[joe_the_user]

AutoML-Zero aims to automatically discover computer programs that can solve machine learning tasks, starting from empty or random programs and using only basic math operations.

If this system is not using human bias, who is it choosing what good program is? Surely, human labeling data involves humans adding their bias to the data?

It seems like AlphaGoZero was able to do just end-to-end ML because it was able to use a very clear and "objective" standard, whether a program wins or loses at the game of Go.

Would this approach only deal with similarly unambiguous problems?

Edit: also, AlphaGoZero was one of the most ML ever created (at least at the time of its creation). How much computing resources would this require for more fully general learning? Will there be a limit to such an approach?

[darawk]

> It seems like AlphaGoZero was able to do just end-to-end ML because it was able to use a very clear and "objective" standard, whether a program wins or loses at the game of Go.

Just a fun note: winning or losing at the game of Go is actually surprisingly subjective:

https://en.wikipedia.org/wiki/Go_(game)#Scoring_rules

[pmontra]

The game ends by agreement of the players. If they don't agree on the result ("those stones are alive!") they must keep playing. Chinese rules are much better at this than Japanese ones especially (IMHO) the old ones with the group tax. There are no ambiguities there. Unfortunately the group tax is unpleasant and Chinese rules are a pain to score manually. Japanese rules are full of flaws but are such a nice shortcut that almost everybody except China use them or some variant of them.

Btw, if any Chinese player is reading this, how do you count the score while playing? Do you count territory and remember the number of captured stones or do you count both stones and territory? Thanks.

[darawk]

> The game ends by agreement of the players. If they don't agree on the result ("those stones are alive!") they must keep playing. Chinese rules are much better at this than Japanese ones especially (IMHO) the old ones with the group tax. There are no ambiguities there. Unfortunately the group tax is unpleasant and Chinese rules are a pain to score manually. Japanese rules are full of flaws but are such a nice shortcut that almost everybody except China use them or some variant of them.

Ambiguities? No. Subjectivity? Yes.

[mlyle]

No, not really. Under Chinese rules, eventually it will reach a clean, objectively scored state. Of course, human players will agree on the score before this point.

[Recursing]

Different scoring rules agree on the winner in >99% of cases

[westurner]

Is the question "Does AutoML-Zero minimize or maximize a cost function with error as a primary component, instead of using a binary win/lose classifier like AlphaGoZero?"

https://en.wikipedia.org/wiki/AlphaZero