searchers, and we are always impatient
for more, because we are far from human-level AI, and the dream of understanding the principles of intelligence,
natural or artificial.
What isn’t discussed enough?
HINTON: What does this tell us about
how the brain works? People ask that,
but not enough people are asking that.
BENGIO: It’s true. Unfortunately, al-
though deep learning takes inspiration
from the brain and from cognition, many
engineers involved with it these days
don’t care about those topics. It makes
sense, because if you’re applying things
in industry, it doesn’t matter. But in
terms of research, I think it’s a big loss if
ble?” In the old days, people in AI made
grand claims, and they sometimes
turned out to be just a bubble. But neu-
ral nets go way beyond promises. The
technology actually works. Further-
more, it scales. It automatically gets
better when you give it more data and a
faster computer, without anybody hav-
ing to write more lines of code.
YANN LECUN: That’s true. The basic
idea of deep learning is not going away,
but it’s still frustrating when people ask
if all we need to do to make machines
more intelligent is simply scale our current methods. We need new paradigms.
YOSHUA BENGIO: The current techniques have many years of industrial
and scientific application ahead of
them. That said, the three of us are re-
ONCE TREATED BY the field with skepticism
(if not outright derision), the artificial
neural networks that 2018 ACM A.M.
Turing Award recipients Geoffrey Hinton, Yann LeCun, and Yoshua Bengio
spent their careers developing are today
an integral component of everything
from search to content filtering. So what
of the now-red-hot field of deep learning
and artificial intelligence (AI)? Here, the
three researchers share what they find
exciting, and which challenges remain.
There’s so much more noise now about
artificial intelligence than there was
when you began your careers—some
of it well-informed, some not. What do
you wish people would stop asking you?
GEOFFREY HINTON: “Is this just a bub-
Reaching New Heights
with Artificial Neural Networks
ACM A.M. Turing Award recipients Yoshua Bengio, Geoffrey Hinton, and Yann LeCun
on the promise of neural networks, the need for new paradigms, and the concept of making
technology accessible to all.
DOI: 10.1145/3324011 Leah Hoffmann
[CONTINUED ON P. 94]