Facebook’s Yann LeCun explains why it lets researchers split their time with academia

Facebook’s chief AI
scientist Yann LeCun.
Facebook

• Facebook’s chief AI scientist, Yann LeCun, says that
  letting AI experts split their time between academia and
  industry is helping drive innovation.
• Writing for Business Insider, the executive and NYU
  professor argues that the dual-affiliation model Facebook uses
  boosts individual researchers and the industry at
  large.
• A similar model has historically been practiced in
  other industries, from law to medicine.

To make real progress in Artificial
Intelligence we need the best, brightest and most diverse minds
to exchange ideas and build on each other’s work. Research in
isolation, or in secret, falls behind the leading edge.

According to Nature Index Science Inc. 2017,
publications resulting from collaborations not just among
academics, which comes most naturally, but between academia and
industry more than doubled from 12,672 in 2012 to 25,962 in 2016.
The burgeoning dual-affiliation model — where academics
actually work inside industry for a time, while maintaining their
academic position — makes possible not only technological
advances like better speech recognition, image recognition, text
understanding, and language translation systems, but also
fundamental scientific advances in our understanding of
intelligence.

Dual affiliation is a boon. It benefits not just the AI
economy but individual academics — both researchers and
students — as well as industry. We need to champion
it.

The Economics of Industry-Academia Collaboration

Worldwide spending on AI systems is predicted to reach
$19.1 billion in 2018, says International Data Corporation. The
number of active AI startups is fifteen times larger than in
2000, per Stanford University. And according to Adobe, the share
of jobs requiring AI is 5.5 times higher than in 2013. Things are
going pretty well and I’m arguing it’s largely thanks to
industry-academia collaborations.

For decades, many professors of business, finance, law, and
medicine have practiced their profession in the private sector
while teaching and doing research at university. A growing number
of leading AI researchers, from colleagues here at Facebook AI
Research (FAIR) to several of my friends at other technology
companies, are embracing a version of dual affiliation. Other
academics, such as my old friend Yoshua Bengio at the University
of Montréal, have not joined corporate research labs but have
played important roles in many companies and startups as advisers
or co-founders.

Facebook CEO Mark Zuckerberg.
AP

The dual affiliation model allows researchers to maximize
their impact. Different research environments lead to different
types of ideas. Certain ideas only flourish in academic
environments, while others can only be developed in industry
where larger engineering teams and larger computing resources are
available.

In the past, true collaborations between industry and
academia were complicated by overly possessive policies regarding
intellectual property — on both sides. But in today’s world
of fast-paced internet services deployment, owning IP has become
considerably less important than turning research results into
innovative products as quickly as possible, and deploying them at
scale. AI researchers establish priority by publishing their
results quickly on open-access repositories such as ArXiv.org.
Many papers are accompanied by open-source releases of the
corresponding code. This practice has increased the rate of
progress of AI-related science and technology and thawed a once
icy relationship. Sharing helps everyone now.

Academia and AI

So investment in basic research in industry, and the
practice of open research, open-source software, together with a
more relaxed attitude towards IP, have made industry-academia
collaborations considerably easier and more fruitful than in the
past. But we must keep pushing. What drives new technologies like
AI is the speed of adoption by the general population, and what
often controls that speed is the number and diversity of talented
people who can apply themselves to the problem. There are only so
many, highly-coveted spots at universities. Meanwhile there’s an
ever-growing need for top-talent in the industry — we’ve
made a great start with great leaders in key positions, but we
need to support — and drive — exponential growth. We
need a deeper bench.

Industry partnerships with academic institutions can help.
They increase the net number of students who can be expertly
trained in AI — giving them the benefit of access to
significant computing power and training data with the
expectation only that they contribute to the field in the future.
The FAIR lab in Paris currently hosts 15 PhD students in
residence, co-advised by a FAIR researcher and a professor.
Ground-breaking research has come out of this program, and I
believe our resident PhD students get a superior research
environment and mentoring than in most purely academic
environments. The program is so successful that we plan to expand
it to 40 students over the next few years. Some students may
choose to join FAIR after graduation, but many will choose to
join other labs, found a startup, or become professors. This is
one way we contribute to the R&D ecosystem.

