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Chapter 1 ■ IntroduCtIon to artIfICIal IntellIgenCe
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There was considerable work in the late 1960s on how a computer could converse
with a human by using natural language instead of computer code. One clever program
created by Joseph Weisenbaum during this time was named ELIZA. While primitive
by today’s standards, it was still able to fool some users into thinking that they were
conversing with another human instead of a machine. The ELIZA project brings up a
very interesting topic regarding how one might determine if a machine has reached
some level of “intelligence.” One good answer lies in what is known as the Turing test,
which I mentioned earlier. In a 1950 article in the Journal of Computing Machinery and
Intelligence, Alan Turing discussed what he felt were sufficient conditions for considering
a machine to have reached an intelligent state. He essentially argued that if a machine
could successfully fool a knowledgeable human observer into thinking that he was
having a conversation with another human instead of a machine, then the machine could
be considered intelligent. Of course, the conversation would have had to done using
a neutral communications channel to avoid the obvious clues of voice or appearance
giving away the machine. Teletypes were the communication devices used in the 1950s
to implement the neutral channel. The Turing test is still a reasonable benchmark, even
considering today’s technologies. One could even use highly effective modern voice
recognition and synthesis technologies to further fool the observer. The Turing test is still
controversial among philosophers and other interested parties who discuss the nature of
intelligence.
In the 1970s, AI was slow to mature, due to the slow growth of computing technology.
There was a lot of interest in natural language processing and image recognition and
analysis, but unfortunately, the computers available to researchers were still quite limited
and not up to these difficult tasks. It soon became apparent that there would have to be
significant improvement in processing power before AI could really progress. In addition,
there were also significant philosophical arguments against AI, including the famous
“Chinese room” argument postulated by John Searle. Minsky argued against Searle’s
hypothesis, which only led to a lot of infighting and misdirection in ongoing research.
Meanwhile, McCarthy argued for a modern AI approach, stating that human intelligence
and machine intelligence are different and should be treated that way.
The 1980s showed considerable improvement in AI development due to the onset
of the PC and many researchers taking on McCarthy’s pragmatic approach. The advent
of expert systems happened in this timeframe, which showed great promise and actual
applications in the business and industrial/manufacturing sectors. I demonstrate several
expert system applications in later chapters. The classic AI methodology continued;
however, the modern approach was rapidly gaining acceptance, and perhaps more
importantly, was used in many real-world situations. Coincidentally, there was a lot
being done with robotics and real robot development at this time. AI research naturally
gravitated to this area, because the areas seemed perfectly complementary. The age
of practical AI had finally arrived and future developments came quickly, as the age
of modern computing was also happening. It was about this time that the real impact
of Moore’s law became apparent. Moore’s law refers to Gordon Moore, one of Intel’s
founders, who stated in 1965: “The number of transistors per square inch on integrated
circuits has doubled every year since their invention.”
This exponential growth in density seems to correlate nicely with the incredible
improvement in computer performance, which is so sorely need for AI improvement and
growth.