I don’t think that response makes sense. The classic instrumental convergence arguments are about a single agent; OP is asking why distinct AIs would coordinate with one another.
I think the AIs may well have goals that conflict with one another, just as humans’ goals do, but it’s plausible that they would form a coalition and work against humans’ interests because they expect a shared benefit, as humans sometimes do.
I don’t think this is an important obstacle — you could use something like “and act such that your P(your actions over the next year lead to a massive disaster) < 10^-10.” I think Daniel’s point is the heart of the issue.
Should
serious problems with Boltzmann machines
instead read
serious problems with Boltzmann brains
?
I don’t think observing that folks in the Middle East drink much less, due to a religious prohibition, is evidence for or against this post’s hypothesis. It can simultaneously be the case that evolution discovered this way of preventing alcoholism, and also that religious prohibitions are a much more effective way of preventing alcoholism.
I had the “Europeans evolved to metabolize alcohol” belief that this post aims to destroy. Thanks!
This post gave me the impression that the evolutionary explanation it gives is novel, but I don’t think that’s the case; here’s a paper (https://bmcecolevol.biomedcentral.com/articles/10.1186/1471-2148-10-15#Sec6) that mentions the same hypothesis.
In
Okay. Though in the real world, it’s quite likely that an unknown frequency is exactly , , or
should the text read “unlikely” instead of “likely” ?
+1 to copper tape being difficult to get off.
(Not related to the overall point of your paper) I’m not so sure that GPT-3 “has the internal model to do addition,” depending on what you mean by that — nostalgebraist doesn’t seem to think so in this post, and a priori this seems like a surprising thing for a feedforward neural network to do.
Can you give some examples?
Like a belief that you’ve discovered a fantastic investment opportunity, perhaps?
I’m interested — 10 please.
Caveat that I have no formal training in physics.
Perhaps you already know this, but some of your statements made me think you don’t. In an electric circuit, individual electrons do not move from the start to the end at the speed of light. Instead, they move much more slowly. This is true regardless of whether the current is AC or DC.
The thing that travels at the speed of light is the *information* that a push has happened. There’s an analogy to a tube of ping-pong balls, where pushing on one end will cause the ball at the other end to move very soon, even though no individual ball is moving very quickly.
(I’ll back off the Superman analogy; I think it’s disanalogous b/c of the discontinuity thing you point out.)
Yeah I like the analogue “some basketball players are NBA players.” It makes it sound totally unsurprising, which it is.
I don’t agree that Vox is right, because:
- I can’t find any evidence for the claim that forecasting ability is power-law distributed, and it’s not clear what that would mean with Brier scores (as Unnamed points out).
- Their use of the term “discovered.”
I don’t think I’m just quibbling over semantics; I definitely had the wrong idea about superforecasters prior to thinking it through, it seems like Vox might have it too, and I’m concerned others who read the article will get the wrong idea as well.
Agree re: power law.
The data is here https://dataverse.harvard.edu/dataverse/gjp?q=&types=files&sort=dateSort&order=desc&page=1 , so I could just find out. I posted here trying to save time, hoping someone else would already have done the analysis.
Thanks for your reply!
It looks to me like we might be thinking about different questions. Basically I’m just concerned about the sentence “Philip Tetlock discovered that 2% of people are superforecasters.” When I read this sentence, it reads to me like “2% of people are superheroes” — they have performance that is way better than the rest of the population on these tasks. If you graphed “jump height” of the population and 2% of the population is Superman, there would be a clear discontinuity at the higher end. That’s what I imagine when I read the sentence, and that’s what I’m trying to get at above.
It looks like you’re saying that this isn’t true?
(It looks to me like you’re discussing the question of how innate “superforecasting” is. To continue the analogy, whether superforecasters have innate powers like Superman or are just normal humans who train hard like Batman. But I think this is orthogonal to what I’m talking about. I know the sentence “are superforecasters a ‘real’ phenomenon” has multiple operationalizations, which is why I specified one as what I was talking about.)
Hmm, thanks for pointing that out about Brier scores. The Vox article cites https://www.vox.com/2015/8/20/9179657/tetlock-forecasting for its “power law” claim, but that piece says nothing about power laws. It does have a graph which depicts a wide gap between “superforecasters” and “top-team individuals” in years 2 and 3 of the project, and not in year 1. But my understanding is that this is because the superforecasters were put together on elite teams after the first year, so I think the graph is a bit misleading.
(Citation: the paper https://stanford.edu/~knutson/nfc/mellers15.pdf)
I do think there’s disagreement between the sources — when I read sentences like this from the Vox article
Tetlock and his collaborators have run studies involving tens of thousands of participants and have discovered that prediction follows a power law distribution. That is, most people are pretty bad at it, but a few (Tetlock, in a Gladwellian twist, calls them “superforecasters”) appear to be systematically better than most at predicting world events … Tetlock even found that superforecasters — smart, well-informed, but basically normal people with no special information — outperformed CIA analysts by about 30 percent in forecasting world events.
I definitely imagine looking at a graph of everyone’s performance on the predictions and noticing a cluster who are discontinuously much better than everyone else. I would be surprised if the authors of the piece didn’t imagine this as well. The article they link to does exactly what Scott warns against, saying “Tetlock’s team found out that some people were ‘superforecasters’.”
What specific rhyme-related tasks are you saying ChatGPT can’t do? I tried it on some unusual words and it got a bunch of things right, made a few weird mistakes, but didn’t give me the impression that it was totally unable to rhyme unusual words.