Artificial Intelligence

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 neuron 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 Scratch, even though Scratch
doesn't have Lisp's emphasis on words as data) 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 send 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
overoptimistic 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.