Huyssteen, Functional or weak Artificial Intelligence

In 1980, John Searle, in the paper “Minds, Brains, and Programs,” introduced a division of the field of AI into “strong” and “weak” AI. Strong AI denoted the attempt to develop a full human-like intelligence, while weak AI denoted the use of AI techniques to either better understand human reasoning or to solve more limited problems. Although there was little progress in developing a strong AI through symbolic programming methods, the attempt to program computers to carry out limited human functions has been quite successful. Much of what is currently labeled AI research follows a functional model, applying particular programming techniques, such as knowledge engineering, fuzzy logic, genetic algorithms, neural networking, heuristic searching, and machine learning via statistical methods, to practical problems. This view sees AI as advanced computing. It produces working programs that can take over certain human tasks. Such programs are used in manufacturing operations, transportation, education, financial markets, “smart” buildings, and even household appliances.

For a functional AI, there need be no quality labeled “intelligence” that is shared by humans and computers. All computers need do is perform a task that requires intelligence for a human to perform. It is also unnecessary, in functional AI, to model a program after the thought processes that humans use. If results are what matters, then it is possible to exploit the speed and storage capabilities of the digital computer while ignoring parts of human thought that are not understood or easily modeled, such as intuition. This is, in fact, what was done in designing the chess-playing program Deep Blue, which in 1997 beat the reigning world chess champion, Gary Kasparov. Deep Blue does not attempt to mimic the thought of a human chess player. Instead, it capitalizes on the strengths of the computer by examining an extremely large number of moves, more moves than any human player could possibly examine.

There are two problems with functional AI. The first is the difficulty of determining what falls into the category of AI and what is simply a normal computer application. A definition of AI that includes any program that accomplishes some function normally done by a human being would encompass virtually all computer programs. Nor is there agreement among computer scientists as to what sorts of programs should fall under the rubric of AI. Once an application is mastered, there is a tendency to no longer define that application as AI.

For example, while game playing is one of the classical fields of AI, Deep Blue’s design team emphatically states that Deep Blue is not artificial intelligence, since it uses standard programming and parallel processing techniques that are in no way designed to mimic human thought. The implication here is that merely programming a computer to complete a human task is not AI if the computer does not complete the task in the same way a human would.

For a functional approach to result in a full human-like intelligence it would be necessary not only to specify which functions make up intelligence, but also to make sure those functions are suitably congruent with one another. Functional AI programs are rarely designed to be compatible with other programs; each uses different techniques and methods, the sum of which is unlikely to capture the whole of human intelligence. Many in the AI community are also dissatisfied with a collection of task-oriented programs. The building of a general human-like intelligence, as difficult a goal as it may seem, remains the vision.


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