A behavior-science perspective on why it’s still a long way to human-like AI

In Greg Egan(link is external)‘s short story Learning to Be Me(link is external), published in 1990 and set in what now feels like a rather near future, every human can have their brain shadowed by a “jewel”—a small computer that fits inside the skull and over time learns the connections among their sensations, thoughts, and actions. Learning is deemed to be complete when the jewel’s patterns of implied behavior become statistically indistinguishable from those of the person’s actual behavior, at which point the brain can be surgically removed and the jewel can take the helm.

The successes of AI systems such as Deep Blue(link is external) (playing chess), and, more recently,Watson(link is external) (playing Jeopardy!(link is external)), ImageNet(link is external) contestants (doing image classification), Siri(link is external)(conversing in natural language), and AlphaGo(link is external) (playing Go) seem to suggest that technology that could support this kind of learning prowess is just around the corner. After all, isn’t human-level intelligence just the sum of the capacities for perception (as in image classification), planning and decision making (as in chess or Go), and language (as in Jeopardy!)? Perhaps surprisingly—for many lay people and for those engineers who have little patience for psychology, ethology, and brain science—it is not.

On the one hand, all the tasks in which modern AI has scored major successes are, without exception, fundamentally alike. What it takes to excel in those tasks is being very, very good at mapping inputs to outputs, or, as a mid-20th century psychologist would say, stimuli to responses (S/R).  In image classification, the input is a picture and the output is a category name (“cat” or “dog”; “beach” or “kitchen”). In Jeopardy!, the input is a phrase and the output—an encyclopedia item (person, entity, or event) that fits it best. In a board game such as chess or Go, the input is the board configuration and the output is the best legal move. The list goes on (for a thorough and somewhat technical discussion, see my paper The minority report: some common assumptions to reconsider in the modeling of the brain and behavior(link is external) (Journal of Experimental and Theoretical AI, 2015) [paywall; email me for a copy].

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