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Well, I must say that I'm pleasantly surprised with the comments so far. I was actually expecting that this would be quickly shot down as either unoriginal or f
by ad510 10y ago
Well, I must say that I'm pleasantly surprised with the comments so far. I was actually expecting that this would be quickly shot down as either unoriginal or fundamentally flawed. But instead it seems no one has caught on to what this post is actually claiming, so I suppose I should now be very blunt about it.
At the beginning of the blog post, it claims that it explains 2 things:
1. where exactly might you be able to use learning algorithms where you can't just use existing physics theories instead
2. a hands on guide to applying learning algorithms in these situations
This is physicist code for "this blog post claims that it solves a major unsolved problem in physics." Let me explain.
Currently, we have the standard model and general relativity, which have been experimentally verified to extreme precision but are fundamentally incompatible with each other. So people have proposed theories of everything such as string theory, loop quantum gravity, and information/digital physics (which I'm obviously a fan of) to resolve these incompatibilities.
One of the biggest problems in fundamental physics right now is that the standard model and general relativity have been verified to such precision that it's hard to think of a practical experiment to show how they are wrong. The conventional wisdom is that this is only possible if we do things like measure the Planck scale or what happens inside a black hole, which are completely impractical on human timescales.
What this post proposes is that you actually don't need to measure the Planck scale or what happens in a black hole in order to test the proposed theories of everything, and instead you can do it with a sufficiently powerful computer simulation and a sufficiently good brain-computer interface. If our technology keeps improving exponentially, this may be possible in the next several decades.
So yeah, I made a bit of a white lie when I framed this post as a summary of recent research in information physics. I can back up almost everything in the post with the sources I linked to, but the part about the 0 or 1 experiment and predicting its outcome using Solomonoff induction is actually original research on my part, and I suspect it would actually be a very big deal if this works the way I think it does.
So here are the possible outcomes for this blog post:
1. The problem in physics I just described is actually already solved.
2. The blog post is fundamentally flawed, and/or it actually doesn't solve the problem that I'm claiming it solves.
3. The blog post actually does solve a major unsolved problem in physics, and this is a huge deal.
This is why I am so surprised at the comments I'm getting so far, since this proposal for experimentally testing theories of everything seems to be passing the internet commenter test. So if no one on HN finds anything seriously wrong with the blog post, can we get people like Scott Aaronson, John Baez, Juergen Schmidhuber, Stephen Hawking, or people of that caliber to look at it so we can get a more definitive answer to whether this actually solves an unsolved problem in physics?
Also, kudos to Xcelerate's comment, which is the closest to the point I was trying to get at with the blog post.
- deleted 10y ago[deleted]
- jerf 10y agoIt is not clear to me that you realize that Solomonoff induction is a mathematical argument, not a practical algorithm. To run it at the level of generality necessary to discover the laws of physics is computationally infeasible. In fact, it's one of those cases where calling it "computationally infeasible" is an inadvertent understatement of the problem, because English doesn't have gradations for this level of difficulty. Merely a "singularity" doesn't help this problem; you need more computation than our physics appears to allow.
- ad510 10y agoYes, I know that Solomonoff induction is completely impractical for real life machine learning. My point was that if you can survive in the simulation to the point where you see either the 0 or the 1, we don't have any way even in theory (let alone in practice) to guess the probabilities of seeing a 0 or 1, unless you use some sort of learning algorithm. You can use any learning algorithm for this; it doesn't have to be Solomonoff induction.
- jerf 10y agoBut your argument seems to fundamentally rest on Solomonoff induction. Put any real algorithm in there, and now you need to ensure that 1. the biases of the algorithm encompass a hypothesis that matches the data and 2. the algorithm will be able to arrive at that hypothesis given a real data stream, and, ideally, a real amount of computation. Both of these are hard questions, in the strongest sense of the term. And once you open that door, well, all you've really done is restate the fact that learning how the universe works seems to be really difficult.
- ad510 10y agoOK, I see what you're saying now. In that case, can you think of a better way of predicting whether you see a 0 or 1 in that situation?