When I show you a segment of a
video and I ask what happens next, you
might be able to predict to some extent, but not exactly; there are probably several different outcomes that are
possible. So when you train a system to
predict the future, and there are several possible futures, the system takes
an average of all the possibilities, and
that’s not a good prediction.
Adversarial training allows us to
train a system where there are multiple
correct outputs by asking it to make a
prediction, then telling it what should
have been predicted. One of the central
ideas behind this is that you train two
neural networks simultaneously; there
is one neural net that does the prediction and there’s a second neural net
that essentially assesses whether the
prediction of the first neural net looks
probable or not.
You recently helped found the Partner-
ship on AI, which aims to develop and
share best practices and provide a plat-
form for public discussion.
There are questions related to
the deployment and perception of
AI within the public and government, questions about the ethics of
testing, reliability, and many other
things that we thought went beyond
a single company.
Thanks to rapid advances in the field,
many of these questions are coming
up very quickly. It seems like there’s a
lot of excitement, but also a lot of apprehension in the public about where
AI is headed.
Humans make decisions under
what’s called bounded rationality. We
are very limited in the time and effort
we can spend on any decision. We are
biased, and we have to use our bias
because that makes us more efficient,
though it also makes us less accurate.
To reduce bias in decisions, it’s better
to use machines. That said, you need
to apply AI in ways that are not biased,
and there are techniques being developed that will allow people to make
sure that the decisions made by AI systems have as little bias as possible.
Leah Hoffmann is a technology writer based in Piermont,
© 2018 ACM 0001-0782/18/3 $15.00
In one sense, it’s very practical.
Deep learning has been successful
not just because it works well, but
also because it automates part of the
process of building and designing intelligent systems. In the old days, everything was manual; you had to find
a way to express all of human knowledge in a set of rules, which turns out
to be extremely complicated. Even
in the more traditional realm of machine learning, part of the system was
trained, but most of it was still done
by hand, so for classical computer vision systems, you had to design a way
to pre-process the image to get it into
a form that your learning algorithm
With deep learning, on the other hand,
you can train an entire system more or
less from end to end.
Yes, but you need a lot of labeled
data to do it, which limits the number
of applications and the power of the
system, because it can only learn whatever knowledge is present within your
labeled datasets. The more long-term
reason for trying to train or pre-train a
learning system on unlabeled data is
that, as you said, animals and humans
build models of the world mostly by
observation, and we’d like machines to
do that as well, because accumulating
massive amounts of knowledge about
the world is the only they will eventually
acquire a certain level of common sense.
What about adversarial training, in
which a set of machines learn together
by pursuing competing goals?
This is an idea that popped up a few
years ago in Yoshua Bengio’s lab with
Ian Goodfellow, one of his students at
the time. One important application is
predictions. If you build a self-driving
car or any other kind of system, you’d
like that system to be able to predict
what’s going to happen next—to simulate the world and see what a particular
sequence of actions will produce without actually doing it. That would allow
it to anticipate things and act accordingly, perhaps to correct something or
plan in advance.
How does adversarial training address
the problem of prediction in the pres-
ence of uncertainty?
[CONTINUED FROM P. 120]
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