental algorithm tweaks). For example, Leslie Pack Kaelbling's AAAI-2010 keynote had the thesis that we need to stop focusing on incremental algorithm improvements in tiny sub-areas of AI, tested on implausibly pure toy benchmark problems with a focus on optimality proofs, and start focusing more on putting things together into integrated systems that handle real-world problems, which will probably require more hybrid methods (like statistical-AI + logical-AI). Though she expressed it a bit more diplomatically, with the tone that we're not fulfilling the potential of the field with the current approach, and has actually done some concrete work following her own proposal. (Slides: http://people.csail.mit.edu/lpk/AAAI10LPK.pdf http://people.csail.mit.edu/lpk/AAAI10LPK.pdf)
- jimbokun 15y ago
  One thing I find disconcerting, are more and more complex models that "work" in the sense of predicting what will happen, but the model is too complex to give humans insight into what is happening. Does that mean that the universe is just inherently too complicated for humans to understand, or do we need models that better lend themselves to explaining the phenomena they predict?
  - _delirium 15y ago
    That's been one disconnect between machine learning and some other applied areas as well. For example, vanilla decision trees aren't really seen as a state-of-the-art ML technique anymore, but they still get a ton of use in medicine, environmental science, and the social sciences, because they're seen as more interpretable, rather than a black-box predictor. There is a bit of work on interpretable ML aiming to bridge that gap, though.

onan_barbarian 15y ago

Patrick Winston, director of the AI Lab, rounded up the usual suspects in this article: early attempts to make money off AI, not getting scads of defense megabucks, and 'balkanization' into well-defined subspecialties such as neural networks or genetic algorithms. He didn't hit on the fundamental problem (to quote (allegedly) Brian Reid from his AI qualifier): "AI is bogus". If only we could resume pouring defense dollars into the money pit of Strong AI; each strong AI researcher could be given a metric butt-ton of money for vaguely defined projects like those pushed by Winston. From the article: "Winston said he believes researchers should instead focus on those things that make humans distinct from other primates, or even what made them distinct from Neanderthals. Once researchers think they have identified the things that make humans unique, he said, they should develop computational models of these properties, implementing them in real systems so they can discover the gaps in their models, and refine them as needed. Winston speculated that the magic ingredient that makes humans unique is our ability to create and understand stories using the faculties that support language: "Once you have stories, you have the kind of creativity that makes the species different to any other." With clear-cut and sensible goals like this, success cannot be far away now, can it?

sp332 15y ago
Do you really think Patrick Winston got to be the Director of the MIT AI Lab for 25 years with such handwavy crap? He knows what he's talking about, even if the article writer hasn't got a clue.
a1k0n 15y ago
That sounds sorta like the neocortex algorithm stuff Numenta is working on, and has been working on for several years. I don't think they've achieved anything beyond what's possible with more conventional approaches yet.
jimbokun 15y ago
"Winston said he believes researchers should instead focus on those things that make humans distinct from other primates, or even what made them distinct from Neanderthals." Seems like an arbitrary focus. There are still a lot of things that non human primates and other animals can do that computers cannot. Why not focus on those things? Many aspects of human intelligence are obviously shared with other animals. Heck, there are probably aspects of the intelligence of some animals that exceed human intelligence. Also, why does he think computers can understand stories if they are incapable of, say, spatial reasoning, computer vision, moving around, manipulating their environment, non-verbal communication, using tools, planning, etc. etc. etc. etc. Even if understanding and telling stories is the ultimate goal, it doesn't necessarily mean that's the best immediate goal, given where we are now.

