That brings us back to this notion that chance is both something that can’t be explained away any further, and yet there’s something deeply human about the desire to create a story to explain why things happen. Computers are now showing us strategies and explanations we never could have arrived at on our own; as you say, they’re outpacing their creators. What are some of the ramifications of that process?
One of the things that really surprised me in writing the book is how quickly these developments are happening. Even the Go victories this year: I think lots of people didn’t expect it to happen that suddenly. And likewise with poker: last year some researchers found the optimal solution for a two-player limit game. Now you got a lot of bots taking on these no-limits stakes games—where you can go all-in, which you often see in tournaments—and they’re faring incredibly well.
In many cases, these poker bots are turning up with strategies that humans would never have thought to attempt.
The developments are happening a lot faster than we expected and they’re going beyond what their creators are capable of. I think it is a really exciting but also potentially problematic line, because it’s much harder to unpack what’s going on when you’ve got a creation which is thinking much further beyond what you can do.
I think another aspect which is also quite interesting is some of the more simple algorithms that are being developed. Along with the poker bots which spend a huge amount of time learning, you have these very high-speed algorithms in gambling and finance, which are really stripped down to a few lines of code. In that sense, they’re not very intelligent at all. But if you put a lot of these things together at very short time scales—again, that’s something that humans can’t compete with. They’re acting so much faster than we can process information; you’ve got this hidden ecosystem being developed where things are just operating much faster than we can handle.
This goes beyond simply teaching bots to play poker or Watson winning at Jeopardy! There are wider ramifications.
Yes. And I think the increasing availability of data and our ability to process it and create machines that could learn on their own, in many ways, it’s challenging some of those early notions about learning machines. Even some of the criticisms and limitations that Alan Turing put forward when they were first coming up with these ideas, they’re now being potentially surpassed by new approaches to how machines could learn.
You have these poker bots, instead of learning to play repeatedly, they’re developing incredibly human traits. Some of these bots, people just treat them like humans: they refer to them in human terms because they bluff and they deceive and they feign aggression. Historically, we think of these behaviors as innate to our species, but we’re seeing now that potentially these are traits you could have with artificial intelligence. To some extent it’s blurring the boundaries between what we think is human and what’s actually something that can be learned by machine.
- Interview with Adam Kucharski author of The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling
One of the things that really surprised me in writing the book is how quickly these developments are happening. Even the Go victories this year: I think lots of people didn’t expect it to happen that suddenly. And likewise with poker: last year some researchers found the optimal solution for a two-player limit game. Now you got a lot of bots taking on these no-limits stakes games—where you can go all-in, which you often see in tournaments—and they’re faring incredibly well.
In many cases, these poker bots are turning up with strategies that humans would never have thought to attempt.
The developments are happening a lot faster than we expected and they’re going beyond what their creators are capable of. I think it is a really exciting but also potentially problematic line, because it’s much harder to unpack what’s going on when you’ve got a creation which is thinking much further beyond what you can do.
I think another aspect which is also quite interesting is some of the more simple algorithms that are being developed. Along with the poker bots which spend a huge amount of time learning, you have these very high-speed algorithms in gambling and finance, which are really stripped down to a few lines of code. In that sense, they’re not very intelligent at all. But if you put a lot of these things together at very short time scales—again, that’s something that humans can’t compete with. They’re acting so much faster than we can process information; you’ve got this hidden ecosystem being developed where things are just operating much faster than we can handle.
This goes beyond simply teaching bots to play poker or Watson winning at Jeopardy! There are wider ramifications.
Yes. And I think the increasing availability of data and our ability to process it and create machines that could learn on their own, in many ways, it’s challenging some of those early notions about learning machines. Even some of the criticisms and limitations that Alan Turing put forward when they were first coming up with these ideas, they’re now being potentially surpassed by new approaches to how machines could learn.
You have these poker bots, instead of learning to play repeatedly, they’re developing incredibly human traits. Some of these bots, people just treat them like humans: they refer to them in human terms because they bluff and they deceive and they feign aggression. Historically, we think of these behaviors as innate to our species, but we’re seeing now that potentially these are traits you could have with artificial intelligence. To some extent it’s blurring the boundaries between what we think is human and what’s actually something that can be learned by machine.
- Interview with Adam Kucharski author of The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling
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