Tony Jebara is a Professor of Computer Science at Columbia University
and Director of Machine Learning Research at Netflix
. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, spatio-temporal data, vision and text.
At the Deep Learning Summit in Boston
last month, Tony presented 'Double-Cover Inference in Deep Belief Networks'. I caught up with him to hear more about his work at Netflix and his thoughts on the recent advancements in deep learning.
Tell us more about your work as Director of Machine Learning Research at Netflix.
At Netflix we are inventing the future of Internet television and helping members across the world find videos to watch and enjoy. We help them make a selection from a catalog of thousands of titles. But we need to tailor recommendations to each user and each session within seconds and within a menu of 10 to 20 visible options. Achieving this relies on our recommendation system which is really an ecosystem of many machine learning algorithms that operate together. We are constantly working on improving these algorithms. We are also
leveraging machine learning across all parts of Netflix: from deciding which new titles to add to our catalog to finding ways to more efficiently stream videos across the Internet.
What do you feel are the leading factors enabling recent advancements and uptake of deep learning?
The recent adoption of deep learning has been enabled by the confluence
of several factors. Of course, bigger data-sets and more computational
power have been essential. But, they triggered something more
important: the freedom to try bigger and deeper models. Recent
theoretical research by Choromanska et al shows that models
with more layers and more neurons per layer become resilient to the
local optima that plague the optimization landscape.
Bigger models find more reliable solutions during learning
while small ones get stuck in bad solutions. So, it's been a chain
reaction: bigger computation led to bigger data, then to bigger
models, then to better optima, and finally to better performance.
What are your thoughts on the recent surge of media interest surrounding deep learning?
The media interest certainly adds to the excitement. The uptake in
press and articles has often revolved on deep learning shattering AI
milestones in areas such as game-playing, computer vision and so on.
But more practical progress is happening in business and commercial
fronts where deep learning and machine learning are permeating almost
every component of the workplace. So, beyond exciting milestones in
the media (such as beating the grandmaster of Go), we are seeing a
sustained ground swell in deep learning at companies all over the
How can larger corporations working on deep learning ensure that their work benefits others within this field?
Corporations are increasingly part of the conversation and now have a
much stronger presence at leading conferences in machine learning and
deep learning. They are not only providing funding and exhibit booths at
the events but are also contributing papers and organizing
workshops. Some are even releasing open-source software and systems (such as Google's TensorFlow) which
broaden the reach of deep learning to anyone in the world who wants to
What present or potential future applications applications of deep learning excite you most?
I'm excited to see how deep learning can help in recommendation,
personalization and search. So far, we've seen deep learning solve
tasks that humans are already good at, such as vision, speech
recognition, gaming or natural language processing. But humans are
notoriously bad at recommending content or items to their friends. We
think aspirationally rather than realistically; we recommend a
high-brow documentary that sounds intellectual rather than what our
friends really would rather watch. I'm excited to see how deep
learning can anticipate these bias and preferences and help us each
optimize our entertainment, our disposable time and our everyday life
What developments can we expect to see in deep learning in the next 5 years?
I expect the science behind deep learning to become much more rigorous
from a theoretical perspective. Right now, we are building complicated
models and getting great results without knowing quite why the systems
are working well. The field is permeated with black-art intuitions and
cryptic engineering know-how. However, in the next 5 years, we will
have a better mathematical explanation of why deep learning works so
well and what the limitations are. This theoretical understanding will
be critical to enable the next generation of break-throughs in machine
Tony Jebara spoke at the Deep Learning Summit in Boston on 12-13 May 2016. Join us at the next Deep Learning Summit on 22-23 September in London. Discounted Super Early Bird tickets end this week on Friday 27 May! Previous events have sold out in advance so book early to avoid disappointment. Visit the event page here.
Future AI & Machine Learning focused events of 2016 include:
Machine Intelligence Summit, Berlin, 29-30 June 2016
IoT Meets AI Dinner, London, 15 September 2016
Deep Learning in Finance Summit, London, 23 September 2016
Deep Learning Summit, London, 22-23 September 2016
Deep Learning Summit, Singapore, 20-21 October 2016
View all upcoming RE•WORK summits here.
Deep Learning Summit
Deep Learning Algorithms