johnswentworth

Karma: 49,588

johnswentworth 7 Dec 2024 2:59 UTC
2 points
0
in reply to: manbearpig’s comment on: Core Pathways of Aging
I don’t know of one, though I haven’t followed the literature much the past couple years.

johnswentworth 6 Dec 2024 18:08 UTC
18 points
5
on: johnswentworth’s Shortform
Basically every time a new model is released by a major lab, I hear from at least one person (not always the same person) that it’s a big step forward in programming capability/usefulness. And then David gives it a try, and it works qualitatively the same as everything else: great as a substitute for stack overflow, can do some transpilation if you don’t mind generating kinda crap code and needing to do a bunch of bug fixes, and somewhere between useless and actively harmful on anything even remotely complicated.
It would be nice if there were someone who tries out every new model’s coding capabilities shortly after they come out, reviews it, and gives reviews with a decent chance of actually matching David’s or my experience using the thing (90% of which will be “not much change”) rather than getting all excited every single damn time. But also, to be a useful signal, they still need to actually get excited when there’s an actually significant change. Anybody know of such a source?
EDIT-TO-ADD: David has a comment below with a couple examples of coding tasks.

johnswentworth 3 Dec 2024 20:07 UTC
17 points
4
in reply to: Alexander Gietelink Oldenziel’s comment on: Should there be just one western AGI project?
On my understanding, the push for centralization came from a specific faction whose pitch was basically:
- here’s the scaling laws for tokamaks
- here’s how much money we’d need
- … so let’s make one real big tokamak rather than spending money on lots of little research devices.
… and that faction mostly won the competition for government funding for about half a century.
The current boom accepted that faction’s story at face value, but then noticed that new materials allowed the same “scale up the tokamaks” strategy to be executed on a budget achievable with private funding, and therefore they could fund projects without having to fight the faction which won the battle for government funding.
The counterfactual which I think is probably correct is that there exist entirely different designs far superior to tokamaks, which don’t require that much scale in the first place, but which were never discovered because the “scale up the tokamaks” faction basically won the competition for funding and stopped most research on alternative designs from happening.

johnswentworth 3 Dec 2024 17:11 UTC
14 points
2
in reply to: Alexander Gietelink Oldenziel’s comment on: Should there be just one western AGI project?
If you mean the Manhattan Project: no. IIUC there were basically zero Western groups and zero dollars working toward the bomb before that, so the Manhattan Project clearly sped things up. That’s not really a case of “centralization” so much as doing-the-thing-at-all vs not-doing-the-thing-at-all.
If you mean fusion: yes. There were many fusion projects in the sixties, people were learning quickly. Then the field centralized, and progress slowed to a crawl.

johnswentworth 3 Dec 2024 17:00 UTC
14 points
2
on: Should there be just one western AGI project?
I think this is missing the most important consideration: centralization would likely massively slow down capabilities progress.

johnswentworth 29 Nov 2024 6:23 UTC
4 points
0
in reply to: Garrett Baker’s comment on: leogao’s Shortform
the number one spontaneous conversation is “what are you working on” or “what have you done so far”, which forces you to re-explain what you’re doing & the reasons for doing it to a skeptical & ignorant audience
I’m very curious if others also find this to be the biggest value-contributor amongst spontaneous conversations. (Also, more generally, I’m curious what kinds of spontaneous conversations people are getting so much value out of.)

johnswentworth 28 Nov 2024 6:24 UTC
8 points
−1
in reply to: leogao’s comment on: leogao’s Shortform
I have heard people say this so many times, and it is consistently the opposite of my experience. The random spontaneous conversations at conferences are disproportionately shallow and tend toward the same things which have been discussed to death online already, or toward the things which seem simple enough that everyone thinks they have something to say on the topic. When doing an activity with friends, it’s usually the activity which is novel and/or interesting, while the conversation tends to be shallow and playful and fun but not as substantive as the activity. At work, spontaneous conversations generally had little relevance to the actual things we were/are working on (there are some exceptions, but they’re rarely as high-value as ordinary work).

