Ege Erdil

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Wiki Contributions


Yes, this summary seems accurate.

I thought cryonics was unlikely to work because a bunch of information might be lost even at the temperatures that bodies are usually preserved in. I now think this effect is most likely not serious and cryonics can work in principle at the temperatures we use, but present-day cryonics is still unlikely to work because of how much tissue damage the initial process of freezing can do.

I don't think those ratings are comparable. On the other hand, my estimate of 3d was apparently lowballing it based on some older policy networks, and newer ones are perhaps as strong as 4d to 6d, which on the upper end is still weaker than professional players but not by much.

However, there is a big gap between weak professional players and "grandmaster level", and I don't think the raw policy network of AlphaGo could play competitively against a grandmaster level Go player.

This is not quite true. Raw policy networks of AlphaGo-like models are often at a level around 3 dan in amateur rankings, which would qualify as a good amateur player but nowhere near the equivalent of grandmaster level. If you match percentiles in the rating distributions, 3d in Go is perhaps about as strong as an 1800 elo player in chess, while "master level" is at least 2200 elo and "grandmaster level" starts at 2500 elo.

Edit: Seems like policy networks have improved since I last checked these rankings, and the biggest networks currently available for public use can achieve a strength of possibly as high as 6d without MCTS. That would be somewhat weaker than a professional player, but not by much. Still far off from "grandmaster level" though.

I think you're ignoring the qualifier "literally portrayed" in Matthew's sentence, and neglecting the prior context that he's talking about AI development being something mainly driven forward by hobbyists with no outsized impacts.

He's talking about more than just the time in which AI goes from e.g. doubling the AI software R&D output of humans to some kind of singularity. The specific details Eliezer has given about this scenario have not been borne out: for example, in his 2010 debate with Robin Hanson, he emphasized a scenario in which a few people working in a basement and keeping all of their insights secret hit upon some key software innovation that enables their piece of consumer hardware to outcompete the rest of the world.

It's worth noting that Robin Hanson also said that "takeoff" is most likely to take months. He just said it for ems, and in his world, that rate of growth was being driven by the entire world economy working as a whole rather than one local part of the world having such better software that it could outcompete everyone else with vastly less material resources. I find you saying this is a "mild win" for Eliezer's prediction incomprehensible given that we live in a world where individual AI labs are being valued at ~ $100B and raising tens of billions of dollars in capital.

I assume John was referring to Unitary Evolution Recurrent Neural Networks which is cited in the "Orthogonal Deep Neural Nets" paper.

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