Those sorts of goals are better suited to individual hobbyists. OpenAI is a blue sky research project set up by billionaires with the goal of improving all of mankind. I hope I'm not being too uncharitable when I say that your comment reminds me of those who scorned the Apollo program for its ambition.

So these would be human-like bots, rather than bot-like bots, like you normally have in games. The bot would simply learn by doing, until it masters the game, not by getting access to game algorithms.

It did not. It received the state of the board as one array, another board state array for capture/komi (since Go does have global state which is not visible purely from the board representation), and a few additional features to help it out with stuff like ladders. It was architected with convolutional layers, but over the Go grid, not pixels. See the AlphaGo paper pg11 for the exact structure of the input: http://www.postype.com/files/2016/04/08/16/05/03384c91046e8e...

Could it have learned from pixels (augmented by the additional necessary global state)? Sure. But that would've been a waste of computation since the visual layout of a Go board is fixed and static, unlike Atari games.

Source? That literally seems to make zero sense to me. Go can be represented in a super-simple state. Why make it spend millions of cycles learning to categorize pixels into that state you already have?

Instead, researchers have provided the raw feed input data to these agents with the hope that the learned features could be interpreted as game state data by humans.

There isn't an API for me to check if i'm still on the foot path and not the road as i walk down the street.

I can't use an API to tell me water is boiling and that i shouldn't stick my hand in it.