Unthinking Machines: Artificial intelligence needs a reboot, say experts

Overall, I'll say that the general sentiment of this panel is half true.<p>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.<p>On the other hand, some of the things these (famous) researchers are noting is complete FUD:<p>&#62;The answer is that there was a lot of progress in the 1960s and 1970s. Then something went wrong.<p>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.<p>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.<p>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.<p>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.<p>Being critical of others' work while not producing anything of value is just mean-spirited. Put up or shut up.<p>[1] <a href="http://effectcheck.com" rel="nofollow">http://effectcheck.com</a>

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.<p>He didn't hit on the fundamental problem (to quote (allegedly) Brian Reid from his AI qualifier): "AI is bogus".<p>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:<p>"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."<p>With clear-cut and sensible goals like this, success cannot be far away now, can it?

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&#38;D into commercially successful products which can further fuel more AI R&#38;D.<p>AI needs to move out of subsidized R&#38;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.

"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.<p>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).<p>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.

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

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.<p>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: <a href="http://www.cis.upenn.edu/~pereira/papers/rsoc.pdf" rel="nofollow">http://www.cis.upenn.edu/~pereira/papers/rsoc.pdf</a>

&#62;<i>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.</i><p>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.<p>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.

There was a similar theme two years ago <a href="http://www.popsci.com/technology/article/2009-12/scientists-collaborate-rebuild-artificial-intelligence-ground" rel="nofollow">http://www.popsci.com/technology/article/2009-12/scientists-...</a><p>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.

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.

To create anything resembling human intelligence, you'd have to create it the way human intelligence was created.<p>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.<p>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.<p>Then the world needs to apply the right selective forces for the whole evolutionary journey to happen.<p>And if you do it right, you'll end up with human like intelligence, an intelligence motivated by something, survival and reproduction.

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.<p>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.

&#62; 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<p>[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).

Weird, I submitted the same article yesterday and got only 5 up votes: <a href="http://news.ycombinator.com/item?id=2525463" rel="nofollow">http://news.ycombinator.com/item?id=2525463</a>.

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.

You can tell this article was written by someone who doesn't follow artificial intelligence and neural networks.<p>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.<p>That alone should make one skeptical about this author's depth of knowledge about artificial intelligence.<p>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.<p>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".<p>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.<p>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.<p>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.<p>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.<p>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.<p>We have made progress, and we will continue to make progress.

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

<i>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."</i><p>Any idea to where is this coming from? Any related articles?

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

"Read the Sequences."

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

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.