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Comment by vbarrielle | Hacker News Reader

Lots of points can be reduced to the ability to simulate a physical environment very fast, and much faster than the actual occuring of events. But it doesn't look like it's easy to simulate physics at high speeds, let alone faster than what happens in our environment. Therefore we are bound by our environment and our limited ability to simulate it.

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This is probably a good criticism if it turns out that the right level of abstraction for most problems is Physics. And it seems like your argument would apply equally well against the idea of human intelligence. Luckily, our minds have developed other abstractions that allow us to solve problems much faster than if we had to simulate them as physics problems. For example, I don't need a physics-level simulation of my friend's brain when I want to predict how they'll react to a gift I'm giving them.

You're right, there are lots of problems where a simpler abstraction is possible.

But I don't think my argument applies to human intelligence, it just means that human intelligence is what you can get with all the data points you can get by observing the world (and some simulation done by our brains, but I'm under the impression that our brains don't perform accurate simulation, looks more like heuristics).

> simulate a physical environment very fast

That's probably only a problem if it is must faster than everbody else.

> let alone faster than what happens in our environment

That is often not very hard. When a bottle rolls off the table, you can catch it by approximately predicting it's trajectory without computing the precise evolution of the ~10^26 atoms that make up the water bottle. Compression is a corner stone of intelligence. The second corner stone is using compression to choose actions that maximize expected cumulative future reward.

One big advantage with machine intelligence is that you can train many agents simultaneously in many environments, letting the agents share what they learn. Even if you were required to train the agents outside simulated environments, their shared learning would allow for more rapid collective progress.

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