tansey 15y ago

Overall, I'll say that the general sentiment of this panel is half true. On the one hand, machine learning research has grown to such a large field that the signal to noise ratio has dropped dramatically. Lots of people try to squeak out another ICML or AAAI paper by making an incremental improvement that gets 94% accuracy instead of 92% on some set of benchmark tasks. This phenomenon is true across almost all academic disciplines, however, and is more an indictment of the "publish or perish" environment than anything else. On the other hand, some of the things these (famous) researchers are noting is complete FUD: >The answer is that there was a lot of progress in the 1960s and 1970s. Then something went wrong. Yep, things got hard. People early on thought that the difficulty of picking fruit would increase linearly over time. If they could pick all this low-hanging fruit in such a short span of time then surely in X years we'd be at point Y! Unfortunately, it turned out that the landscape was much steeper and fraught with local optima. As a machine learning researcher, I do try to focus on high-level problems that haven't be tackled before. My startup[1] is an example of that, and the extensions to it that I'm researching are as well. But does it really count as revolutionary from an academic sense? Probably not. The fact is that at this point, everyone has thought of something closely related to whatever you want to work on. Even if you've found an institution that enables you to explore freely, big impacts are really hard to come by these days. And when they do, older academics like the ones on this panel don't want to give credit because it's just another incremental improvement in their eyes. I suppose it's just frustrating to hear these guys sit at a panel and complain that AI researchers need to get to work-- they're AI researchers! Why aren't they doing anything? They have tenure and all the free time in the world. But they don't want to do that. They want to sit back and judge people while pointing to contributions they made forty years ago as proof that they can judge. Being critical of others' work while not producing anything of value is just mean-spirited. Put up or shut up. [1] http://effectcheck.com http://effectcheck.com

Groxx 15y ago
>Being critical of others' work while not producing anything of value is just mean-spirited. Put up or shut up. That's awfully close to "it takes one to know one", which is a fallacy at best. I can tell good tea from bad tea, but I've never grown it. Closer to the programming realm, any theoretical computer scientist will lament the huge delay between theory and practice - does this mean they don't have a valid complaint, simply because they aren't fixing everything? Specific to the piece: >Chomsky derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don't try to understand the meaning of that behavior. Chomsky compared such researchers to scientists who might study the dance made by a bee returning to the hive, and who could produce a statistically based simulation of such a dance without attempting to understand why the bee behaved that way. A valid complaint from a researcher, I think, and Chomsky has hardly "not produced anything of value". The first round of AI work in the 60s and 70s was a lot of theoretical work getting, as you mentioned, the low-hanging fruit in a new field. And then it turned out to be harder than the initial extreme success implied, and took more computational power. In more recent years, there have been business reasons to use relatively simple AI models to do fairly basic tasks - predicting your tastes in movies, for example. I think they're complaining about that focus, taking a known and just tweaking it until it works without doing any real foundational work, and they're not claiming that everybody in the field today is lazy. It's a complaint you see in every field that gains attention. The original researchers see a decreased focus on research, and little progress for how many people are in the field, and lament for the good old days when people thought instead of made money. A rose-tinted view, to be sure, but the viewers aren't wholly value-less for doing so.

jallmann 15y ago

"AI" nowadays is a mess. I could write a book on why I feel this way, but a lot of it has to do with the prevalence of narrowly defined, domain-specific algorithms that need to be heavily tuned to fit your usage parameters. Even then, you can't always be sure they'll work well. AI is not an easy problem, otherwise we'd have made more progress by now. And unfortunately, barring a major breakthrough, there won't ever be a "one size fits all" approach to AI (or at least a less fractured algorithm landscape). It's all pretty disillusioning, especially if you started out as a bushy-tailed CS undergrad with visions of a grand unified theory of artificial intelligence.

hooande 15y ago

Is anyone on hn working on technology that is similar to a human (or even a rat) in it's ability to learn and form hypotheses? I've only known one or two people who actually tried it, and it usually didn't last long. Personally I feel that most of the benefits that come from "strong ai" can be duplicated with basic statistical analysis. In the spirit of Peter Norvig's "more data beats better algorithms", I think we might all be better served by making an effort to gather and structure as much data about the world as possible. It's not as sexy as creating an artificial sentient being, but over time I think the results would be similar.

lincolnq 15y ago
Two from the last couple years: http://en.wikipedia.org/wiki/Adam_%28robot%29 http://en.wikipedia.org/wiki/Adam_%28robot%29 http://creativemachines.cornell.edu/eureqa http://creativemachines.cornell.edu/eureqa The Singularity Institute is working on making such a thing safe: http://singinst.org http://singinst.org

fleitz 15y ago

Pure AI doesn't need a reboot, they just need to start solving practical problems, if you look at what Google does it's essentially AI. The problem with AI as such is that it over promises and under delivers while there is tremendous benefit possible to society with the existing research that has been conducted. The essential problem are there are few vertically integrated companies that can turn AI R&D into commercially successful products which can further fuel more AI R&D. AI needs to move out of subsidized R&D and into productization similar to how Bell labs worked. I actually think this is a much bigger problem that extends to most sciences. There is a lot of scientific research out there that is being poorly monetized.

