An Advocate of Deep Learning
Digital Leadership
Andrew
Ng, chief scientist of Baidu Research, sees artificial intelligence as part of
a larger socially valuable endeavor.
In the field of artificial
intelligence, the phrase deep learning applies to software
that improves its model of reality with experience. Consider, for example, a
project developed at Google in 2012, in which a neural network running on
16,000 computer processors, browsing through 10 million YouTube videos, began
on its own to identify and seek out one of the most popular YouTube genres: cat
videos.
The
then director of that project, Andrew Ng, went on to become the founding chief
scientist at Baidu Research, an innovation center run by the giant Web services
company Baidu. The parent company owns the largest search engine in China,
along with Chinese-language browsers, online encyclopedias, social networks,
and other Web-based services. According to the company, Baidu responds to more
than 6 billion search requests from more than 138 countries every
day. Because search engines and advertising placement platforms (such as
Baidu’s Phoenix Nest) depend on artificial intelligence (AI) to satisfy vague
or ambiguous requests, the company — along with Google, Microsoft, and other
providers of internet guidance — has a natural interest in machine learning.
Thus, Baidu Research, formed in 2014 in Sunnyvale, Calif., is a nexus of
leading global AI research; it contains three facilities: Big Data Lab, the
Institute of Deep Learning, and the Silicon Valley AI Lab.
Cooperation
between human and machine intelligence is a motif in Ng’s career. As a computer
science professor at Stanford University in 2008, he began teaching an online
course in machine learning. This eventually developed into a flagship class in
the university’s much-heralded MOOC (massive open online course) platform.
Enrollment in this and similar classes grew rapidly, reaching more than 100,000
students within a few years. In 2012, Ng and his Stanford colleague Daphne Koller
founded Coursera, an educational technology company that offers students free
online courses from top-tier universities. Ng remains its chairman and has also
kept his affiliation with Stanford.
Ng is a leading advocate of
technological development, speaking regularly about the benefits of having
smart devices in everyday life. These devices can recognize and obey voice
commands; conduct precision operations in agriculture, finance, manufacturing,
and medicine; and prevent motor vehicle accidents. In recent essays on autonomous vehicles, Ng has openly
described Baidu’s plan to put commercial, self-driving cars on the road by
2018, pending expedited regulatory approvals. Citing the way that railroads
were introduced in the 19th century, Ng suggests that policymakers, technology
companies, and government agencies collaborate in public–private partnerships.
The parties could test solutions, modify transportation infrastructure, and
educate the public about autonomous vehicles and how they will affect society.
To address fears about job loss through automation, he favors a similarly
comprehensive approach: a universal basic income, which would provide financial
support for people who committed themselves to being lifelong learners.
Ng sat down with strategy+business after
his talk at the San Francisco Structure Data conference in March. In
conversation, he continually returned to the same theme he had emphasized at
the conference. Companies would benefit most, he said, from an end-to-end
strategy that is optimized for a higher return on value — to the individual, to
the enterprise, and ultimately to the world.
S+B: What is deep learning, and what
value does it bring to an organization?
NG: Deep
learning is a new take on ideas that have been around for decades. People first
began experimenting with artificial neural networks, loosely inspired by the
brain, years ago. But only recently have we had the computing power, data, and
expertise to create networks that learn a hierarchy of concepts in an emergent
manner without guidance or design by a human programmer. [These deep learning
networks] can help extract patterns from, and make sense of, the complex data
inside today’s organizations.
Several
years ago, we saw deep learning beginning to work really well compared with
more traditional AI approaches. Older generations of AI algorithms didn’t know
what to do with all the data we now have.
In
the last five years, we finally have developed the scale of computation needed
to build neural networks that are large enough to exploit the huge amounts of
data that we collect through internet and mobile usage. By building huge neural
networks, we can measure the performance of our deep learning algorithms, and
their performance just keeps going up as we feed in more and more data. This
means we can make far more accurate predictions and models, and use the huge
amounts of data we gather to address problems in companies.
For
quite a few companies, deep learning is driving tremendous amounts of revenue.
Web searches and online advertising are probably the most important short-term
applications. The search results are much more relevant to users, which is good
for advertisers, good for users, and good for us.
S+B: How do you see this evolving
during the next decade?
