WSJ asks whatâ€™s next for Artificial Intelligence? Director of AI at Facebook Yann LeCun asks: How do you teach a machine?
Adam Rifkin stashed this in Machine Learning
Yann LeCun, director of artificial-intelligence research atÂ Facebook, on a curriculum for software:
The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simpleâ€”recognizing an object in a photo, driving a carâ€”are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a $30 gadget can beat us at a board game, but it canâ€™t doâ€”or learn to doâ€”anything else.
This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats. Machine learning is the basis on which all large Internet companies are built, enabling them to rank responses to a search query, give suggestions and select the most relevant content for a given user.
Deep learning, modeled on the human brain, is infinitely more complex. Unlike machine learning, deep learning can teach machines to ignore all but the important characteristics of a sound or imageâ€”a hierarchical view of the world that accounts for infinite variety. Itâ€™s deep learning that opened the door to driverless cars, speech-recognition engines and medical-analysis systems that are sometimes better than expert radiologists at identifying tumors.
Despite these astonishing advances, we are a long way from machines that are as intelligent as humansâ€”or even rats. So far, weâ€™ve seen only 5% of what AI can do.
YOU CANâ€™T TEACH (MACHINES) COMMON SENSE
At least not yet. And itâ€™s the biggest barrier to true artificial intelligence.
Predictive learning, also called unsupervised learning, is the principal mode by which animals and humans come to understand the world. Take the sentence â€śJohn picks up his phone and leaves the room.â€ť Experience tells you that the phone is probably a mobile model and that John made his exit through a door. A machine, lacking a good representation of the world and its constraints, could never have inferred that information. Predictive learning in machinesâ€”an essential but still undeveloped featureâ€”will allow AI to learn without human supervision, as children do. But teaching common sense to software is more than just a technical questionâ€”itâ€™s a fundamental scientific and mathematical challenge that could take decades to solve. And until then, our machines can never be truly intelligent.Â â€”Yann LeCun