Maro 15y ago
According to the panelists, what Google does (statistical inference) is not pure AI.

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stralep 15y ago

Winston speculated that the magic ingredient that makes humans unique is our ability to create and understand stories using the faculties that support language: "Once you have stories, you have the kind of creativity that makes the species different to any other." Any idea to where is this coming from? Any related articles?

davidhollander 15y ago

>Winston said he believes researchers should instead focus on those things that make humans distinct from other primates, or even what made them distinct from Neanderthals. According to cognitive science, this is capacity for analogical reasoning. Compared to the other self-aware, social, tool using animals exhibiting emotion such as dolphins, elephants, and the great apes, what sets humans apart is our massive capacity for analogical reasoning that leaves dolphins in a distant second. In other words, humans can not only think in terms of relations, but how relations relate to one another much easier than any other animal.

lars 15y ago
And for automatic analogical reasoning, we've had the Structure Mapping Engine [1] since the 80's. I don't think focusing on what makes humans unique is necessarily the way to go, I think a better focus would be on what makes animals in general unique, compared to our computers. Being consciousness and being able to move around in the environment are two things that your average cat is still way better at than any computer. [1] http://en.wikipedia.org/wiki/Structure_mapping_engine http://en.wikipedia.org/wiki/Structure_mapping_engine
- jimbokun 15y ago
  My cat also has a much better mental model of me and how I will respond to her stimuli than any computer does.

Detrus 15y ago

There was a similar theme two years ago http://www.popsci.com/technology/article/2009-12/scientists-collaborate-rebuild-artificial-intelligence-ground http://www.popsci.com/technology/article/2009-12/scientists-... I think the main problem is computers are still too slow, so it's difficult for individual researchers to experiment. Saw a paper a year ago about deep belief networks on GPUs, seems like the field is not even taking advantage of current hardware. You need several modern GPUs to run the equivalent of a bee brain in reasonable time.

lars 15y ago
> I think the main problem is computers are still too slow I don't think this is right. Depending on your neuron model, you'd probably be able to simulate as many neurons as is in the human brain if you threw it against, say, Google's server farm. The brain is modular, so you should be able to parallelize it relatively easily. The reason no one has done that yet is that we don't yet know enough about how to connect those neurons the way they are connected in the brain. (The Blue Brain project aim to have this figured out by 2019 though.)
- Detrus 15y ago
  Oh yea Google's server farm has just about the guesstimated computational power of a human brain, 20 petaflops. Supercomputers can simulate a rat or bee brain with a few teraflops. I was talking about regular personal computers that individual researches can use to experiment. Supercomputer time is expensive and there are lots of annoying technical problems. They also use a lot of poorly optimized software. I figure another 5 years, a few doublings in GPU speed, new OpenCL/CUDA based neural net software and we'll fit a bee brain in one PC. Memristors are expected by 2015 http://spectrum.ieee.org/robotics/artificial-intelligence/moneta-a-mind-made-from-memristors/0 http://spectrum.ieee.org/robotics/artificial-intelligence/mo... they'll help run such brains in real time. Also these old charts http://www.transhumanist.com/volume1/moravec.htm http://www.transhumanist.com/volume1/moravec.htm comparing brains to computers need to be adjusted because glial cells also participate in signal processing. So we need a few teraflops of padding.

karolisd 15y ago

To create anything resembling human intelligence, you'd have to create it the way human intelligence was created. Imagine you are God and you have the tools to create a universe and it's laws. And you want humans, but you can't just make them. The universe you create will need to have the right laws of physics such that a self-replicating molecule (essentially a program, right?) will arise. And that the molecules can mix with each other and new combinations can arise. And you need molecules that can form bubbles so that cells can happen. And you need a bunch of elements for all sorts of things. Then the world needs to apply the right selective forces for the whole evolutionary journey to happen. And if you do it right, you'll end up with human like intelligence, an intelligence motivated by something, survival and reproduction.

