[blamestross]

If the universe is a game you are playing, then yes playing that game is "experience", but for an AI to engage with reality it has to have experience in reality, not a game. The ability to play go very well doesn't enable an AGI to better understand reality.

> The experience is the data in Reinforcement Learning.

This is very true, and the critical problem. Data about how reality responds to an AI's actions is very sparse right now.

AIs do have a potential advantage in communications efficiency, but at some level of scale compression will happen, locally "irrelevant" data will be discarded and simplified approximations replace it. None of this will change the "big O" of the scalability of intelligence, just the constant factors. There is no exponential kickoff point.

[namedlambda]

What is the difference between experiencing reality and a game?

The difference I can see is that there is no one explicit objective function, but this doesn't stop generally capable agents [1], and doesn't imply that inverse RL is not possible.

> The ability to play go very well doesn't enable an AGI to better understand reality.

I disagree, model based RL constructs a model of the agent's reality and can use it to plan ahead, train the agent, or do some form of monte-carlo tree search. The latter is something very similar to how we imagine and think about the future.

[1] https://deepmind.com/blog/article/generally-capable-agents-e...

[blamestross]

I'm in total agreement about the potential of growing AGI out of these methods, but there will be bottlenecks well before the gods of the singularity come knocking.

[blamestross]

> What is the difference between experiencing reality and a game?

Finding out the consequences of an action is a lot more expensive in reality than a simulation of a game.

[namedlambda]

There is nothing fundamentally different between an infinite horizon game and reality.