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Do you have a meaningful counterargument to Wolfram's computational irreducibility thesis, or have you not considered the issue at all?
Kurzweil style number-go-up arguments are fun Whig catnip, but they completely handwave away crucial details. There's a reason Malthus' predictions ended up being wrong despite being mathematically sound.
Considering that wolfram alpha is now 100% obsolete due to AI advancements maybe he isn't the best sword to bring to this gunfight.
Yes he has an idea about complex systems needing to be fully simulated and in that way becoming the very thing they simulate. But guess what! Computational reducibility is a thing too, and it works really really well! Human brains do it every day even!
Bringing Malthus into this is also an interesting choice, considering he was wrong precisely since he underestimated the pace of technological change.
I'll take that as a no, since you restrict yourself to discussing the context of the argument instead of its substance.
Computation of complex systems is not irreducible. It can also be run 1 billion times faster than real time even if you wish to maintain all parameters. I do not see it as an impediment to AI development.
Is that clear?
It is.
And what leads you to believe this? Specifically in light of his examples.
We can simulate the weather for my town tomorrow with pretty damn good accuracy. Enough accuracy that it is VERY useful. Without building a computer the size of universe.
How about the weather in your town in a month? Or that same weather if you lived on the Moon where Navier-Stokes doesn't work because you're in a vaccum?
If your argument is that computational irreducibility is not relevant because we have found heuristics for certain problems, it seems to miss the point that the whole postulate is that a lot of important problems are not like the weather in your town tomorrow and do not have a convenient heuristic or general solution. And hence that throwing more compute at them won't really do anything.
I'm certain having more compute around will solve some problems, though we are sure to hit diminishing returns, but it seems weird to me to assume that it will solve all problems as you seem to imply.
Have you seen the new AI weather predictors? They can do what it took a supercomputer days to do in minutes on a laptop.
For inputs, GraphCast requires just two sets of data: the state of the weather 6 hours ago, and the current state of the weather. The model then predicts the weather 6 hours in the future. This process can then be rolled forward in 6-hour increments to provide state-of-the-art forecasts up to 10 days in advance.
https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/
This is just the first one...
Ultra extreme computational reducibility in fast action for yah' in both computational load and floating point elimination.
No, I have not seen them. Can I download and run them locally?
Are you sure that https://github.com/google-deepmind/graphcast is runnable on laptop?
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