By Steven Rosenbush
As babies drop spoons and cups from their high-chairs, they come
to understand the concept of gravity. To a parent, it might seem
like the process takes forever, but babies typically grasp the idea
in a few months.
Algorithms require much more data and time to learn much
narrower lessons. A handful of scientists are pushing the furthest
limits of artificial intelligence by training it to better learn by
itself, more like a baby.
"This is the single most important problem to solve in AI
today," says Yann LeCun, chief artificial intelligence scientist at
It is a Manhattan Project-like effort that will go on for years,
if not decades. At Facebook, Alphabet Inc.'s Google and other
companies and universities around the world, scientists are working
to create better AI that learns through self-supervision, teaching
itself about the world the way people do. The immediate goal is
broader AI that can perform multiple tasks, but that could one day
lead to artificial general intelligence, or machines with humanlike
Scientists have had some early success with self-supervised
learning, especially in areas such as natural-language processing
used in mobile phones, smart speakers and customer-service
While there is no assurance of success, ongoing innovations
could help unlock applications from the creation of fully
autonomous vehicles to virtual tutors for school children, more
effective medical-imaging analysis and the real-time identification
of hate speech on Facebook, according to Dr. LeCun.
Today, training AI is time-consuming and expensive, Dr. LeCun
says, and for all that effort it can't comprehend concepts such as
gravity. You might be able to teach today's AI about the dangers of
driving a car too close to a cliff, he says, "but you would have to
crash thousands of times."
In self-supervised learning, AI can train itself without the
need for external labels attached to the data. It doesn't need to
be told "this is a cat" to identify other images of cats, or to
distinguish between images of "cats" and "chairs."
Dr. LeCun is now focused on applying self-supervised learning to
a more complex problem, computer vision, in which computers
interpret images such as a person's face.
The next phase, possible over the next decade or two, is to try
to create a machine that can "learn how the world works by watching
video, listening to audio and reading text," he says.
Dr. LeCun, who shared the 2018 A.M. Turing Award for his work on
deep learning, joined Facebook in 2013.
"Yann's a visionary," says Kyunghyun Cho, a professor of
computer science and data science at New York University's Courant
Institute of Mathematical Sciences, where Dr. LeCun also is
The push for self-supervised learning is a high priority at
Facebook, which is under pressure from lawmakers, outside groups
and its own users to crack down harder on misinformation and hate
An audit commissioned by Facebook, made public in July, found
the company had not done enough to police hate speech and other
problematic content on its platform, despite investments in
AI-based censors and teams of human analysts trained to track down
and remove harmful content.
Self-supervised learning, which can strengthen AI-based filters,
is "very important" to detect hate speech in hundreds of languages,
Dr. LeCun says. "You can't wait for users to flag hate speech. You
have to take it down before anyone sees it," he says.
Not all research is focused on self-supervised learning. Another
important approach is called neuro-symbolic, which combines two
techniques, deep learning and symbolic AI. Using this
neuro-symbolic approach, International Business Machines Corp. is
at work on a technology that extends AI's strength in interpreting
human language to machine language. An AI bot works alongside human
engineers, reading computer logs to spot a system failure,
understand why the system crashed, and offer a remedy. It also can
be used to help people write software code, suggesting ideas much
as a spell-checker might.
Broader AI could, in time, increase the pace of scientific
discovery, given its potential to spot patterns not otherwise
evident, says Dario Gil, director of IBM Research. "It would help
us address huge problems, such as climate change and developing
Until now, the best-performing self-supervised learning has
relied on "contrastive learning," or using examples of what a thing
is not to train the system to recognize the thing itself, according
to Michal Valko, a machine learning scientist at DeepMind, a
U.K.-based Google division focused on AI. A few images of a dog
might be included in a data set of cat images to better illustrate
what a cat is. The so-called negative examples were thought to
improve the performance of the system but could introduce errors,
Dr. Valko says.
A promising idea in self-supervised learning emerged in June,
when Dr. Valko and others at DeepMind published a paper outlining a
new approach. The DeepMind research showed that these negative
examples could be eliminated.
"By doing so we improve the performance, make the method more
robust, and possibly increase the applicability of the method," he
For AI to model and navigate the surrounding world, it will also
be important for AI to go beyond predicting the next item in a
sequence, one at a time. Right now, natural-language processing,
for example, predicts the next word in a sequence by probability.
Ultimately, AI that learns in a self-supervised way would be able
to predict a sequence of events in an arbitrary order and skip
unimportant steps -- much like humans learn to go to the store
without having to perform each step of the process in the same
order every time, says NYU's Dr. Cho.
The ability to make such nonlinear language predictions is
closely related to making longer-term predictions in the physical
world, he says. "We know how to develop a car that can drive by
itself and stay in the lane," he says. But there are unsolved
higher-level problems associated with autonomous vehicles where
self-learning AI can play a role.
"Instead of saying, how do I change the steering wheel moment to
moment, I can just say, 'I need to go to the store,'" Dr. Cho
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Write to Steven Rosenbush at firstname.lastname@example.org
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