NG: I
see the leading edge of deep learning shifting toward high-performance
computing. My teams and I have been building very large supercomputers to take
advantage of the huge amounts of data that we have.
Most
of the short-term economic value from deep learning today is what we call
supervised learning. For example: When given an email, the program predicts
whether it is spam. Or given an ad, it predicts whether the user will click on
this ad. We’ve already created tremendous value from supervised learning. And I
think we’re on a clear path over the next several years to create amazing
amounts of additional value.
But
looking a little further out, I see many projects, such as image recognition
and speech recognition, where deep learning is already making tremendous
progress. These will not just drive huge economics for companies, but also help
us make the world a much better place.
Just
by developing deep learning for self-driving motor vehicles, we can make travel
much safer and more convenient, and add years to almost everyone’s lives in
terms of both life expectancy and time saved. Depending on whose numbers you
believe, the risk of car accidents may shorten our children’s average life
expectancy by three years. People in the U.S. also spend an average of three
years in a car over their lifetime. Potentially, we can give back six years of
worthwhile time to an individual. That seems like a big goal.
S+B: You’re talking about self-driving
cars. Are those really feasible anytime soon?
NG: Yes.
I hope that we’ll be able to commercialize self-driving cars in about three
years and mass-produce them in five years. These time frames are difficult to
predict, but this is our best guess right now.
When
I look at the state of self-driving cars today, an analogy [to today’s
human-driven cars] seems apt. When your car drives into a construction zone, it
must behave differently than when it’s on a regular street. It must drive more
slowly, and pay attention to construction workers.
I
don’t see it being feasible in the near term for computer vision to reliably
distinguish among the hand gestures (like stop, go, and slow down) made by
construction workers. But we could solve the problem by making modest
infrastructure changes. Give the construction worker a wireless beacon. With
modest changes to societal expectations and the way we design and build the
roads, we can make self-driving cars a reality. And they will be much safer
than human-driven cars.
I
want to build a better society with AI, with intelligence embedded throughout
the environment’s devices. Speech controls today are like touch screens were in
the early 2000s: too rudimentary to be effective. But with the development of
the iPhone, Steve Jobs and Apple figured out how to make the touch screen work
well. Speech controls will have an equally transformative effect. They will
affect every aspect of society. I imagine robot security guards and robots that
follow spoken instructions. Someday I hope to have grandchildren who are
mystified at how, back in 2016, we would have to adjust the dial on our
thermostat, instead of simply telling the house that we were cold.
S+B: You’ve been involved in four major
organizations: Stanford, Coursera, Google, and now Baidu. As somebody who has
been thinking about deep learning, how do you think we can make a better
corporate algorithm?
NG: I’ve
spent a lot of time trying to design a research organization that is
responsible for making sure that the technology it invents can really help
hundreds of millions of people. We thus refer to Baidu Research as an
end-to-end research organization. For example, we have people inventing neural
networks, and then figuring out where the data they use will come from and how to
incorporate it into products. And, ultimately, how this will improve daily
life.
At
Coursera, some of my proudest moments happened when top management would face a
tough decision, and someone else, not me, would stand up and say, “Let’s go
back to basics. Let’s figure out what’s best for learners and do that first.”
I
really value it when people step back to think that way. Too many individuals
get caught up in doing something because they’ve always done it that way or
because others do the same thing. They end up with activities that look useful,
but that may not really accomplish much.
S+B: To positively affect the world
with AI and technology, humans need to trust the systems. But trust in large
systems is eroding. How do you face that issue?
NG: I
think it’s important that we step up to helping government and society address
some of the problems associated with AI. For example, one of the biggest
challenges is job displacement. As technologists, we should speak openly about
that. There are 3.5 million truck drivers in the United States.What will self-driving
vehicles do to them?
Similarly, what will AI do to specialists in medical imagery? Or to some roles
in agriculture? Few people have started to plan for this future.
Generally,
new technology creates new, meaningful roles for people, and that will probably
happen this time as well. But there is a problem in the interim period. That’s
why I support the idea of a guaranteed basic income for people who cannot find
jobs. But we should require people who receive that income to study and keep
learning. If we could pay the unemployed to gain skills, it would be better for
them and for society. The world is changing faster than ever before, and in order
to keep up, it’s important that everyone keep learning.
endeavor.
by Juliette
Powell
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