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Artificial General Intelligence: Batting the Knowledge Ball toward IBM and Google

If you are interested in “artificial intelligence” or “artificial general intelligence”, you will want to read “Creative Blocks: The Very Laws of Physics Imply That Artificial Intelligence Must Be Possible. What’s Holding Us Up?” Artificial General Intelligence is a discipline that seeks to render in a computing device the human brain.

Dr. Deutsch asserts:

I cannot think of any other significant field of knowledge in which the prevailing wisdom, not only in society at large but also among experts, is so beset with entrenched, overlapping, fundamental errors. Yet it has also been one of the most self-confident fields in prophesying that it will soon achieve the ultimate breakthrough.

Adherents of making a machine’s brain work like a human’s are, says Dr. Deutsch:

split the intellectual world into two camps, one insisting that AGI was none the less impossible, and the other that it was imminent. Both were mistaken. The first, initially predominant, camp cited a plethora of reasons ranging from the supernatural to the incoherent. All shared the basic mistake that they did not understand what computational universality implies about the physical world, and about human brains in particular. But it is the other camp’s basic mistake that is responsible for the lack of progress. It was a failure to recognize that what distinguishes human brains from all other physical systems is qualitatively different from all other functionalities, and cannot be specified in the way that all other attributes of computer programs can be. It cannot be programmed by any of the techniques that suffice for writing any other type of program. Nor can it be achieved merely by improving their performance at tasks that they currently do perform, no matter by how much.

One of the examples Dr. Deutsch invokes is IBM’s game show “winning” computer Watson. He explains:

Nowadays, an accelerating stream of marvelous and useful functionalities for computers are coming into use, some of them sooner than had been foreseen even quite recently. But what is neither marvelous nor useful is the argument that often greets these developments, that they are reaching the frontiers of AGI. An especially severe outbreak of this occurred recently when a search engine called Watson, developed by IBM, defeated the best human player of a word-association database-searching game called Jeopardy. ‘Smartest machine on Earth’, the PBS documentary series Nova called it, and characterized its function as ‘mimicking the human thought process with software.’ But that is precisely what it does not do. The thing is, playing Jeopardy — like every one of the computational functionalities at which we rightly marvel today — is firmly among the functionalities that can be specified in the standard, behaviorist way that I discussed above. No Jeopardy answer will ever be published in a journal of new discoveries. The fact that humans perform that task less well by using creativity to generate the underlying guesses is not a sign that the program has near-human cognitive abilities. The exact opposite is true, for the two methods are utterly different from the ground up.

IBM surfaces again with regard to playing chess, a trick IBM demonstrated years ago:

Likewise, when a computer program beats a grandmaster at chess, the two are not using even remotely similar algorithms. The grandmaster can explain why it seemed worth sacrificing the knight for strategic advantage and can write an exciting book on the subject. The program can only prove that the sacrifice does not force a checkmate, and cannot write a book because it has no clue even what the objective of a chess game is. Programming AGI is not the same sort of problem as programming Jeopardy or chess.

After I read Dr. Deutsch’s essay, I refreshed my memory about Dr. Ray Kurzweil’s view. You can find an interesting essay by this now-Googler in “The Real Reasons We Don’t Have AGI Yet.” The key assertions are:

The real reasons we don’t have AGI yet, I believe, have nothing to do with Popperian philosophy, and everything to do with:

• The weakness of current computer hardware (rapidly being remedied via exponential technological growth!)
• The relatively minimal funding allocated to AGI research (which, I agree with Deutsch, should be distinguished from “narrow AI” research on highly purpose-specific AI systems like IBM’s Jeopardy!-playing AI or Google’s self-driving cars).
• The integration bottleneck: the difficulty of integrating multiple complex components together to make a complex dynamical software system, in cases where the behavior of the integrated system depends sensitively on every one of the components.

Dr. Kurzweil concludes:

The difference between Deutsch’s perspective and my own is not a purely abstract matter; it does have practical consequence. If Deutsch’s perspective is correct, the best way for society to work toward AGI would be to give lots of funding to philosophers of mind. If my view is correct, on the other hand, most AGI funding should go to folks designing and building large-scale integrated AGI systems.

These discussions are going to be quite important in 2014. As search systems do more thinking for the human user, disagreements that appear to be theoretical will have a significant impact on what information is displayed for a user.

Do users know that search results are shaped by algorithms that “think” they are smarter than humans? Good question.

Stephen E Arnold, December 15, 2013

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