“Artificial Intelligence” is a technology area with a vague definition, but it has gotten a lot of attention recently. Google recently bought the company DeepMind for more than $500 million, according to reports. DeepMind’s web site characterizes it as “a cutting edge artificial intelligence company,” describing it as combining “the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms.” Facebook recently created an Artificial Intelligence lab, adding to the drumbeat for AI.
Just what is “artificial intelligence”? In The Software Society, I objected to the term as misleading, arguing that the intelligence exhibited by computers shouldn’t have as its objective mimicking a human for the sake of doing so, as the famous “Turing Test” for the success of an artificial intelligence system implies. Instead, “computer intelligence” can augment human intelligence by doing what it does best; for example, computers have a memory of details that goes well beyond what any human could master and an ability to access that data quickly. Computers win at chess because they can look at every possible move up to a large number of moves ahead to see the implications of the next move. The key to computers helping people is making that computer intelligence more easily accessible, rather than trying to emulate human emotions and foibles.
It seems that, as soon as something reaches a certain level of commercial utility, such as speech recognition, it no longer falls under “artificial intelligence,” so another objection is that AI seems to describe an objective always in the future. Perhaps it is convenient to have a term such as artificial intelligence for a body of research that does things that we associate with human intelligence. The danger is that the methodology underlying this body of techniques is misunderstood, as in an article in the February 4 issue of the Wall Street Journal entitled, “Startups, Tech Giants Race To Code the Human Brain.”
I’ll delve into what the technology in this area is, and why it’s misleading to compare the methods to the way human brains work. But I feel, for credibility, I need to first indicate why I’m qualified to comment on this. My PhD dissertation was on a simple model of neurons as a logic element and resulted in a couple of refereed research articles (e.g., “Nets of variable-threshold elements”, IEEE Transactions on Computers, July 1968). I taught courses on artificial intelligence as a prof at USC at the start of my career, and presented technical talks such as “On the Design of Self-Programming Computers” (Proc. 1969 Symp. American Society of Cybernetics, October 1969). I wrote a technical book, Computer Oriented Approaches to Pattern Recognition (Academic Press, 1972), which was the first comprehensive book on what is considered machine learning today, with chapters on subjects such as “cluster analysis and unsupervised learning.”
I applied that pattern recognition technology as manager of the Computer Science Division of an engineering company for ten years, with applications such as radar and sonar target recognition, predicting cancer survival rates, detecting traffic patterns to control freeway on-ramp access, predicting smog alerts, and, yes, I even helped the NSA recognize patterns in data. I then spent ten years running a venture-backed speech recognition company I founded, where we developed the first commercial speech recognition system that worked at the phonetic level (trademarked the Phonetic Engine). The speech technology used a form of mathematical neural networks (described in “A Continuous Speech Recognizer Using Two-Stage Encoder Neural Nets,” Proc. International Joint Conference on Neural Networks, Washington D.C., January 1990).