make new discoveries in ways people
have not been able to do before. I
would love to discover something that
people haven’t noticed yet.
What about your recent discovery, in an
analysis of 150,000 American yearbook
photos, that people’s smiles broad-
ened during each decade since 1900?
For the portraits, we were very happy to see the increase in smiling over
time. We thought, wow, this is a really cool discovery. Of course, then we
found some psychological literature
that indicates people have already noticed this.
Your work has found applications in
areas from entertainment to security.
What other pie-in-the-sky applications
or discoveries do you hope to see?
Frankly, my goal has always been
to understand and model biological vision. Human vision is too hard,
because it connects with everything
else. We don’t see things as they are;
we see them tinted by language and
culture and all the baggage. But if I’m
able to build a model of a rabbit’s vision or a rat’s vision by the time I retire, I think that would be absolutely
fantastic. Imagine having a model of
this remarkable apparatus that almost all living creatures possess.
Now, because this is such a hard
problem, you don’t get wins very often.
A lot of the time, it’s a depressing slog.
But once in a while, as a kind of by-product, some really neat things come
up that you can use to create pretty
pictures. And I think the world needs
more pretty pictures.
Leah Hoffmann is a technology writer based in Piermont, NY.
© 2017 ACM 0001-0782/17/09 $15.00
Another example is the Shannon
trick of synthesizing text. Imagine
if you start typing an SMS on your
phone but you keep using the predic-tive function. The algorithm is very
basic—it’s just “look for the last time
something like this occurred and
steal the next most probable letter.”
But you get really interesting results,
because you have a lot of data.
Thanks to the Internet, you’ve got ac-
cess to a massive corpus of data. Didn’t
one of your early papers examine two
million images from Flickr?
Exactly. Initially, we said, “We’ll
just download 20,000 images.” The results weren’t great. But my then-grad
student, James Hays, was like, “Why
don’t we just keep downloading?” If
you look at the big neural networks
right now, it is really impressive what
they can do. But I think people are forgetting that one of the reasons they’re
so powerful is that they are able to
gobble up orders of magnitude more
data than we could do with earlier
methods. This is not very glamorous,
because it suggests that humans are
not so smart. It’s really the data.
That reminds me of the old philo-
sophical debate about experiential vs.
a priori knowledge.
People like to rationalize. They like
to get a nice beautiful theory of the
world. But reality is often really noisy
and complicated, and in a way, data allows you to use this complexity, to not
have to throw it away. It’s not the minimalist beauty, the clean lines. It’s the
beauty of a jumbled mess.
Your analyses of photographic data
sets like faces and building facades
have also revealed lots of visual trends
that might not otherwise have been
easy to notice.
That is a big beautiful promise
and we’re only scratching the surface. People are good at finding certain kinds of patterns. We can hold a
small number of things in our minds
and compare them. We are not able
to find a tiny, tiny little pattern over
thousands or millions of data points,
or very subtle changes over a long
range of time. Using computer vision
and techniques, I’m hoping we can
“We don’t see things
as they are; we see
them tinted by
language and culture
and all the baggage.”
[CONTINUED FROM P. 104]
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