6ren 15y ago

> Chomsky derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don't try to understand the meaning of that behavior [He's not deriding these, but] statistical methods can be used to infer models: you have a series of models, and you measure how well each one models the data, and you include a measure of the complexity of the model (e.g. the choices (information) needed to specify that model). The model requiring the least information wins (related to Occam's Razor).

woodson 15y ago
Yeah, but a relevant measure of complexity is not that easy to come by. And by what measure do you judge which is more 'valid'? For example, AIC, BIC etc. may have a strong effect on which model 'wins'. You are right of course, but I'm wary of making claims based on inferences from model comparisons without stating such limitations.
- 6ren 15y ago
  Yes, measure of model complexity is a problem. A solution is to use a program for a Turing machine as the model. The length of the program represents the complexity (which measures the choices that form the program). There's different formulations of a turing machine (different languages for programming one), but since any turing machine can be simulated by any other turing machine with a program, the length of that program becomes the error (that represents the difference in complexity introduced by your selection of some specific Turing machine). If you are comparing models using the same turing machine, then this constant difference doesn't matter. a-b = (a+x) - (b+x) One practical solution is that more data swamps minor differences between the specification language used to describe models. More data is better.

brendano 15y ago

Everyone on the panel is quite senior -- the comments have the flavor of "darn young researchers these days." It might be interesting to hear from AI researchers under 40 about this. Second, on the question of statistics and language; there's an excellent Fernando Pereira essay which addresses, among other things, Chomsky's old opposition to statistical theories: http://www.cis.upenn.edu/~pereira/papers/rsoc.pdf http://www.cis.upenn.edu/~pereira/papers/rsoc.pdf

_delirium 15y ago
That's an excellent paper; thanks for the pointer. I think it correctly notices that a lot of both sides have been talking past each other lately, because the old 50s/60s oppositions aren't quite there anymore. In some ways, Chomsky's critique is obsolete, but that's in part precisely because Chomskyian ideas have now been taken up in the statistical community, in ideas like grammar induction. ML is now willing to study grammar models higher up the Chomsky hierarchy than the simple Markov-chain models they investigated in the 50s, which does owe something to Chomsky's studies in formal grammar (like the existence of the Chomsky hierarchy), even if Chomsky himself has been slow to notice that they have indeed taken up some of his ideas.

dmfdmf 15y ago

AI is an epistemological problem. What stalled AI is the lack of a comprehensive theory of concepts and theory of induction. All the traits cited in the article that distinguish us from the animals are derivatives of reason. Whenever these supposed intellectuals get around to realizing this fact they can all eat crow and thank Ayn Rand for solving the problem of concepts and leaving significant clues to the solution to the problem of induction.

moultano 15y ago
>thank Ayn Rand for solving the problem of concepts and leaving significant clues to the solution to the problem of induction. Please explain.
- dmfdmf 15y ago
  There are two fundamental epistemological issues with respect to reason. i) how do we form concepts and what (in reality) do they refer to and ii) how do we reason from specific observations to general conclusions and validate our conclusions (i.e. not deduction but induction). Both of these problems were known (and unsolved) by the Greeks. Ayn Rand (in my judgment) solved the first problem, the problem of universals. How do we form concepts (classes) and how are they connected to the actual things subsumed by the concept. Her answer was that concepts are formed on the principle of measurement omission, which means the objects that form a class posses some trait(s) which is present but unspecified. This is what she called the "some but any" principle. She said this is the same principle behind algebra in that a variable "x" represents some quantity but may have any quantity but remains unspecified. She explains all of it in her "Introduction to Objectivist Epistemology" As far as induction, she never tackled that but she did say that she thought the basic principles of induction are probably buried in higher (calculus or above) mathematics. She was being tutored in mathematics at the time of her death, hunting for the principle of induction. In my view the principles of induction can be found in the work of E.T. Jaynes on (Bayesian) probabililty theory and the logic of science. My own view is that AI is stuck because we don't yet have the basic principles of how reason works, so we can't program a computer to do it. N.B. Most people know of Ayn Rand because of her advocacy of selfishness in ethics and capitalism in politics and economics. What they don't realize is that her views in these higher level branches follow from her deep thinking in the more fundamental levels of epistemology and metaphysics.

forensic 15y ago

If the status quo is a problem at all, then it's a problem with all of modern academia. The problem will only be solved by better ways of selecting and supporting academics. Fix how stuff is funded and you fix the issue.

indrax 15y ago

"Read the Sequences."

