*Anecdotes*:

* Doing object recognition used to be a hard problem. If you need a car detector, you would hire five PhDs and let them work on it for a few years. Now it's a solved problem, even your phone has a strong enough computer to do a good job.

* Doing object recognition used to be a hard problem. If you need a car detector, you would hire five PHDs and let them work on it for a few years. Now it's a solved problem, even your phone has a strong enough computer to do a good job.

* Google voice was unusable for me a few years ago, now it gets my bad English accent.

* DeepMind f*king solved Go. A game which 2 years ago was thought to be 10 years out of reach.

* DeepMind f*king solved Go. A game that 2 years ago was thought to be 10 years out of reach.

* Object detection is real time, one can use it for self driving cars, the radar solution might be getting outdated.
* Baidu is working on speech recognition for mandarin (lots of illiterate people with phones).
* Word embeddings give you natural language models that do away with the tweaks one used to encode all sorts of idiosyncrasies in the English language.

*Shallow field* OpenAI CTO: "As Ilya likes to say, deep learning is a shallow field — it's actually relatively easy to pick up and start making contributions.
* deep learning techniques started to be effective recently (2010? 2012?)

* you can do a lot without being a hard core mathematician
* the field is still a bit of an art so coding contest guys shouldn't feel bad about tweaks and hacks

* previous experience in the old techniques is not very relevant
* a home setup or some AWS resources are enough to start (a google datacenter of GPUs would help)
* state of the art results significantly improving on the previous state of the art

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