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But with supervised learning, I usually can start by asking: How predictive is
this model that we've built? And once I understand how predictive it is, then I
can start taking it apart and ask: How does it work? What does it learn? What
are the features that it rendered important?
That's completely true both at The New York Times and at Columbia. One of
the driving themes of my work has been taking domain questions and asking:
How can I reframe this as a prediction task?
Gutierrez: How do you think about whether you're solving the right problem?
Wiggins: The key is usually to just keep asking, “So what?” You've predicted
something to this accuracy? So what? Okay, well, these features turned out to be
important. So what? Well, this feature may be related to something that you could
make a change to in your product decisions or your marketing decisions. So what?
Well, then I could sit down with this person and we could suggest a different
marketing mechanism. Now you've started to refine and think all the way
through the value chain to the point at which it's going to become an insight
or a paper or product—some sort of way that it's going to move the world.
I think that's also really important for working with junior people, because I
want junior people always to be able to keep their eyes on the prize, and you
can't do that if you don't have the prize in mind. I can remember when I was
much younger—a postdoc—I went to see a great mathematician and I talked
to him for maybe 20 minutes about a calculation I was working on, as well as
all of the techniques that I was learning. He sat silently for about 10 minutes
and then he finally said, “What are you trying to calculate? What is the goal
of this mathematical manipulation you're doing?” He was right, meaning you
need to be able to think through toward “So what?” If you could calculate
this, if you could compute this correlation function, or whatever else it is
that you're trying to compute, how would that benefit anything? And that's a
thought experiment or a chain of thinking that you can do in the shower or in
the subway. It's not something that even requires you to boot up a computer.
It's just something that you need to think through clearly before you ever pick
up a pencil or touch a keyboard.
John Archibald Wheeler, the theoretical physicist, said you should never do
a calculation until you know the answer. That's an important way of think-
ing about doing mathematics. Should I bother doing this mathematics? Well, I
think I know what the answer's going to be. Let me go see if I can show that
answer. If you're actually trying to do something in engineering, and you're try-
ing to apply something, then it's worse than that, because you shouldn't bother
doing a computation or collecting a data set or even pencil-and-paper work
until you have some sense for “So what?” If you show that this correlation
function scales to T 7/8 , so what? If you show that you can predict something
to 80-percent accuracy on held-out data, so what? You need to think through
how it will impact something that you value.
 
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