Editors’ Choice: Good AI Computing Well

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Two Directions in AI

In the inaugural issue of AI & Society, published in 1987, Ajit Narayanan identified two directions that propelled the discipline of artificial intelligence. The first was “Implement and be damned” whereby programs are produced to replicate tasks performed by humans with relevant expertise (p. 60). Motivated by efficiency, these programs might only tangentially be identified as AI, Narayanan noted, because, rather than adhering to certain computing principles, they might simply be written in a particular programming language associated with AI. (See, for example, Lisp.) The second direction was “We’re working on it,” which he associated with “grandiose claims” about the future of AI systems that “‘could control a nuclear power station’” or “‘shield us from incoming missiles’.” But both directions in AI shared the same dangers, according to Narayanan: an economic imperative that would further displace the care of humans for that of profit and a misplaced belief in the power of computation to solve problems more accurately than humans, perhaps even perfectly. To combat these dangers, he pointed to the importance of accountability to the general public; for, “as long as AI is removed from the domain of ordinary people, AI will remain unaccountable for whatever products it produces” (p. 61).

AI Now

In the three decades since Narayanan made his argument, much has changed, with ordinary people being dialed into the everyday relevance of AI, as well as its potential for transformative societal effects. In addition to the near constant heralding of the practical benefits of AI on college campuses, to the aging, in music streaming, and with transportation, AI has also been celebrated for its potential in creative endeavors in IBM’s Watson advertisements that have featured Bob Dylan and Stephen King. (Much-needed parody of Dylan’s ad is available here.) And although such celebration may be premature, the success of Google’s AlphaGo points to the very real possibility of strategic, quotidian invention on the part of AI.

 

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