Artificial intelligence seems to be everywhere, but what we are really witnessing is a supervisedlearning revolution: We teach computers to see patterns, much as we teach children to read. But the future
of A.I. depends on computer systems that learn on their own, without supervision, researchers say.
When a mother points to a dog and tells her baby, “Look at the doggy,” the child learns what to call
the furry four-legged friends. That is supervised learning. But when that baby stands and stumbles, again
and again, until she can walk, that is something else. Computers are the same. Just as humans learn mostly
through observation or 20 , computers will have to go beyond supervised learning to reach the holy
grail of human-level intelligence.
“We want to move from systems that require lots of human knowledge and human hand engineering”
toward “increasingly more and more 21 systems,” said David Cox, IBM Director of the MIT-IBM
Watson AI Lab. Even if a supervised learning system read all the books in the world, he noted, it would
still 22 human-level intelligence because so much of our knowledge is never written down.
Supervised learning depends on annotated data: images, audio or text that is painstakingly labeled
by hordes of workers. They circle people or outline bicycles on pictures of street traffic. The labeled data
is fed to computer algorithms, teaching the algorithms what to look for. After ingesting millions of labeled
images, the algorithms become expert at recognizing what they have been taught to see. But supervised learning is constrained to relatively narrow domains defined largely by the training
data. “There is a limit to what you can apply supervised learning to today 23 the fact that you need a
lot of labeled data,” said Yann LeCun, one of the founders of the current artificial-intelligence revolution
and a recipient of the Turing Award, the equivalent of a Nobel Prize in computer science, in 2018. He is
vice president and chief A.I. scientist at Facebook. Methods that do not rely on such precise human-provided supervision, while much less explored,
have been eclipsed by the success of supervised learning and its many practical applications — from selfdriving cars to language translation. 24 supervised learning still cannot do many things that are simple
even for toddlers. “It’s not going to be enough for human-level A.I.,” said Yoshua Bengio, who founded Mila, the
Quebec AI Institute, and shared the Turing Award with Dr. LeCun and Geoffrey Hinton. “Humans don’t
need that much supervision.” Now, scientists at the forefront of artificial intelligence research have turned their attention back to
less-supervised methods. “There’s self-supervised and other related ideas, like reconstructing the input
after forcing the model to a compact representation, predicting the future of a video or masking part of the
input and trying to reconstruct it,” said Samy Bengio, Yoshua’s brother and a research scientist at Google.
Source: https://www.nytimes.com/2020/04/08/technology/ai-computers-learning-supervised-unsupervised.html?searchResultPosition=1
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