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error on held-out data. They're going to be interested in the insights and iden-
tification of the interesting covariates, or the interesting interactions among
the covariates revealed to you.
I come from a tradition in physics that has a long relationship with predictive
interpretability. We strive to build models that are as simple as possible but
not simpler, and the real breakthroughs, the real news-generating events, in the
history of physics have been when people made predictions that were borne
out by experiment. Those were times that people felt they really understood
a problem.
Gutierrez:
Whose work is currently inspiring you?
Wiggins:
It's always my students. For example, I have a former student, Jake
Hofman, who's working with Duncan Watts at Microsoft Research. Jake was
really one of the first people to point out to me how social science was birth-
ing this new field of computational social science, where social science was
being done at scale. So that's an example of a student who has introduced me
to all these new things.
I would also say that all of the kids who go through hackNY are constantly
introducing me to things that I've never heard of and explaining things to me
from the world that I just don't understand. We had a hackNY reunion two
Friday nights ago in San Francisco. I was out there to give a talk. We organized
a reunion, and the Yo app had just launched. So a lot of the evening was me
asking the kids to explain Yo to me, which meant explaining the security flaws
in their API and not just how the app worked. So that's the benefit of working
with great students. Students are constantly telling you the future of technol-
ogy, data science, and media amongst other things, if you just listen to them.
Former students and postdocs of mine have gone on to work at BuzzFeed,
betaworks, Bitly, and all these other companies that are at the intersection of
data and media.
I have also benefited greatly from really good colleagues whom I find inspir-
ing. The way I ended up here at The New York Times, for example, was that,
when I finally took a sabbatical, I asked all my faculty colleagues what they did
with their sabbaticals, because I had never taken one. My friend and colleague
Mark Hansen did the “Moveable Type” lobby art here in the New York Times
Building. So if you go look at the art in the lobby, Mark Hansen wrote the
Python to make the lobby art “go”, and he did that in 2007 when they moved
into this building. So he knew many people at The New York Times, and he
introduced me to a lot of people here and was somebody who explained to
me—though he didn't use these words—that The New York Times is now in
a similar state to the state that biology was in 1998. That is, that it's a place
where they have abundant data, and it's still up for grabs what the right way is
to use machine learning to make sense of those data.
