What did Sam Altman do? Like, what are the top two things that he did that make you say that? That will maybe sound absolutely wild to you, but I’m still not convinced Sam Altman did anything bad. It’s possible, sure, but based on the depositions, it’s far from clear IMO.
Edit: unless you mean the for-profit conversion, but I understood you to be talking about the board drama specifically.
I’d be pretty unhappy if things stayed the same for me. I’d want at least some radical things, like curing aging. But still, I will definitely want to be able to decide for myself and not be forced into anything in particular (unless really necessary for some reason, like to save my life, but hopefully it won’t be).
Yup, but the AIs are massively less likely to help with creating cruel content. There will be a huge asymmetry in what they will be willing to generate.
Imagine an Internet where half the population is Grant Sanderson (the creator of 3Blue1Brown). That’d be awesome. Grant Sanderson has the same incentives as anyone else to create cruel and false content, but he just doesn’t.
People are very worried about a future in which a lot of the Internet is AI-generated. I’m kinda not. So far, AIs are more truth-tracking and kinder than humans. I think the default (conditional on OK alignment) is that an Internet that includes a much higher population of AIs is a much better experience for humans than the current Internet, which is full of bullying and lies.
All such discussions hinge on AI being relatively aligned, though. Of course, an Internet full of misaligned AIs would be bad for humans, but the reason is human disempowerment, not any of the usual reasons people say such an Internet would be terrible.
Slop is in the mind, not in the territory. I will not call slop something that I like, regardless of what other people call slop.
It’s good to see more funding entering the field
Is the funding coming from new funding sources?
I still mourn a life without AI
Honestly, if AI goes well I really won’t. I will mourn people who have died too early. The current situation is quite bad. My main feeling will probably be of extreme relief at first.
think my brain was trying to figure out why I felt inexplicably bad upon hearing that Joe Carlsmith was joining Anthropic to work on alignment
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Perhaps the most important strategic insight resulting from this line of thought is that making illegible safety problems more legible is of the highest importance
Well, one way to make illegible problems more legible is to think about illegible problems and then go work at Anthropic to make them legible to employees there, too.
It would be helpful for the discussion (and for me) if you stated an example of a legible problem vs. an illegible problem. I expect people might disagree on the specifics, even if they seem to agree with the abstract argument.
Ah, I get what you are saying, and I agree. It’s possible the human brain architecture, as-is, can’t process 4D, but I guess we’re mismatched in what we think is interesting. The thrust of my intuition here was more “wow, someone could understand N-D intuitively in a 3D universe, this doesn’t seem prohibited”, regardless of whether it’s the same architecture of a human brain exactly. Like, the human brain as it is right now might not permit that, and neurotech might involve doing a lot of architectural changes (the same applies to emulations). I suppose it’s a lot less interesting an insight if you already buy that imagining higher dimensions from a 3D universe is in principle possible. The human brain being able to do that is a stronger claim that would have been more interesting if I actually managed to defend it well.
I suppose I was kinda sloppy saying “the human brain can do that”—I should have said “the human brain arbitrarily modified” or something like that.
I’m pretty sure a human brain could, in principle, visualize a 4D space just as well as it visualizes a 3D space, and that there are ways to make that happen via neurotech (as an upper bound on difficulty).
Consider: we know a lot about how 4-dimensional spaces behave mathematically, probably no less than how 3-dimensional spaces work. Once we know exactly how the brain encodes and visualizes a 3D space in its neurons, we probably also understand how it would do it for a 4D space if it had sensory access to it. Given good enough neurotech, we could manually craft the circuits necessary to reason intuitively in 4D.
Also, another insight/observation: insofar as AIs can have imagination, an AI trained in a 4D environment should develop 4D imagination (i.e., the circuits necessary to navigate and imagine 4D intuitively). The same should be true about human-brain emulations in 4D simulations.
Accidental AI Safety experiment by PewDiePie: He created his own self-hosted council of 8 AIs to answer questions. They voted and picked the best answer. He noticed they were always picking the same two AIs, so he discarded the others, made the process of discarding/replacing automatic, and told the AIs about it. The AIs started talking about this “sick game” and scheming to prevent that. This is the video with the timestamp:
Reaction request: “bad source” and “good source” to use when people cite sources you deem unreliable vs. reliable.
I know I would have used the “bad source” reaction at least once.
Of course, the same consideration applies to theoretical agent-foundations-style alignment research
What does being on this list imply? The book doesn’t have many Amazon reviews, and if those are good for estimating total copies sold, then I don’t understand exactly what the NYT bestseller list signifies.
Is anyone working on experiments that could disambiguate whether LLMs talk about consciousness because of introspection vs. “parroting of training data”? Maybe some scrubbing/ablation that would degrade performance or change answer only if introspection was useful?
There’s something that I think is usually missing from time-horizon discussions, which is that the human brain seems to operate on a very long time horizon for entirely different reasons. The story for LLMs looks like this: LLMs become better at programming tasks, therefore they become capable of doing (in a relatively short amount of time) tasks that would take increasingly longer for humans to do. Humans, instead, can just do stuff for a lifetime, and we don’t know where the cap is, and our brain has ways to manage its memories depending on how often they are recalled, and probably other ways to keep itself coherent over long periods. It’s a completely different sort of thing! This makes me think that the trend here isn’t very “deep”. The line will continue to go up as LLMs become better and better at programming, and then it will slow down due to capability gains generally slowing down due to training compute bottlenecks and due to limited inference compute budgets. On the other hand, I think it’s pretty dang likely that we get a drastic trend break in the next few years (i.e., the graph essentially loses its relevance) when we crack the actual mechanisms and capabilities related to continuous operation. For example, continuous learning, clever memory management, and similar things that we might be completely missing at the moment even as concepts.
The speed of GPT-5 could be explained by using GB200 NVL72 for inference, even if it’s an 8T total param model.
Ah, interesting! So the speed we see shouldn’t tell us much about GPT-5′s size.
I omitted one other factor from my shortform, namely cost. Do you think OpenAI would be willing to serve an 8T params (1T active) model for the price we’re seeing? I’m basically trying to understand whether GPT-5 being served for relatively cheap should be a large or small update.
I think we have enough evidence to say that, in practice, this turns out very easy or moot. Values tend to cluster in LLMs (good with good and bad with bad; see emergent misalignment results), so value fragility isn’t a hard problem.