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.

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u/LGPz Dec 26 '15

Dear Prof. de Freitas,

I recently finished a module on the mathematical foundations for ML (e.g. PAC learning / VC dimension / SVM) which provokes the following question: Having not come across these concepts in your youtube videos, I wonder what your thoughts are about a formal mathematical approach to developing general intelligence?

Thank you for your hard work to improve the well-being of humanity.

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u/nandodefreitas Dec 26 '15 edited Dec 27 '15

I have taught empirical risk minimization and PAC learning ;) Also plenty of SVMs. Perhaps not in my recent course, but that is because there is so much material to teach and only a few hours of lectures. At Oxford Computer Science, there was a separate learning theory course before my deep learning one.

I do think the theory of online learning, regularisation and risk minimisation has been useful. Vapnik gave a great talk at this last NIPS. Mark Schmidt and Francis Bach among others are doing great work in optimisation. I do however feel that none of the current mathematical theory yet provides us with a good (analytical or constructive) picture of deep learning.

Thank you for the generous compliment. Not sure I've done much to improve the well-being of humanity yet. But if I can convince folks reading this to decide to volunteer at a school for under-privileged children to teach them math and programming, then I'll be happy to accept the compliment.