For LeCun, the proposals could be the first steps on a path to building machines with the ability to reason and plan like humans--what many call artificial general intelligence, or AGI. He also steps away from today's hottest trends in machine learning, resurrecting some old ideas that have gone out of fashion. But his vision is far from comprehensive; indeed, it may raise more questions than it answers. The biggest question mark, as LeCun points out himself, is that he does not know how to build what he describes. The centerpiece of the new approach is a neural network that can learn to view the world at different levels of detail.

Around a year and a half ago, Yann LeCun realized he had it wrong. LeCun, who is chief scientist at Meta's AI lab and one of the most influential AI researchers in the world, had been trying to give machines a basic grasp of how the world works--a kind of common sense--by training neural networks to predict what was going to happen next in video clips of everyday events. But guessing future frames of a video pixel by pixel was just too complex. Now, after months figuring out what was missing, he has a bold new vision for the next generation of AI. In a draft document shared with MIT Technology Review, LeCun sketches out an approach that he thinks will one day give machines the common sense they need to navigate the world.

What is the contribution that the arts and humanities can make to our engagement with the increasingly pervasive technology of artificial intelligence? My aim in this short article is to sketch some of these potential contributions. Perhaps the most fundamental contribution of the arts and humanities is to make vivid the fact that the development of AI is not a matter of destiny, but instead involves successive waves of highly consequential human choices. It's important to identify the choices, to frame them in the right way, and to raise the question: who gets to make them and how? This is important because AI, and digital technology generally, has become the latest focus of the historicist myth that social evolution is preordained, that our social world is determined by independent variables over which we, as individuals or societies, are able to exert little control. So we either go with the flow, or go under.

"That job [to formalize ... the fundamental elements of human understanding] proved difficult and was never finished. A.I. researchers need to return to that project sooner rather than later, ideally enlisting the help of cognitive psychologists who study the question of how human cognition manages to be endlessly flexible. Today's dominant approach to A.I. has not worked out. If machine learning and big data can't get us any further than a restaurant reservation, even in the hands of the world's most capable A.I. company, it is time to reconsider that strategy.'