Artificial Intelligence Notes
taken mostly from Brian Harvey’s notes
John McCarthy, one of the founders of AI research, has defined the field as "getting
computers to do things which, when done by people, are said to involve
intelligence." The point of this definition is to finesse the whole question of what
"intelligence" means, or of whether computers "can think."
So, for example, the world's chess champion is now a computer program, but the
way the program chooses its moves is, everyone agrees, completely different from
the way human chess players do it. Chess programs use the sheer speed of
computers (much, much faster than neurons firing in the human brain) to overcome
the human's ability, so far not understood or reproducible at all, to understand the
important characteristics of a chess position at a glance.
There have been machines to do specific calculations (e.g., of planetary orbits) for
thousands of years. But people generally date the idea of the programmable
computer, which could do more than one kind of computation, to Charles Babbage's
proposal to build a "difference engine" in 1822. He never completely built his
proposed machine, despite a lot of money poured into the project by the British
government, because the engineering techniques of the day weren't up to the task.
(In 1991, the London Science Museum finally built a Difference Engine based on
Babbage's drawings.)
In 1889, Herman Hollerith patented a machine that read holes in punched cards to
tabulate data; his machines were used to carry out the 1890 US Census. He founded
the company that later became IBM.
Babbage's and Hollerith's machines were programmable -- you could tell them to
perform different computations -- but they were all about numbers. That was the
situation with computers in general, for the most part, until the AI researchers
introduced symbolic computing: programs about relationships among concepts,
often expressed in the form of mathematical logic, using words and lists of words as
the fundamental data types. John McCarthy invented the Lisp programming
language, a remote ancestor of SNAP, to express this new kind of programming.
Other researchers at around the same time were more interested in trying to
simulate the exact mechanisms of the brain. They developed software simulations
of the neuron, the brain's fundamental building block. Each neuron is connected to
other neurons, to form a network -- a "neural net." The neuron essentially computes
a weighted sum of signals from other neurons, then either does or doesn't "fire" and
sends an output signal to yet other neurons.
In computer-simulated neural nets, the neurons are typically arranged in "layers":
Some neurons get their inputs from an external source, such as a digital camera
pixel; these "first level" neurons send signals to "second level" neurons, which in
turn send signals to "third level" neurons, and so on. Usually there aren't very many
such levels; three levels seem to be enough for most purposes.
The 1969 book _Perceptrons_ by Marvin Minsky and Seymour Papert proved some
theorems about limitations of neural nets -- not that they weren't universal
computers in the Turing sense (which they are), but that the level of complexity
required for certain computations was greater than researchers had hoped. The
book gave rise to a wave of pessimism about neural nets as a vehicle for artificial
intelligence, although there is still argument about whether the book itself
warranted this pessimism. In any case, the result was a virtual abandonment of that
kind of research in the 1970s, when almost all AI research used the competing
symbolic/logical approach. That approach had many early successes, up to a point,
and then seemed unable to progress. (For example, the computer chess programs
use symbolic computation.)
In the 1980s and later, researchers have combined the two approaches, adding a
third set of ideas from statistics. There have been some impressive advances fairly
recently, although in restricted domains. For example, computer recognition of
handwritten characters has advanced to the point where the US Postal Service uses
computers to read zip codes on envelopes and sort the mail automatically. Although
the success rate is not quite 100%, the cases in which the computer fails to sort the
mail correctly turn out to be ones that are also hard for human sorters. (For
example, some people's way of writing the digit 1 looks similar to the way they
write 7.) But computers still can't reliably recognize handwritten letters, just digits.
The field of AI research has always had nay-sayers who think the whole enterprise
is impossible. (Two leading critics of AI are Berkeley professors Hubert Dreyfus and
John Searle.) This reaction was partly fueled by early overly optimistic predictions
by AI researchers, but another reason is that once something works, it's no longer
thought of as "AI." For example, speech recognition, which was once considered an
impossible AI dream, is now commonplace in telephone voicemail systems, but
people don't think of this as representing human-style intelligence just because
machines can clearly do it.
The original AI researchers included some who were trying specifically to simulate
the human brain, and others who just wanted to solve problems using more
"computer-like" techniques. These two approaches have spun off two separate
research areas: "Cognitive Science" is (in part, and in its origins) an attempt to
understand and simulate human thinking, while "Expert Systems" use mainly
symbolic reasoning to solve pragmatic problems ranging from medical diagnosis to
deciding whether to authorize a credit card transaction.