In the past, people like Eliezer Yudkowsky (see 1, 2, 3, 4, and 5) have argued that MIRI has a medium probability of success. What is this probability estimate based on and how is success defined?
I've read standard MIRI literature (like "Evidence and Import" and "Five Theses"), but I may have missed something.
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(Meta: I don't think this deserves a discussion thread, but I posted this on the open thread and no-one responded, and I think it's important enough to merit a response.)
"Antiantiheroic epistemology might be a better term, i.e., I think that a merely accurate epistemology doesn't have a built-in mechanism which prevents people from thinking they can do things because the outside view says it's nonvirtuous to try to distinguish yourself within reference class blah. "
Taken literally, I can't possibly disagree with this, but it doesn't seem to answer my question, which is "where is the positive evidence that one is not supposed to ignore." I favor combining many different kinds of evidence, including sparse data. And that can and does lead to very high expectations for particular individuals.
For example, several of my college fraternity brothers are now billionaires. Before facebook Mark Zuckerberg was clearly the person with the highest entrepreneurial potential that I knew, based on his intelligence, motivation, ambition, and past achievements in programming, business, and academics. People described him to me as resembling a young Bill Gates. His estimated expected future wealth based on that data if pursuing entrepreneurship, and informed by the data about the relationship of all of the characteristics I could track with it, was in the 9-figure range. Then add in that facebook was a very promising startup (I did some market sizing estimates for it, and people who looked at it and its early results were reliably impressed).
Moving from entrepreneurship to politics, one can predict success to a remarkable degree with evidence like "Eton graduate, Oxford PPE graduate with first class degree, Oxford Union leader, interested in politics, starts in an entry-level political adviser job with a party." See this post or this paper. Most of the distance in log odds to reliably becoming Prime Minister, let alone Member of Cabinet or Parliament, can be crossed with objective indicators. Throw in a bit more data about early progress, media mentions, and the like and the prediction improves still more.
I would then throw in other evidence, like the impressiveness of the person's public speaking relative to other similar people, their number and influence of friends and contacts in high places relative to other similar types (indicating both social capital, and skill at getting more), which could improve or worsen the picture. There is still a sizable chunk of randomness in log terms, as political careers are buffeted by switches in party control, the economy, the rise and fall of factions that carry members with them, and other hard-to-control factors at many stages. So I can and do come to expect that someone will probably get federal political office, and have a good shot at Cabinet, and less so for PM. But within the real distribution of characteristics I won't be convinced that a young person will probably become PM, which would require almost zero noise.
In science I can be convinced a young star is a good prospect for Nobel or Fields medal caliber work. But I would need stronger evidence than we have seen for anyone to expect that they would do this 10 times (since no one has done so). I am sympathetic to Wei Dai's comment
I would be quite surprised to see you reliably making personal mathematical contributions at the level of the best top math and AI people. I would not be surprised to see MIRI workshop participants making progress on the problems at a level consistent with the prior evidence of their ability, and somewhat higher per unit time because workshops harvest ideas generated over a longer period, are solely dedicated to research, have a lot of collaboration and cross-fertilization, and may benefit from improved motivation and some nice hacking of associated productivity variables. And I would not be surprised at a somewhat higher than typical rate of interesting (to me, etc) results because of looking at strange problems.
I would be surprised if the strange problems systematically deliver relatively huge gains on actual AI problems (and this research line is supposed to deliver AGI as a subset of FAI before others get AGI so it must have great utility in AGI design), i.e. if the strange problems are super-promising by the criteria that Pearl or Hinton or Ng or Norvig are using but neglected by blunder. I would be surprised if the distance to AGI is crossable in 20 years.
You are asking other people for their money and time, when they have other opportunities. To do that they need an estimate of the chance of MIRI succeeding, considering things like AI timelines, the speed of takeoff given powerful AI, competence of other institutions, the usefulness of MIRI's research track, the feasibility of all alternative solutions to AI risk/AI control problems, how much MIRI-type research will be duplicated by researchers interested for other reasons over what timescales, and many other factors including the ability to execute given the difficulty of the problems and likelihood of relevance. So they need adequate object-level arguments about those contributing factors, or some extraordinary evidence to trust your estimates of all of them over the estimates of others without a clear object-level case. Some of the other opportunities available to them that they need to compare against MIRI:
I read every blog post they put out.
I figure I can use my retirement savings for this.
I thought it came from them being collectively foolish or ig... (read more)