Common Sense and Smart Appliances

dilbert-common-sense

At CES 2018, most new consumer products, such as smart appliances and self-driving cars, will sport the label “AI”. As the New York Times puts it: “the real star is artificial intelligence, the culmination of software, algorithms and sensors working together to make your everyday appliances smarter and more automated”. Given the economic and intellectual clout of the acronym AI, it is worth pausing to question its significance, and to reflect on the possible implications of its inflated use in the tech industry and in the academic world.

In 1978, John McCarthy quipped that producing a human-level, or general, AI would require “1.7 Einsteins, 2 Maxwells, 5 Faradays and .3 Manhattan Projects.” Forty years later, despite many predictions of an upcoming age of superintelligent machines, little progress has been done towards the goal of a general AI.  The AI powering the new consumer devices is in fact mostly deep learning, a specialized learning technique that performs pattern recognition by interpolating across a large number of data points.

Twenty years earlier, in 1959, in his seminal paper “Programs with Common Sense“, McCarthy associated human-like intelligence with the capability to deduce consequences by extrapolating from experience and knowledge even in the  presence of previously unforeseen circumstances. This is in stark contrast with the interpolation of observations to closely related contingencies allowed by deep learning. According to this view, intelligent thinking is the application of common sense to long-tail phenomena, that is, to previously unobserved events that are outside the “manifold” spanned by the available data.

This generalized form of intelligence appears to be strongly related to the ability to acquire and process information through language. A machine with common sense should be able to answers queries such as “If you stick a pin into a carrot, does it make a hole in the carrot or in the pin?“, without having to rely on many joint observations of carrots and pins. To use Hector Levesque‘s distinction, if state-of-the-art machine learning techniques acquire street smarts by learning from repeated observations, general AI requires the book smarts necessary to learn from written or spoken language.  Language is indeed widely considered to loom as the the next big frontier of AI.

As most of the academic talent in AI is recruited by the Big Five (Amazon, Apple, Google, Facebook and Microsoft),  the economic incentives for the development of general AI seem insufficient to meet the level of effort posited by McCarthy. And so, rather than worrying about the take-over of a super-AI, given the dominance of the current state-of-the-art specialized AI, what we should “be most concerned about is the possibility of computer systems that are less than fully intelligent, but are nonetheless considered to be intelligent enough to be given the authority to control machines and make decisions on their own. The true danger […] is with systems without common sense making decisions where common sense is needed.” (Hector Levesque, “Common Sense, the Turing Test, and the Quest for Real AI“).

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