Last week, from June 24th - 26th, Google held its Google Cloud Next ‘18 conference at the Moscone Center in San Francisco. It was Google’s biggest event ever, with 25000 people attending.

In the previous post, I gave an intuitive view of the workings of reinforcement learning, and worked through how the learning process would play out for tic tac toe and chess. Now, let’s get to the really interesting stuff - Go!

In 2014, Google acquired a British startup named DeepMind for half a billion dollars. A steep price, but the investment seems to have paid off many times over just from the publicity that DeepMind generates. ML researchers know DeepMind for its frequent breakthroughs in the field of deep reinforcement learning. But the company has also captured the attention of the general public, particularly due its successes in building an algorithm to play the game of Go. Considering the frequency with which DeepMind makes progress in the field - AlphaGo, AlphaGo Zero and AlphaZero in the last couple of years - it becomes hard to keep track of what exactly is happening, both at a technical level and from the point of view of implications. I plan to do just that - provide a high-level view of DeepMind’s successes in Go, and explain the distinctions between the different versions of AlphaGo that they have produced.

After spending 1.5 years in an MS programme at CMU, I’ve decided to move into an applied ML role in industry instead of continuing on to a PhD. I’ve had a lot of discussions with people about the case for and against a PhD, and I thought it’d be useful to explain my thought process in deciding definitively against a PhD. While I’ve tried to be objective in listing out the arguments for and against, at the end of the day the choice of which arguments outweigh the other is obviously a deeply subjective one, so I want to emphasize that the focus of this post is to explain why I decided against a PhD.