Is artificial intelligence the new all natural? That’s what Tech Crunch’s Devin Coldewey thinks.
In the United States, there are no formalized requirements that a food product must meet to be deemed all natural. It means many things to many different people, especially those people marketing foods. Throwing an extra, positive sounding descriptor on a product is a great tactic for boosting its commercial appeal. Artificial intelligence is much the same; in the absence of authority, ideas about its meaning abound. Coldewey argues that many, if not most, claims of artificial intelligence are mere puffery.
What is Intelligence
We can debate whether a computer has artificial intelligence, but this raises the larger question of the meaning of intelligence. This article is hardly the place to review the theories behind intelligence, you’d be reading forever. I like defining intelligence as the ability to solve complex problems with creativity by gathering information, developing knowledge, and executing ideas. Researchers posit a number of areas of intelligence; without going into all of the proposed intelligence types, examples include linguistic, artistic, and numeric, among many more. This raises the interesting question of whether one can be intelligent if he or she excels in some categories but lags in others. Psychologist Charles Spearman’s research in the early 1900s identified g-factor as an underlying general intelligence, a high level concept driving performance on discrete measures. G-factor manifests as the correlation in performance on the discrete intelligence measures; intelligence in one area suggests intelligence in other areas. As an aside, Spearman, having used tens of intelligence metrics, developed factor analysis, whereby several variables are examined to determine whether they move together, thus possibly under control of some other (perhaps unmeasured) driver.
We run into a problem when considering artificial intelligence in the context of different forms of intelligence. Computers are clearly capable on a mathematics ability axis when one considers how numeric intelligence is measured (i.e.: solving math problems), however they fall short with art (screenplays written by computers are more comedy than drama!). Perhaps we need a method of arriving at a computer’s g-factor, if artificial intelligence can even be described with a g-factor.
Defining Artificial Intelligence
Given the complexity of defining intelligence, what can we say of artificial intelligence? I propose that rather than defining artificial intelligence as binary–as a system either having artificial intelligence or not–a system must be considered as having intelligence on continua on multiple axes.
Under such a paradigm, a computer employed to solve Ito calculus problems such as predicted rocket flight trajectories, might score very highly on numeric ability but poorly on self awareness. Self aware robots, likewise, may perform well on inter- and intrapersonal intelligence, but poorly on mathematical intelligence. To measure these systems’ intelligence requires a global review of their skills, maybe this is accomplished by scoring each metric (of how many to be determined) and taking an average. Maybe achieving this requires accepting that there are too many facets of artificial intelligence to reduce it to a single value.
This is more than an academic exercise. Where artificial intelligence is of great interest to consumers, researchers, product designers, healthcare, industry, government and military, and more, we must have a uniform definition, scoring system, and vocabulary to communicate it.
© Peter Roehrich, 2017