DESPITE the fact that he does not see
very well, Alexei Efros, recipient of the
2016 ACM Prize in Computing and a
professor at the University of California
at Berkeley, has spent most of his career trying to understand, model, and
recreate the visual world. Drawing on
the massive collection of images on the
Internet, he has used machine learning algorithms to manipulate objects
in photographs, translate black-and-white images into color, and identify
architecturally revealing details about
cities. Here, he talks about harnessing
the power of visual complexity.
You were born in St. Petersburg (Russia),
and were 14 when you came to the U.S.
What drew you to computer science?
I was interested in computers from
an early age. I remember reading a
book about PDP- 11 assembly language programming when I was 12
and dreaming about how one day, I
might actually have a computer of my
own to try this out in practice. Then,
in high school, I did some research
with a professor at the University of
Utah. It sounds kind of brazen, but I
went to the CS department and was
like, “Bring me to your chairman.”
Tom Henderson was the chair at that
time and, you know, he actually saw
me. I told him that I wanted to do
computer science and asked him for
a problem. And he basically said, “Ok,
weird Russian kid. I have a robot running around; do you want to help with
that?” It was wonderful.
You did your undergraduate work at
the University of Utah, as well.
Interestingly enough, I was actually considering whether I should go
into computer science (CS) or theater.
In fact, I applied to Carnegie Mellon
University because it’s one of the top
departments in CS, but also one of
the top universities for theater. Then
I showed my father the tuition, and,
well, we were immigrants. So I went
to the University of Utah, where CS
was much stronger than theater, and I
think I got a very good education. But
I’m still practicing my stagecraft twice
a week in my classes.
I’ve seen your talks. You’re a very en-
There is this whole dichotomy be-
How did you get involved with com-
tween the geeks and the artsy people—
either you are good with numbers, or
with arts and humanities. I think it’s
misplaced. CS is hot right now. A lot of
smart kids go into CS, and many look
down at all of these humanities peo-
ple with disdain. In my classes, I try to
remind them that computer scientists
are hot now, but physicists were hot
in the Sixties, and chemists were hot
in the Thirties, and they’re not super-
hot now. Shakespeare is going to be
around much longer than Python.
puter vision, graphics, and machine
Even in high school, my goal was to
solve AI. But then I reasoned it out: AI
is too hard, and you don’t know when
you’re succeeding. With language, you
kind of know when you’re succeeding,
but that’s also very high-level. Meanwhile, almost all animals have vision.
Vision seems like the most basic thing,
so it’s got to be easy, right?
Basically, I think I’ve just had one
idea throughout my whole career, and
I’ve been milking it since undergrad,
and the idea is not even that profound.
It’s that we fetishize intellectual contributions—algorithms, data structures, and so on. And we often forget
that a lot of the complexity in the
world is actually due to the data. My
favorite example is in computer graphics. We know how light behaves, and
we can simulate everything we want.
But the reason current animated movies don’t look like the real thing is the
data. There is a lot of entropy in the
world and it’s just too hard to capture.
The algorithms are fine. It’s the data
that is miss-
All The Pretty Pictures
Alexei Efros, recipient of the 2016 ACM Prize in Computing,
works to harness the power of visual complexity.
DOI: 10.1145/3121444 Leah Hoffmann
[CONTINUED ON P. 103]