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An achievable view of artificial intelligence

From the cautionary tale of AI run amok in Stanley Kubrick’s 2001: A Space Odyssey, to the benign computerized assistant that helped Captain Kirk “boldly go where no man had gone before” in Star Trek, the 1960s were filled with visions of an AI-enhanced future that still hasn’t materialized a half-century later.

And almost every product on the market that includes software is advertised as “powered by AI.” But before you bet your technical future on the brave new world of AI, a clear-eyed look at some facts would be wise.

There have also been the more dystopian visions of AI displacing wide swaths of the human workforce, leading to millions of people whose jobs were taken over by the AI-powered machines, or even an ultimate war fueled by our AI progeny’s conclusion that humans are superfluous and inefficient, such as that envisioned in the Terminator movies.

When most of us hear the term “artificial intelligence,” we tend to think of the kind of science fiction AI that can respond to somewhat ambiguous voice commands and perform complex calculations and feats of logic.

This is a slippery and imprecise definition for the simple reason that we have a very hard time defining “intelligence.” Set aside the purported measures of intelligence such as the Intelligence Quotient (IQ) test or academic achievement tests such as those used for college admissions.

Instead, for purposes of this discussion, let us consider intelligence the combination of the ability to store and recall basic facts, to relate and reason about those facts, and to apply creative solutions to new situations.

Written forty years ago, this book breaks down what it means to think at the lowest level, and delves into the mind-boggling layers of abstraction that exist between even simple mathematical concepts and how we think about them when we use arithmetic in our daily lives.

In contrast, when a data model if formalized using OWL or another formal logic representation (e.g., Common Logic), we can write inference rules and apply them using the rules of formal logic.

Consider a simple example: We create a simple data model that contains one class, “Person,” with two attributes, “name” and “sex.” We establish two relationships that can exist among instances of the Person class: “has_parent” and “has_sibling.” With this simple model we can store data such as PersonA has_sibling PersonB and PersonB has_Parent PersonC.

simple rule such as “if person1 has_sibling person2 and person2 sex=Female, then person1 has_sister person2” lets us infer new knowledge about every person in the database (remember, the original data model did not include the concept of a sister).

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