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P H O T O B Y L I N D A A . C I C E R O / S T A N F O R D N E W S S E R V I C E

sets would help computers make better decisions. This prompted you to build ImageNet, a hierarchically organized image database in which each node of the hierarchy is depicted by hundreds and thousands of images.

In the field of AI, there are a few important problems that everyone works on; we call them ‘holy grail’ problems.

One of them is understanding objects,
which is a building block of visual in-
telligence. Humans are superbly good
at recognizing tens of thousands and
even millions of objects, and we do it
effortlessly on a daily basis. So I was
working on this problem when I was
a Ph.D. student and in my early years
as an assis-
a Ph.D. in a combination of cognitive
neuroscience and computer vision—
we didn’t call it AI at that point.

This was during one of the so-called AI
winters, when interest and investment
cooled as people realized technologies
had failed to live up to their hype.

While I was studying for my Ph.D., it was a very interesting time. Machine learning became a very important tool in computer vision, so I was in the generation of students who got a lot of exposure and training in that subject.

That training helped crystallize an in-
sight that proved pivotal to the field of
AI, namely that creating better data-
THOUGH STANFORD UNIVERSITY profes-
sor Fei-Fei Li began her career during
the most recent artificial intelligence
(AI) winter, she’s responsible for one
of the insights that helped precipitate
its thaw. By creating Image-Net, a hier-
archically organized image database
with more than 15 million images, she
demonstrated the importance of rich
datasets in developing algorithms—
and launched the competition that
eventually brought widespread atten-
tion to Geoffrey Hinton, Ilya Sutskever,
and Alex Krizhevsky’s work on deep
convolutional neural networks. Today
Li, who was recently named an ACM
Fellow, directs the Stanford Artificial
Intelligence Lab and the Stanford Vi-
sion and Learning Lab, where she
works to build smart algorithms that
enable computers and robots to see
and think. Here, she talks about com-
puter vision, neuroscience, and bring-
ing more diversity to the field.

Your bachelor’s degree is in physics
and your Ph.D. is in electrical engineer-
ing. What drew you to computer vision
and artificial intelligence (AI)?

When I was an undergrad at Princeton, I had a lot of academic freedom. By sophomore year, I was already fascinated by the writings of physicists from the early 20th century—people like Schrödinger and Einstein who, in the later part of their careers, all had a lot of curiosity about life and intelligence. Then I did a couple of research projects related to neuroscience and modeling; I was hooked. I decided to pursue

DOI: 10.1145/3303853 Leah Hoffmann

Q&A
Guiding Computers,
Robots to See and Think

Fei-Fei Li, co-director of Stanford University’s Human-Centered AI Institute, wants to create algorithms that can learn the way human babies do.

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