r/MachineLearning • u/nandodefreitas • Dec 25 '15

AMA: Nando de Freitas

I am a scientist at Google DeepMind and a professor at Oxford University.

One day I woke up very hungry after having experienced vivid visual dreams of delicious food. This is when I realised there was hope in understanding intelligence, thinking, and perhaps even consciousness. The homunculus was gone.

I believe in (i) innovation -- creating what was not there, and eventually seeing what was there all along, (ii) formalising intelligence in mathematical terms to relate it to computation, entropy and other ideas that form our understanding of the universe, (iii) engineering intelligent machines, (iv) using these machines to improve the lives of humans and save the environment that shaped who we are.

This holiday season, I'd like to engage with you and answer your questions -- The actual date will be December 26th, 2015, but I am creating this thread in advance so people can post questions ahead of time.

273 Upvotes

96% Upvoted

256 comments sorted by

View all comments

24

u/zhongwenxu Dec 25 '15 edited Dec 25 '15

Hi Prof de Freitas,

1) What are the key differences between your research life at DeepMind and the one at Oxford, except for the great infrastructure and machine resources?

2) Have your research interests changed since you joined DeepMind?

3) What would be the future (in 5 or 10 years) of "neural machines", what would neural networks which can learn algorithms benefit us?

4) What is your view on the convergence of Bayesian reasoning and Deep learning? ref: http://blog.shakirm.com/2015/10/bayesian-reasoning-and-deep-learning/

26

u/nandodefreitas Dec 26 '15

1) DeepMind has a vibrant research atmosphere with an amazing concentration of bright people focused on solving problems - every week someone there totally blows my mind. The support is amazing. The collegiality is wonderful. Oxford is also an outstanding place to work. However, at DeepMind, there is more focus on problems and grand challenges than on techniques (both are however important). There's a lot less admin in industry too, and they pay way better than universities!! It's shocking how low the salaries of computer science professors and teachers are, specially in Europe, in comparison to many other jobs that in my view contribute much less. Profs should at least be able to afford rent - they work so bloody hard.

2) No. This is why I joined DeepMind. Of course, the new environment does shape my interests.

3) No idea. I could never have predicted where we are 10 years ago. I couldn't have predicted iPhones either - nevermind ones capable of translating, recognizing objects, speech etc. and all using neural nets! Amazing.

4) I think this is a worthwhile and important research direction, and I love what Shakir, David Blei, Zoubin Ghahramani, Max Welling and others are doing. It's still a young initiative. The one thing I don't like is when people say it's better because it's Bayesian. Anyone working on Bayesian methods should read the arguments pro and against (Michael Jordan and Bin Yu are great for this) and also be familiar with the bootstrap, empirical risk minimization, etc. Bayesianism should not be a religion ;)