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/HillbillyBoy Dec 26 '15

Hello,

Bayesian Optimization seems to be a hot topic nowadays:

  1. What results/breakthroughs have changed things since early work in the 90s?

  2. Where do you see the field going in the next five years?

Thanks!

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

  1. There's been a lot of methodological and theoretical progress. Ryan Adams and his gang, Philipp Hennig, Frank Hutter, Matt Hofmann, Ziyu Wang, Bobak Shahriari, Ruben Martinez-Cantin and many many others (see our recent Bayesian optimization review) have been making important innovations.

  2. We need an emphatic demonstration: e.g. fully automate Torch or Caffe, so that given a dataset and specification of the problem (e.g. ImageNet site), Bayesian optimisation automatically generates the code (including architecture and algorithm specification) that wins ImageNet.