johnswentworth 27 Nov 2024 19:02 UTC
10 points
10
in reply to: DaystarEld’s comment on: You are not too “irrational” to know your preferences.
The ice cream snippets were good, but they felt too much like they were trying to be a relatively obvious not-very-controversial example of the problems you’re pointing at, rather than a central/prototypical example. Which is good as an intro, but then I want to see it backed up by more central examples.
The dishes example was IMO the best in the post, more like that would be great.
Unfiltered criticism was discussed in the abstract, it wasn’t really concrete enough to be an example. Walking through an example conversation (like the ice cream thing) would help.
Mono vs open vs poly would be a great example, but it needs an actual example conversation (like the ice cream thing), not just a brief mention. Same with career choice. I want to see how specifically the issues you’re pointing to come up in those contexts.
(Also TBC it’s an important post, and I’m glad you wrote it.)

johnswentworth 26 Nov 2024 17:49 UTC
31 points
19
on: You are not too “irrational” to know your preferences.
This post would be a LOT better with about half-a-dozen representative real examples you’ve run into.

johnswentworth 21 Nov 2024 16:55 UTC
12 points
6
in reply to: johnswentworth’s comment on: Why Don’t We Just… Shoggoth+Face+Paraphraser?
One example, to add a little concreteness: suppose that the path to AGI is to scale up o1-style inference-time compute, but it requires multiple OOMs of scaling. So it no longer has a relatively-short stream of “internal” thought, it’s more like the natural-language record of an entire simulated society.
Then:
- There is no hope of a human reviewing the whole thing, or any significant fraction of the whole thing. Even spot checks don’t help much, because it’s all so context-dependent.
- Accurate summarization would itself be a big difficult research problem.
- There’s likely some part of the simulated society explicitly thinking about intentional deception, even if the system as a whole is well aligned.
- … but that’s largely irrelevant, because in the context of a big complex system like a whole society, the effects of words are very decoupled from their content. Think of e.g. a charity which produces lots of internal discussion about reducing poverty, but frequently has effects entirely different from reducing poverty. The simulated society as a whole might be superintelligent, but its constituent simulated subagents are still pretty stupid (like humans), so their words decouple from effects (like humans’ words).
… and that’s how the proposal breaks down, for this example.

johnswentworth 21 Nov 2024 4:38 UTC
10 points
11
in reply to: Daniel Kokotajlo’s comment on: Why Don’t We Just… Shoggoth+Face+Paraphraser?
I haven’t decided yet whether to write up a proper “Why Not Just...” for the post’s proposal, but here’s an overcompressed summary. (Note that I’m intentionally playing devil’s advocate here, not giving an all-things-considered reflectively-endorsed take, but the object-level part of my reflectively-endorsed take would be pretty close to this.)
Charlie’s concern isn’t the only thing it doesn’t handle. The only thing this proposal does handle is an AI extremely similar to today’s, thinking very explicitly about intentional deception, and even then the proposal only detects it (as opposed to e.g. providing a way to solve the problem, or even a way to safely iterate without selecting against detectability). And that’s an extremely narrow chunk of the X-risk probability mass—any significant variation in the AI breaks it, any significant variation in the threat model breaks it. The proposal does not generalize to anything.
Charlie’s concern is just one specific example of a way in which the proposal does not generalize. A proper “Why Not Just...” post would list a bunch more such examples.
And as with Charlie’s concern, the meta-level problem is that the proposal also probably wouldn’t get us any closer to handling those more-general situations. Sure, we could make some very toy setups (like the chess thing), and see what the shoggoth+face AI does on those very toy setups, but we get very few bits, and the connection is very tenuous to both other threat models and AIs with any significant differences from the shoggoth+face. Accounting for the inevitable failure to measure what we think we’re measuring (with probability close to 1), such experiments would not actually get us any closer to solving any of the problems which constitute the bulk of the X-risk probability mass. It’s not “a start”, because “a start” would imply that the experiment gets us closer, i.e. that the problem gets easier after doing the experiment. If you try to think about the You Are Not Measuring What You Think You Are Measuring problem as “well, we got at least some tiny epsilon of evidence, right?”, then you will shoot yourself in the foot; such reasoning is technically correct, but the correct value of epsilon is small enough that the correct update from it is not distinguishable from zero in practice.

johnswentworth 21 Nov 2024 2:27 UTC
10 points
11
in reply to: Daniel Kokotajlo’s comment on: Why Don’t We Just… Shoggoth+Face+Paraphraser?
The problem with that sort of attitude is that, when the “experiment” yields so few bits and has such a tenuous connection to the thing we actually care about (as in Charlie’s concern), that’s exactly when You Are Not Measuring What You Think You Are Measuring bites real hard. Like, sure, you’ll see this system do something in the toy chess experiment, but that’s just not going to be particularly relevant to the things an actual smarter-than-human AI does in the situations Charlie’s concerned about. If anything, the experimenter is far more to likely to fool themselves into thinking their results are relevant to Charlie’s concern than they are to correctly learn anything relevant to Charlie’s concern.

