[IanCal]

That doesn't follow. For you to confuse it, you need to change the inputs. For images, this is fine, we can smoothly change lots of little things. For chess games or go you don't have that freedom.

You can download the weights for LCZero right now though and try out your theory. https://github.com/LeelaChessZero/lc0/wiki/Getting-Started

[CJefferson]

You are right, I should try. I'll see if I can find time in the new year.

I'd prefer to try with a go player, because as you say, in chess it's hard to exactly control the input to the network, it's easier in Go.

[IanCal]

Here's a go setup https://github.com/gcp/leela-zero

There's current best weights available. Not alphazero, but I would expect that issues would be general and so if there are issues with leela zero they may transfer and if you don't see issues with leela zero they're unlikely to exist in alpha zero (at least, if they do they may be very particular to subtle training differences).

Would be very interested to see what you find if you get the chance.

[kzrdude]

You can change the inputs: it depends on when (ply) and which move you play. Some moves are uncommon enough to make it possible for you to uncover something?

[IanCal]

You absolutely can change the inputs, but the point I wanted to make is that unlike images where you can make a human-irrelevant changes you can't really do that with chess or go.

If you want to construct a particular position on the board, you'd likely need to use multiple steps, require the AI to play very particular moves and then the outcome would be a certain move from the AI. Even then, a simple incorrect classification doesn't help all that much, you need your opponent to make repeated mistakes.

I think in reality if you uncovered a type of move it wasn't expecting you are likely to uncover a new strategy in general rather than a trick. Image classification however lets you play uninterrupted with tiny pixel value changes, and you only need a single incorrect output to "win".