Okay. Though in the real world, it's quite likely that an unknown frequency is exactly 0, 1, or 1/2
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.)