AI wrangles torrents of information
By Maria Temming
in a typical network keep studying all
incoming information. When the network sees a cat photo, for example, even
neurons responsible for noticing trucks
pay attention. That’s unnecessarily time-and energy-consuming, Shrivastava says.
In graduate school, Shrivastava found
a way to identify and
activate only the neurons most relevant to
each input. He used
hash functions, computational tools that
organize records in
databases much like
the Dewey Decimal
System organizes
books in a library.
Shrivastava fashioned a set of hash
functions to organize
and quickly locate virtual neurons in a network based on their
relevance to a given input — so you could
find all the cat neurons and ignore the
truck neurons.
“I was thinking about this problem
for more than two years,” he says. “You
keep all your hard problems in the back
of your head.” He’d return to this one and
usually get nowhere. But the
day the path to an answer
came to him, he solved it in
a couple of hours. He recalls
sitting in his bedroom, reading and rereading his solution to convince himself it
would actually work.
The system he came up
with may be considered
“the best research work
in machine learning in that year,” says
Moshe Vardi, also a computer scientist
at Rice. The work won the Outstanding
Paper Award at the 2014 Conference on
Neural Information Processing Systems.
Since then, Shrivastava has built an
image-classifying neural network that
works about as well as standard networks,
A
.
S
H
R
I
VAS
TAVA
The world is awash in data. Anshumali
Shrivastava may save us from drowning
in it.
Every day, over 1 billion photos are
posted online. In a single second, the
Large Hadron Collider can churn out a
million gigabytes of observations. Big
data is ballooning
faster than current
computer programs
can analyze it.
“We have this
huge ocean of data,”
says electrical and
computer engineer
Richard Baraniuk at
Houston’s Rice Uni-
versity, “and we’ve
got to suck it out
through a garden
hose.”
So Shrivastava, 33,
a computer scientist
at Rice, is designing a
new generation of artificial intelligence
programs to efficiently process floods of
information.
“He’s very creative” in his strategies
to wrangle unwieldy datasets, says Piotr
Indyk, an electrical engineer and computer scientist at MIT. “Some of these
things I say, ‘I wish I came up with that.’
They’re clear, beautiful and they work.”
Shrivastava got into artificial intelligence because number-crunching
algorithms that solve real-world problems are “where you see math in action,”
he says. But as a Ph.D. student in computer science at Cornell University,
Shrivastava realized how inefficient artificial neural networks, today’s premiere
AI programs, really are.
Neural networks are made of pieces of
computer code called artificial neurons.
To learn a task such as image recognition, an AI network might study labeled
images, with each of the artificial neurons
in the network gaining expertise at recognizing certain patterns.
But even as they specialize, all neurons
but uses 95 percent fewer computations.
Such efficiency could free up time and
energy for an AI program to process
other information, for instance, audio
for speech recognition, paving the way
for more versatile artificial intelligence.
He has also developed other ways to
streamline computation since joining
the Rice faculty in 2015. He’s “incred-
ibly bright and incredibly fast,” Vardi
says. “We sometimes have to run after
him because his mind is racing ahead.”
Shrivastava and colleagues at Rice
and Duke University recently applied
hashing to databases of Syrian civil war
victims. Getting an accurate death count
to help prosecute perpetrators of crimes
against humanity has proved difficult.
Databases of victims reported by family
members, the media and other sources
contain duplicate records; it would take
a computer more than a week to compare
all 354,000 records to each other to find
repeats. Once Shrivastava’s computer
program assigned each record in four
victim databases a hash code, it used
those codes to identify likely duplicates
in just a couple of minutes. The pro-
gram, reported in June in the Annals
of Applied Statistics, then checked only
those records for matches.
Closer to home, Shrivastava and col-
leagues created a smartphone app for
navigating shopping malls or other
large buildings based on photos
of a person’s surround-
ings. The app distilled
user-taken photos into
hash codes to compare
with reference photo
codes, pinpointing loca-
tions within two seconds.
With the flood of big
data growing, it would
be easy for Shrivastava
to get overwhelmed and
discouraged. Fortunately, “there’s not a
glum bone in his body,” Baraniuk says.
Shrivastava might stall on a particular
problem for months before getting the
kind of brain blast that led to his hash-
based eureka moment. But when he can
kick a slow-moving computer system into
high gear, he says, “that’s worth it.” s
Anshumali Shrivastava, 33
COMPU TER SCIENCE
RICE UNIVERSI T Y
We sometimes
have to run
after him
because his
mind is racing
ahead.
MOSHE VARDI
