JE: What did it feel like to lose to Deep Blue?
GK: Losing is always bad. So it's for me, I mean bad feelings. For me, it was just a first loss in the match, period. Not machine, just in chess. And in my book Deep Thinking I confess a few times I was a sore loser and very upset and I had all the criticism about the way IBM organised the match. I am still sticking to some of my criticism, there I gave in my book a lot of credit to IBM's scientific team. It's water under the bridge already.
JE: It's a moment in history ...
GK: It's a moment in history though it was a brute force, not a human-like machine, a type B machine, that supposedly had to triumph in the game of chess. But as we discovered a game of chess was vulnerable to very powerful machines with sufficient algorithms and bigger databases, and very high-speed processors. So when you look at Go [the game], it's already a different kind of machine, this is a deep learning programme. So it's not that machine that relies on heavily or exclusively on brute force. It's still brute force but you have elements of deep learning.
And while many people are just very impressed that AlphaGo destroyed the best Go players — and then AlphaGo Zero, that had no information except the rules, destroyed AlphaGo — for me, it just shows how miserable the human knowledge of the game Go is. Go is very complex. What the best Go players know about Go today probably reflects what the best chess players knew about chess 200 years ago. And if you show the games, modern chess games, to the greatest chess players of the nineteenth century, they will be shocked because that will go way beyond their imagination.
But let me go back to this point. It's a closed system, so in any closed system, machines will always dominate. Anything that is quantifiable machines will do better than humans and we should not make an assumption that you could automatically transfer the knowledge from the closed system to an open-ended system.