We are in the midst of a revolution in machine intelligence, the art and engineering practices that let computers perform tasks that, until recently, could be done only by people.
There is now software that identifies faces at border crossings and matches them against passports or that labels people and objects in photographs posted to social media. Algorithms can teach them to play Atari video games. A camera and chip embedded in top-of-the-line sedans let the vehicles drive autonomously on the open road.
The learning curve
What separates these agents from earlier success stories, such as IBM’s Deep Blue, which beat the world’s reigning chess champion in 1997, and IBM’s Watson, which accomplished the same for the quiz show Jeopardy in 2011, is that they are taught by trial and error.
The new wave of artificial intelligence (AI) is based on insights derived from the way animals and people learn and analysis of the underlying brain circuits that allowed theorists to develop supervised learning algorithms: The software is shown an image, and depending on whether or not it correctly identifies the face or increases the video game score, parameters internal to the program (so-called synaptic weights) are minutely adjusted.
Such machine learning, if done over trillions of machine cycles (yes, it is very computing-intensive), can lead to systems that match or, in some cases, exceed human performance metrics. And, of course, the algorithm never gets distracted or tired and remains focused, day and night.
AI is the future
Within a decade these instances of “weak” or “narrow” AI — able to replicate specific human tasks — will permeate society. Siri is only the beginning. Driverless cars and trucks will become the norm, and our interactions in supermarkets, hospitals, industry, offices and financial markets will be dominated by narrow AI. The torrid pace of these advances will put severe stress on society to deal peacefully with the attendant problems of unemployment (the US trucking industry alone employs several million drivers) and growing inequality.
‘Weak’ vs ‘Strong’ AI
Obscured by this razzle-dazzle progress is how far away we remain from “strong” or “general” AI, comparable to the intelligence of the proverbial man or woman in the street who can navigate a car, hurtle on skis down a mountain slope, carry on a conversation about pretty much any topic — often in two or more languages. That same ordinary individual might also play a variety of games, serve on a jury and plan for retirement decades in the future. Hampering our ability to design general AI is the embarrassing fact that we don’t understand what we mean by “intelligence.” This lack of knowledge makes any predictions of when we will achieve strong AI fraught with uncertainty. Still, it may not be so far away. For the record, most experts believe that strong machine intelligence will arrive before the century is over, assuming current trends continue.