How the Enlightenment Ends - The Atlantic: Second, that in achieving intended goals, AI may change human thought processes and human values. AlphaGo defeated the world Go champions by making strategically unprecedented moves—moves that humans had not conceived and have not yet successfully learned to overcome. Are these moves beyond the capacity of the human brain? Or could humans learn them now that they have been demonstrated by a new master?
EDMON DE HARO
Before AI began to play Go, the game had varied, layered purposes: A player sought not only to win, but also to learn new strategies potentially applicable to other of life’s dimensions. For its part, by contrast, AI knows only one purpose: to win. It “learns” not conceptually but mathematically, by marginal adjustments to its algorithms. So in learning to win Go by playing it differently than humans do, AI has changed both the game’s nature and its impact. Does this single-minded insistence on prevailing characterize all AI?
Other AI projects work on modifying human thought by developing devices capable of generating a range of answers to human queries. Beyond factual questions (“What is the temperature outside?”), questions about the nature of reality or the meaning of life raise deeper issues. Do we want children to learn values through discourse with untethered algorithms? Should we protect privacy by restricting AI’s learning about its questioners? If so, how do we accomplish these goals?
If AI learns exponentially faster than humans, we must expect it to accelerate, also exponentially, the trial-and-error process by which human decisions are generally made: to make mistakes faster and of greater magnitude than humans do. It may be impossible to temper those mistakes, as researchers in AI often suggest, by including in a program caveats requiring “ethical” or “reasonable” outcomes. Entire academic disciplines have arisen out of humanity’s inability to agree upon how to define these terms. Should AI therefore become their arbiter?
EDMON DE HARO
Before AI began to play Go, the game had varied, layered purposes: A player sought not only to win, but also to learn new strategies potentially applicable to other of life’s dimensions. For its part, by contrast, AI knows only one purpose: to win. It “learns” not conceptually but mathematically, by marginal adjustments to its algorithms. So in learning to win Go by playing it differently than humans do, AI has changed both the game’s nature and its impact. Does this single-minded insistence on prevailing characterize all AI?
Other AI projects work on modifying human thought by developing devices capable of generating a range of answers to human queries. Beyond factual questions (“What is the temperature outside?”), questions about the nature of reality or the meaning of life raise deeper issues. Do we want children to learn values through discourse with untethered algorithms? Should we protect privacy by restricting AI’s learning about its questioners? If so, how do we accomplish these goals?
If AI learns exponentially faster than humans, we must expect it to accelerate, also exponentially, the trial-and-error process by which human decisions are generally made: to make mistakes faster and of greater magnitude than humans do. It may be impossible to temper those mistakes, as researchers in AI often suggest, by including in a program caveats requiring “ethical” or “reasonable” outcomes. Entire academic disciplines have arisen out of humanity’s inability to agree upon how to define these terms. Should AI therefore become their arbiter?