INTERVIEW

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Kevin Ferguson

Six Questions for Kevin Ferguson, co-author of Deep Learning and the Game of Go.

Kevin Ferguson and Max Pumperla are deep learning specialists skilled in distributed systems and data science. Together, they built the open source bot BetaGo. They also both count Max, the hero of the movie Pi, as a major influence. “He’s a talented mathematician who slowly loses his mind over the stock market and has an intense relationship with his power tools. That’s essentially my short bio,” says Pumperla.

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Which came first, the book or the BetaGo bot?

How did you get interested in Go? Did you play it before you built the bot, or did you just see it as a great use case for deep learning?

With the Sedol Lee upset in 2016 as big a shock as Kasparov v Deep Blue in chess?

Before the big match against Lee, DeepMind (the bot developers) held five test games against Fan Hui, a Chinese pro who lived in France. Fan was maybe the top player in Europe at the time, and AlphaGo won all those games. What Go players understood is that Fan Hui is a very strong player, but Sedol Lee is on a completely different level. If you know chess, it’s like the difference between and IM and a Super-GM. So a lot of Go players studied the Fan Hui games, and concluded: this is the strongest Go AI yet, but it has some weaknesses, and Sedol Lee is going to exploit those weaknesses in a way that an average pro can’t.

But the big unknown was how much AlphaGo could improve in the six months of training between the two matches. The answer was a lot: by DeepMind’s estimates, the Sedol Lee version of AlphaGo was something like 600 Elo points stronger than the Fan Hui version.

Is it true that Go is a harder challenge for a computer than chess, as it requires intuition and creativity as well as intelligence? How does deep learning mimic these most human of qualities?

The power of deep learning is that it lets computers learn how to do something very similar to chunking. When training a deep learning model, two things happen simultaneously: first it’s learning to organize the raw input into a structured representation; and second it’s learning to make decisions from that representation. And that lets a computer deal with unstructured inputs in a way that was not possible before.

Comparing the Go bots to Deep Blue, how has AI has changed in the past 22 years? Was Deep Blue using a version of Deep Learning?

Both of these accomplishments are pretty amazing and I think there’s a lot worth studying in how modern chess engines work. But the neat thing about the AlphaGo-style tree search is that it’s more similar to how expert humans play. Top players can read out sequences better than amateurs, but you’re talking about 2 to 3x faster, not 1000x faster. But top humans are much better than amateurs at judging which moves are worth looking at.

Is your book all fun and games, or does building a Go bot teach any skills developers can use at work?

Originally published at https://freecontent.manning.com.

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