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Machine Understanding
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October 24, 2011
Stanford AI Class thoughts, and a brief tour of AI history
by William P. Meyers"people who face a difficult question often answer an easier one instead, without realizing it"
— Daniel Kahneman
Despite time management issues (which will only get worse this week) I managed to struggle through the first two weeks, or four units, of the online Stanford introduction to artificial intelligence course.
In the past I had already tried to struggle through Judea Pearl's Probabilistic Reasoning in Intelligent Systems. That was published well over 20 years ago, and yet this course uses many of the same examples. The course is much more about working out actual examples; it is practical, not so theoretical. We've covered Bayes networks and conditional probability, both concepts I had already learned because Numenta was using them. Pearl's book contains a lot of material about wrong directions to take; the Stanford course seems to be focussed on what actually works, at least for Google.
My impression is still that the Stanford AI paradigm, while very practical, is not going to provide the core methods for truly intelligent machines, which I characterize as machine understanding. I think this largely because I am ancient and have watched quite a few AI paradigms come and go over the decades.
When AI got started, let's say in the 1950's, there was an obsession with logic as the highest form of human intelligence (often equated with reasoning). That computers operated with logic circuits seemed a natural match. Math guys who were good at logic tended to deride other human activities as less difficult and requiring less intelligence. Certain problems, including games with limited event spaces (like checkers), could be solved more rapidly by computers (once a human had written an appropriate program) than by humans. By the sixties, at latest by the seventies, computers running AI programs would be smarter than humans. In retrospect, this was idiotic, but the brightest minds of those times believed it.
One paradigm that showed some utility was expert systems. To create one of these, experts (typical example: a doctor making a diagnosis) were consulted to find out how they made decisions. Then a flow chart was created to allow a computer program to make a decision in a similar manner. As a result a computer might appear intelligent, especially if provided by the then more difficult trick of an audio imitation voice output, but today no one would call such a system intelligent. That is no more intelligent than the early punch card sorters that once served as input and output for computers that ran on vacuum tubes.
In the 1980's there was a big push in artificial neural networks. This actually was a major step towards machines being able to imitate human brain functions. It is not a defunct field. Some practical devices today work with technologies developed in that era. But scaling, the problems grew faster than the solutions. No one could build an artificial human brain out of the old artificial neural networks. We know that if we can exactly model a human brain, down to the molecular (maybe even atomic) level, we should get true artificial intelligence. But simplistic systems of neurons and synapses are not easy to assemble into a funcioning human brain analog.
The Stanford model for AI has been widely applied to real world problems, with considerable success. This probabilistic model allows it to deal with more complex data than the old logic and expert system paradigms ever could. Machine systems really can learn new things about their environment and store and organize that information in a way that allows for practical decision making. Clearly that is one thing human brains can do, and it is a lot more difficult than playing in a set-piece world like tic-tac-toe or even chess.
Still, sad as the state of human reasoning can be at times, and as slow as we are to learn new lessons, and as proud as we are of our least bouts of creativity, (and as much as we may occasionally ignore the rule against run-on sentences), I think the Stanford model is not, by itself, going to lead to machine understanding. The human brain has a lot of very interesting structures at the gross level and at the synaptic level. Neurologists have not yet deciphered them. Their initial "programming," or hard-wiring is purely the result of human evolution.
When is imitated intelligence real intelligent? When does a machine (or a human, for that matter) understand something, as opposed to merely changing internal memory to reflect the external reality?
Then again, maybe a Stanford model computer/program/input/output system would have done better at the Stanford AI course than I have. I certainly have not been getting all the quizzes and homework problems right on the first try. On the other hand, I think it will be some good long time before a machine can read, say, a book on neurology and carry on an extended intelligent conversation about it.