This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.
In 1996 and 1997, reigning world chess champion Garry Kasparov played two six-game matches against an IBM computer called Deep Blue. Kasparov won the first match. Deep Blue was victorious in the second match, marking the first time that a computer had defeated a reigning world champion. Today, any of us could buy an inexpensive chess program for our laptop computer which would consistently outplay Deep Blue. In 1997 however, Deep Blue’s victory was a milestone in the development of artificial intelligence (AI). There was much speculation at the time about the future of AI and about the similarities of conputing machines and the human mind. Yale University professor David Gelernter urged restraint.
“The idea that Deep Blue has a mind is absurd. How can an object that wants nothing, fears nothing, enjoys nothing, needs nothing, and cares about nothing have a mind? It can win at chess, but not because it wants to. It isn’t happy when it wins or sad when it loses. What are its [post]-match plans if it beats Kasparov? Is it hoping to take Deep Pink out for a night on the town?” (Gelertner, 1997)
Ask ten people to define “artificial intelligence” and you will get ten different answers. This is, in some ways, beneficial ambiguity. Firm definitions can be needlessly constraining to those whose role it is to innovate. According to Stone et al at Stanford University, “the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.” (Stanford, 2016). Nevertheless, to talk about a thing intelligently, it is worth a paragraph or two to define it as it is used in the present discussion. The word “artificial” is relatively easy to define. We use it to refer to things that do not occur naturally, things that are created by humans. The word “intelligence” is a bit more difficult. Philosophers, psychologists, anthropologists, and even theologians offer a variety of definitions. For the purposes of this article series, we will start with a definition which focuses on problem-solving. Psychologist Howard Gardner wrote, “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner, 1983).
Thus, artificial intelligence or AI, as we shall use it here, is a machine capability, which does not occur naturally, to solve problems or create something of value. We call this the realm of narrow AI, where an intelligent apparatus is created to solve a particular kind of problem or create a particular type of valuable thing. General AI, which theoretically includes non-trivial foresight, independent problem identification, and problem prioritization based on something like a human value system, or human-like hopes and dreams does not yet exist. In Gelertner’s view, “the gap between the human and the [electronic] surrogate is permanent… Machines will continue to make life easier, healthier, richer, and more puzzling. And humans will continue to care, ultimately, about the same things they always have: about themselves, about one another, and many of them, about God.” (Gelertner, 1976)
In the next installment, we will consider, “What does AI offer humankind?”
Notes and references:
Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020. https://www.wcsaglobal.org/volume-1-issue-2-2020/
Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.
Gelernter, D., 1997, How Hard Is Chess? TIME, (May 19, 1997)
Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. http://ai100.stanford.edu/2016-report