Only partially relevant, but it's exciting to hear a new John/David paper is forthcoming!
Furthermore: normalizing your data to variance=1 will change your PCA line (if the X and Y variances are different) because the relative importance of X and Y distances will change!
Thanks for writing this up. As someone who was not aware of the eye thing I think it's a good illustration of the level that the Zizians are on, i.e. misunderstanding key important facts about the neurology that is central to their worldview.
My model of double-hemisphere stuff, DID, tulpas, and the like is somewhat null-hypothesis-ish. The strongest version is something like this:
At the upper levels of predictive coding, the brain keeps track of really abstract things about yourself. Think "ego" "self-conception" or "narrative about yourself". This is normally a model of your own personality traits, which may be more or less accurate. But there's no particular reason why you couldn't build a strong self-narrative of having two personalities, a sub-personality, or more. If you model yourself as having two personalities who can't access each other's memories, then maybe you actually just won't perform the query-key lookups to access the memories.
Like I said, this doesn't rule out a large amount of probability mass, but it does explain some things, fit in with my other views, and hopefully if someone has had/been close to experiences kinda like DID or zizianism or tulpas, it provides a less horrifying way of thinking about them. Some of the reports in this area are a bit infohazardous, and I think this null model at least partially defuses those infohazard.
This is a very interesting point. I have upvoted this post even though I disagree with it because I think the question of "Who will pay, and how much will they pay, to restrict others' access AI?" is important.
My instinct is that this won't happen, because there are too many AI companies for this deal to work on all of them, and some of these AI companies will have strong kinda-ideological commitments to not doing this. Also, my model of (e.g. OpenAI) is that they want to eat as much of the world's economy as possible, and this is better done by selling (even at a lower revenue) to anyone who wants an AI SWE than selling just to Oracle.
o4 (God I can't believe I'm already thinking about o4) as a b2b saas project seems unlikely to me. Specifically I'd put <30% odds that the o4-series have their prices jacked up or its API access restricted in order to allow some companies to monopolize its usage for more than 3 months without an open release. This won't apply if the only models in the o4 series cost $1000s per answer to serve, since that's just a "normal" kind of expensive.
Then, we have to consider that other labs are 1-1.5 years behind, and it's hard to imagine Meta (for example) doing this in anything like the current climate.
That's part of what I was trying to get at with "dramatic" but I agree now that it might be 80% photogenicity. I do expect that 3000 Americans killed by (a) humanoid robot(s) on camera would cause more outrage than 1 million Americans killed by a virus which we discovered six months later was AI-created in some way.
Previous ballpark numbers I've heard floated around are "100,000 deaths to shut it all down" but I expect the threshold will grow as more money is involved. Depends on how dramatic the deaths are though, 3000 deaths was enough to cause the US to invade two countries back in the 2000s. 100,000 deaths is thirty-three 9/11s.
Is there a particular reason to not include sex hormones? Some theories suggest that testosterone tracks relative social status. We might expect that high social status -> less stress (of the cortisol type) + more metabolic activity. Since it's used by trans people we have a pretty good idea of what it does to you at high doses (makes you hungry, horny, and angry) but its unclear whether it actually promotes low cortisol-stress and metabolic activity.
I'm mildly against this being immortalized as part of the 2023 review, though I think it serves excellently as a community announcement for Bay Area rats, which seems to be its original purpose.
I think it has the most long-term relevant information (about AI and community building) back loaded and the least relevant information (statistics and details about a no-longer-existent office space in the Bay Area) front loaded. This is a very Bay Area centric post, which I don't think is ideal.
A better version of this post would be structured as a round up of the main future-relevant takeaways, with specifics from the office space as examples.
I'm only referring to the reward constraint being satisfied for scenarios that are in the training distribution, since this maths is entirely applied to a decision taking place in training. Therefore I don't think distributional shift applies.
I second this, it could easily be things which we might describe as "amount of information that can be processed at once, including abstractions" which is some combination of residual stream width and context length.
Imagine an AI can do a task that takes 1 hour. To remain coherent over 2 hours, it could either use twice as much working memory, or compress it into a higher level of abstraction. Humans seem to struggle with abstraction in a fairly continuous way (some people get stuck at algebra; some cs students make it all the way to recursion then hit a wall; some physics students can handle first quantization but not second quantization) which sorta implies there's a maximum abstraction stack height which a mind can handle, which varies continuously.