Noam Chomsky v. IBM’s Watson Computer
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It seems less like lack of interest than anger at what he sees as a misguided approach. I think he thinks that Watson and Deep Blue are "brute force" solutions (hence the steamroller metaphor), while his work on the relation of computation and grammar (and the vision guy he mentioned) actually get at the core of intelligence.
He may think there is a small elegant machine in the brain that understands grammar, and which is of course computational. He may be right but Watson is still interesting for any reasonably curious person. It's not obvious how much brute force you need to play Jeopardy.
Given that, assuming that it implements some small and elegant algorithm is a very strange assumption to make.
Brute force is a good approach given our current technology and understanding, but it isn't necessarily similar to the brain.
I don't think anyone thinks they're meant to be similar. It's not neurobiology, it's computer science.
The steamroller metaphor is intended to illustrate the brute force aspect of the approach. Google + deep blue burns many many orders of magnitude more calories than a human.
Yes, Watson is brute force compared to a human brain. But Watson is still fundamentally different than the brute-force approach of traversing decision trees of a well-defined rule set.
-- Arthur C. Clarke
Watson isn't the end, it's a building block. It's true that it's been largely hyped to some degree, but I think that opens doors that wouldn't have been opened otherwise. Chomsky's dismissal seems too quick.
He indicates that his inclination is toward a different research approach which is not brute force oriented (meaning it's more algorithmically/conceptually sophisticated but not fundamentally different).
This is why he trivializes the notion of "intelligence"... b/c he probably has a hard time calling the approach he favors intelligence, much less a far less intelligent brute force approach.
In Chomsky's world, intelligence essentially refers to "innate knowlege systems". If it's brute force than it's totally non-innate.
http://www.google.com/search?tbo=p&tbm=bks&q=turing+...
A quick email conversation or off-the-cuff interview is too brief to convey the context. Most of the dismissive comments here don't seem to engage that context in a way that makes me confident that they're informed by it.
Watson did not get it. it was finding sensible answers, but answers that did not fit the category. It got a couple wrong, then stopped buzzing on that category. After it saw the correct answers from the other contestants for a few, it started buzzing again, with the correct answers.
Even if you were right, it'd just be another example of "do submarines swim?". The difference is meaningless.
Let's go with your example. You don't touch things that cause pain. And you think that hot stoves, fire, etc cause pain. But what if people show you the trick to walking over hot coals? Then you are able to touch an object that you previously would not have. Not because your list of items that cause pain has changed, but because you developed a deeper understanding of WHY they caused pain, and adjust your algorithm to take that into account. You now are thinking about HOW you are touching an object, and not just about the object itself.
To me, this is a clear, meaningful difference. We're not talking about semantics, we're talking about a fundamental adjustment to the questions and processes you go through before making a decision.
Lets just imagine that we're using NN, and of course ML is large topic, not limited to NN, but this is sufficient for now. Adding new data points changes the weights on the neural network -- or can even add new inputs or hidden layers.
I don't actually know what the hot coals trick is, but lets say that it is walking over them at the right rate -- this info gets added to the inputs, with no burning as the output (of course its probably not a step function).
Basically you've now increased what you know... burning is a function of temperature and duration.
No change to the fundamental algorithm.
To be clear... adding a new data point doesn't just change how you react to that one piece of stimulus, but depending on what context is provided, it can change all the weights and structure of the NN. Effectively, change your world view.
Which isn't to say an interpreter is "intelligent" but the criteria of whether you are "using the same algorithm" seems insufficient.
However, Kurzweil has written about it elsewhere. I don't know enough about Chomsky to know if he's expressed his opinions on AI in more depth, but he seems to be suggesting AI can never really "think".
It's 28 pages, not the 8 pages claimed by Chomsky, but it's quite readable.
A generally intelligent AI will not be passive, it will demand to be recognized for what it is and perhaps as an independent creature with rights. We would be wise not to try to enslave these creatures.
As Eliza demonstrates, simply doing something that seems "smart" is often nearly trivial.
You don't have to build a bird to build a 747. And you don't have to replicate human cognition, reason, experience, culture, etc. to build a worthwhile artificial intelligence.
I think this is the greatest error that many people make in approaching AI. AI need not be complete, nor need it be like us, it merely needs to be functional and useful. It may well be that an automated agent that can do limited research for you in a manner similar in scope to Watson may not even remotely "understand" in a human sense the knowledge it is exploring, but perhaps it doesn't need to. Anymore than a 747 needs to understand how to fly.
What an immature response. You're the one acting like a twelve year-old here. Unless you've read a good portion of his writings on language and cognition published through the last half century, you might not want to dismiss his life's work based on an off-the-cuff response to a random email prodding him for sound bites.
There are solid reasons to believe that purely empiricist approaches to learning have fundamental limitations that seem incompatible with what we know about human cognition. Those reasons (e.g. the poverty of the stimulus argument) may be less convincing now than they were in the 60s but they are still not easily dismissable. Keep in mind that Chomsky's goal is not to build useful software but to uncover the roots and mechanisms of human language and cognition.