keefe 15y ago
so many words... this is probably a better place to start http://singinst.org/singularityfaq http://singinst.org/singularityfaq

knowtheory 15y ago

You can tell this article was written by someone who doesn't follow artificial intelligence and neural networks. How? Because people in the field of neural networks and AI would never claim that Minsky "pioneered neural networks". To the contrary (and as Minsky's wikipedia article – i'm sure the source of this claim – obliquely notes), Minsky's pessimism about the abilities of neural network computing lead to the abandonment of artificial neural networks as a major research topic. That alone should make one skeptical about this author's depth of knowledge about artificial intelligence. Beyond that, this article and the quotes therein, are just flat out incorrect. There are people who are attempting to analyze behavior, model it, and build systems that mimic this behavior. They're called cognitive scientists. This approach is taken by linguists, psychologists and philosophers all. But this stuff is incredibly difficult to analyze, let alone model correctly. It annoys me to hear the opinions of the panelists reduced to "oh gee, why isn't anyone doing more holistic research". When i read the actual quotes by Minsky, Partee and Chomsky, i hear the three things i expected to hear, and that each academic has been saying for years. 1) Chomsky, an old school linguist, doesn't like systems that we can't introspect and verify as correctly modeling human behavior. 2) Partee, who is responsible for recognizing the power and importance of Montague Semantics and linguistic pragmatics, states that AI requires world/state modeling that is equivalent in complexity to that required for robust natural language processing (a position i agree with) 3) Minsky thinks nobody is trying hard enough, and that the constraints put on researchers from actual implementation has lead us down a blind alley. Lastly, Sydney Brenner complains that neuroscientists can't see the forest for the trees. I guess he's not familiar with all the research in cognitive psychology, trying to model cognitive facilities like memory, language use, decision making, attention switching and more. That we haven't "solved" AI or made thinking machines is a misleading claim that is contrary to all of the awesome stuff that humans have built in the past 10 years. Look at all of the stuff that Google has built and tell me that we don't have thinking machines that can understand (or if you'd like to be more circumspect, predict) what we want. Tell me that Watson wasn't a marvel of not just engineering but modeling intelligence. The major editorial thrust of this article is an incorrect platitude, which isn't supported by reality or the assertions and claims made by the panelists (whom i each respect for the work they have contributed to the broader field of cognitive science), and it annoys me that this claptrap pastiche is being passed on as journalism. We have made progress, and we will continue to make progress.

rsaarelm 15y ago

Not sure just how important the humans being different from other animals thing is. We've got a billion years of evolution of multicellular life, working up to the brain of the not-quite-human primates, and something like two million years in which humans developed their unique traits. Wouldn't be my first guess that all the heavy lifting happened in the last couple million years instead of the other 998 million.

lincolnq 15y ago
Here, read this: http://yudkowsky.net/singularity/power http://yudkowsky.net/singularity/power I agree with you for the most part. But somehow, what makes us different from apes was some kind of keystone. Whatever evolved in the last two million years in our brains made a HUGE difference in our effectiveness. Perhaps if we understand it we'll understand something fundamental about intelligence.
- rsaarelm 15y ago
  My take on that is that mammalian intelligence was already a massively powerful thing, and just needed to stumble into some sort of trick to make it more, I don't know, recursive? to start the emerging human dominance. I'd bet money that going from a genuinely wolf-level AI we understand from first principles to a human-level AI is much easier than going from AIBO to the level of an actual wolf. The problem with intuiting about this is that assessing animal intelligence is a lot harder for us than assessing human intelligence. It's Moravec's paradox all over again, the thing that seems so simple to us we used to think of it as an absence of skill rather than a skill, behaving like an animal, might be the hardest thing in making an AI.

empiricus 15y ago

In a couple of years we will start seeing a lot of computer vision applications - and robots will be one of them. This is because the computing power has just got at the needed level. Once you have vision you can really start applying the other old AI stuff like planning, etc. Of course this is not Artificial General Intelligence, but it is a step forward and it will greatly improve the visibility of current AI.

olalonde 15y ago

Weird, I submitted the same article yesterday and got only 5 up votes: http://news.ycombinator.com/item?id=2525463 http://news.ycombinator.com/item?id=2525463.

ColinWright 15y ago
Getting an item noticed on HN is now largely pot luck. It is correlated with quality, but it's also correlated with time of day, day of the week, number of current readers, and it's still got a huge component of luck. If you don't get four or five upvotes in the first 30 to 90 minutes, it sinks without trace. In part this is because of the huge volume. In part the volume is because the focus is as wide as it always was, but the audience has increased enormously. Many potential solutions have been discussed, but in general, no one has produced a convincing analysis accompanied by concrete solutions that will clearly work. Currently it's a case of - live with it. http://news.ycombinator.com/item?id=2022547 http://news.ycombinator.com/item?id=2022547