johnswentworth 18 Nov 2024 21:03 UTC
6 points
4
in reply to: Leon Lang’s comment on: Leon Lang’s Shortform
I think this misunderstands what discussion of “barriers to continued scaling” is all about. The question is whether we’ll continue to see ROI comparable to recent years by continuing to do the same things. If not, well… there is always, at all times, the possibility that we will figure out some new and different thing to do which will keep capabilities going. Many people have many hypotheses about what those new and different things could be: your guess about interaction is one, inference time compute is another, synthetic data is a third, deeply integrated multimodality is a fourth, and the list goes on. But these are all hypotheses which may or may not pan out, not already-proven strategies, which makes them a very different topic of discussion than the “barriers to continued scaling” of the things which people have already been doing.

johnswentworth 17 Nov 2024 22:40 UTC
2 points
0
in reply to: Bogdan Ionut Cirstea’s comment on: johnswentworth’s Shortform
Some of the underlying evidence, like e.g. Altman’s public statements, is relevant to other forms of scaling. Some of the underlying evidence, like e.g. the data wall, is not. That cashes out to differing levels of confidence in different versions of the prediction.

johnswentworth 15 Nov 2024 23:50 UTC
5 points
3
in reply to: Linch’s comment on: The Median Researcher Problem
Oh I see, you mean that the observation is weak evidence for the median model relative to a model in which the most competent researchers mostly determine memeticity, because higher median usually means higher tails. I think you’re right, good catch.

johnswentworth 15 Nov 2024 23:11 UTC
1 point
−11
in reply to: Vladimir_Nesov’s comment on: johnswentworth’s Shortform
FYI, my update from this comment was:
- Hmm, seems like a decent argument...
- … except he said “we don’t know that it doesn’t work”, which is an extremely strong update that it will clearly not work.

johnswentworth 15 Nov 2024 22:07 UTC
5 points
3
in reply to: Leon Lang’s comment on: johnswentworth’s Shortform
Still very plausible as a route to continued capabilities progress. Such things will have very different curves and economics, though, compared to the previous era of scaling.

johnswentworth 15 Nov 2024 21:20 UTC
6 points
−9
in reply to: Vladimir_Nesov’s comment on: johnswentworth’s Shortform
I don’t expect that to be particularly relevant. The data wall is still there; scaling just compute has considerably worse returns than the curves we’ve been on for the past few years, and we’re not expecting synthetic data to be anywhere near sufficient to bring us close to the old curves.

johnswentworth 15 Nov 2024 21:10 UTC
2 points
0
in reply to: Linch’s comment on: The Median Researcher Problem
unless you additionally posit an additional mechanism like fields with terrible replication rates have a higher standard deviation than fields without them
Why would that be relevant?

johnswentworth 15 Nov 2024 16:52 UTC
37 points
12
on: johnswentworth’s Shortform
Regarding the recent memes about the end of LLM scaling: David and I have been planning on this as our median world since about six months ago. The data wall has been a known issue for a while now, updates from the major labs since GPT-4 already showed relatively unimpressive qualitative improvements by our judgement, and attempts to read the tea leaves of Sam Altman’s public statements pointed in the same direction too. I’ve also talked to others (who were not LLM capability skeptics in general) who had independently noticed the same thing and come to similar conclusions.
Our guess at that time was that LLM scaling was already hitting a wall, and this would most likely start to be obvious to the rest of the world around roughly December of 2024, when the expected GPT-5 either fell short of expectations or wasn’t released at all. Then, our median guess was that a lot of the hype would collapse, and a lot of the investment with it. That said, since somewhere between 25%-50% of progress has been algorithmic all along, it wouldn’t be that much of a slowdown to capabilities progress, even if the memetic environment made it seem pretty salient. In the happiest case a lot of researchers would move on to other things, but that’s an optimistic take, not a median world.
(To be clear, I don’t think you should be giving us much prediction-credit for that, since we didn’t talk about it publicly. I’m posting mostly because I’ve seen a decent number of people for whom the death of scaling seems to be a complete surprise and they’re not sure whether to believe it. For those people: it’s not a complete surprise, this has been quietly broadcast for a while now.)