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The funny thing about AI at this scale is we don't really know why the computer does what it does. It's more of a inductive extrapolation that we can verify th

The funny thing about AI at this scale is we don't really know why the computer does what it does. It's more of a inductive extrapolation that we can verify that a technique works for a small problem, so we'll throw a whole bunch of GPU power and data at it and it SHOULD work for a big problem. How it actually works is fuzzy though as there's just a couple of gigabytes of floats representing weights in neural networks. No human can look at that and say: "Oh! I see why it made that move". It's so much data that it becomes kind of nebulous what the AI is doing.

gearhart 11y ago
It certainly seems incomprehensible now, but that isn't necessarily true in neural networks - the amazing thing about some of the experiments using the results of intermediate layers in image recognition is that they seem to be building up higher and higher order "understanding" as you get to deeper and deeper layers which correlates directly with how a human might explain their strategy. You can imagine a means of interpreting intermediate layers of alphago's weighting function similar to the second image in [1] (not the best example, I apologise) that would produce images or other abstract representations of the strategy that layer was encoding, similar to how a human might classify moves or patterns into categories. [1] http://cs231n.github.io/convolutional-networks/ http://cs231n.github.io/convolutional-networks/
- narrator 11y ago
  There's a name for this phenomenon: "Subsystem inscrutablity". See this presentation on Alpha Go linked to from here: http://nextbigfuture.com/2016/03/what-is-different-about-alphago-versus.html?m=1 http://nextbigfuture.com/2016/03/what-is-different-about-alp...
panic 11y ago
The neural net guides the search for the right move, but the program ultimately considers many possible sequences and picks the one it thinks is best. The program could justify its choice in the same way human players do: by playing out alternative choices and showing how those choices would have put it into a worse position.
fma 11y ago
Turn on the debugger and step through it!
Florin_Andrei 11y ago
> No human can look at that and say: "Oh! I see why it made that move". Well, it depends. During the game last night there were quite a few forced moves that require an immediate and unique reply. The motivation for the reply is quite clear. But that's the simple stuff.
chillacy 11y ago
Seems to be similar for intuition in humans–some moves just feel right, and they're the result of thousands of hours of experience. You can justify it afterwards, but the intuition itself usually comes first. The AI google designed is architected similarly to how the brain works in dual process theory: you have a NN providing intuition like system 1 and a supervisor much like system 2 which double checks system 1 https://youtu.be/l-GsfyVCBu0?t=52m10s https://youtu.be/l-GsfyVCBu0?t=52m10s

gearhart 11y ago

It certainly seems incomprehensible now, but that isn't necessarily true in neural networks - the amazing thing about some of the experiments using the results of intermediate layers in image recognition is that they seem to be building up higher and higher order "understanding" as you get to deeper and deeper layers which correlates directly with how a human might explain their strategy. You can imagine a means of interpreting intermediate layers of alphago's weighting function similar to the second image in [1] (not the best example, I apologise) that would produce images or other abstract representations of the strategy that layer was encoding, similar to how a human might classify moves or patterns into categories. [1] http://cs231n.github.io/convolutional-networks/ http://cs231n.github.io/convolutional-networks/

narrator 11y ago
There's a name for this phenomenon: "Subsystem inscrutablity". See this presentation on Alpha Go linked to from here: http://nextbigfuture.com/2016/03/what-is-different-about-alphago-versus.html?m=1 http://nextbigfuture.com/2016/03/what-is-different-about-alp...

panic 11y ago

The neural net guides the search for the right move, but the program ultimately considers many possible sequences and picks the one it thinks is best. The program could justify its choice in the same way human players do: by playing out alternative choices and showing how those choices would have put it into a worse position.

fma 11y ago

Turn on the debugger and step through it!

Florin_Andrei 11y ago

> No human can look at that and say: "Oh! I see why it made that move". Well, it depends. During the game last night there were quite a few forced moves that require an immediate and unique reply. The motivation for the reply is quite clear. But that's the simple stuff.

chillacy 11y ago

Seems to be similar for intuition in humans–some moves just feel right, and they're the result of thousands of hours of experience. You can justify it afterwards, but the intuition itself usually comes first. The AI google designed is architected similarly to how the brain works in dual process theory: you have a NN providing intuition like system 1 and a supervisor much like system 2 which double checks system 1 https://youtu.be/l-GsfyVCBu0?t=52m10s https://youtu.be/l-GsfyVCBu0?t=52m10s