A file photo of one of
Facebook’s offices.
Stefanie
Loos/Reuters

The goal for this ecosystem is to improve everyone’s
opportunity — not only students, but seasoned academics too.
Just because renowned researchers welcome new opportunities to
participate in research outside of academia, they shouldn’t have
to jeopardize their own careers — which often happened in
the past. Many academics were forced to choose one or the
other.

I spent the first 15 years of my professional career in
industry research at AT&T Bell Labs, AT&T Labs-Research,
and the NEC Research Institute, before becoming a professor at
NYU in 2003. When I joined Facebook in 2013, I was fortunate
enough to be able to keep my professor position and share my time
between FAIR and NYU. My dual affiliation allows me, among other
things, to keep educating the next generation of scientists. The
same holds for a number of academics working at FAIR today
— some 20% of the time, some 50%, and some 80% like me. It’s
also true for the five key research hires we just announced, who
will help build our new Pittsburgh lab and FAIR teams in London,
Seattle, Paris, and Menlo Park. The dual affiliation model hedges
our personal risk while making our research, and knowledge, more
powerful.

Dual Affiliation, Exponential Progress

For us academics, industry affiliation offers any number of
benefits: resources in the form of compute power and funding,
more collaboration with others, and the opportunity for immediate
real-world application of research, at a scale that proves out
hypotheses much faster than in a lab. People think such benefits
must come with an asterisk — that they’ll be expected to be
sucked into the shipping product machine. In the right industry
environments, this simply isn’t the case.

In fact, fundamental research really benefits when it is
untethered from the resource hunt. The dual affiliation model
lets academics control their own agenda and timeline. Freed from
time crunch, they identify research trends in both academia and
in industry, and can act upon whichever’s most promising. They
are not pressured by product groups to bring their research to
application, to achieve “real world impact” the way many
companies with AI-powered products pressure their AI
engineers.

At FAIR, for instance, we want researchers to focus on
long-term challenges. And in the process of working towards
fundamental scientific advances, we often invent new techniques,
develop new tools, or discover new phenomena that turn out to be
useful. More often than not, ambitious long-term projects end up
having product impact much quicker than we thought. Although FAIR
is set up as a basic research lab focused on long-term horizons,
our work has had a large impact on products for such applications
as language translation, image, video and text understanding,
search and indexing, content recommendation, and many other
areas.

Yann
LeCun.
Getty

Some of us in AI are working to solve real-world problems
that impact billions of people by applying image, text, speech,
audio and video understanding, reasoning, and action planning. At
FAIR, we openly share our advances as much as we can, as fast as
we can in the form of technical papers, open source code and
teaching material. We produce new knowledge and tools to educate
people on the latest developments and make science progress
faster.

Others in industry, academia and government can innovate on
top of our work, creating new products, building new startups,
and making new scientific discoveries. Our goals are shared, and
these advances are for everyone’s benefit. The AI software tools
we are producing are used by hundreds of groups for research in
high-energy physics, astrophysics, biology, medical imaging,
environmental protection and many other domains.

I started my professional career at AT&T Bell
Laboratories in the late 1980s, and saw a culture of ambitious,
open research that produced many of the innovations that power
the modern world. These innovations, including the transistor,
the solar cell, the laser, digital communication technology, the
Unix system, and the C/C++ language, had a big impact on
AT&T. But these and many more discoveries and innovations, a
dozen of which won Nobel Prizes and Turing Awards, have had an
even bigger impact on the world at large.

That’s what we are after, with AI. Understanding
intelligence in machines, animals and humans, is one of the great
scientific challenges of our times and building intelligent
machines is one of the greatest technological challenges of our
times. No single entity in industry, academia or public research
has a monopoly on the good ideas that will achieve these goals.
It’s going to take the combined effort of the entire research
community to make progress in the science and technology of
intelligence.

Yann LeCun is Vice President and Chief AI Scientist at
Facebook and Silver Professor at NYU affiliated with the Courant
Institute and the Center for Data Science. He was the founding
Director of Facebook AI Research and of the NYU Center for Data
Science. He received a PhD in Computer Science from Université
P&M Curie (Paris). After a postdoc at the University of
Toronto, he joined AT&T Bell Labs, and became head of Image
Processing Research at AT&T Labs in 1996. He joined NYU in
2003 and Facebook in 2013.

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