This is a special post for quick takes by Joseph Miller. Only they can create top-level comments. Comments here also appear on the Quick Takes page and All Posts page.
This is an attempt to compile all publicly available primary evidence relating to the recent death of Suchir Balaji, an OpenAI whistleblower.
This is a tragic loss and I feel very sorry for the parents. The rest of this piece will be unemotive as it is important to establish the nature of this death as objectively as possible.
I was prompted to look at this by a surprising conversation I had IRL suggesting credible evidence that it was not suicide. The undisputed facts of the case are that he died of a gunshot wound in his bathroom sometime around November 26 2024. The police say it was a suicide with no evidence of foul play.
Most of the evidence we have comes from the parents and George Webb. Webb describes himself as an investigative journalist, but I would classify him as more of a conspiracy theorist, based on a quick scan of some of his older videos. I think many of the specific factual claims he has made about this case are true, though I generally doubt his interpretations.
Webb seems to have made contact with the parents early on and went with them when they first visited Balaji's apartment. He has since published videos from the scene of the death, against the wishes of the p...
3The undisputed facts of the case are that he died of a gunshot wound in his bathroom sometime around November 26 2024. The police ruled it as a suicide with no evidence of foul play.
As in, this is also what the police say?
Did the police find a gun in the apartment? Was it a gun Suchir had previously purchased himself according to records? Seems like relevant info.
As in, this is also what the police say?
Yes, edited to clarify. The police say there was no evidence of foul play. All parties agree he died in his bathroom of a gunshot wound.
Did the police find a gun in the apartment? Was it a gun Suchir had previously purchased himself according to records? Seems like relevant info.
The only source I can find on this is Webb, so take with a grain of salt. But yes, they found a gun in the apartment. According to Webb, the DROS registration information was on top of the gun case[1] in the apartment, so presumably there was a record of him purchasing the gun (Webb conjectures that this was staged). We don't know what type of gun it was[2] and Webb claims it's unusual for police not to release this info in a suicide case.
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Well, it seems quite important whether the DROS registration could possibly have been staged. If e.g. there is footage of Suchir buying a gun 6+ months prior, using his ID, etc. then the assassins would have had to sneak in and grab his own gun from him etc. which seems unlikely.
Is the interview with the NYT going to be published?
Is any of the police behavior actually out of the ordinary?
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That would be difficult. To purchase a gun in California you have to provide photo ID[1], proof of address[2] and a thumbprint[3]. Also it looks like the payment must be trackable[4] and gun stores have to maintain video surveillance footage for up to year.[5]
My guess is that the police haven't actually invested this as a potential homicide, but if they did, there should be very strong evidence that Balaji bought a gun. Potentially a very sophisticated actor could fake this evidence but it seems challenging (I can't find any historical examples of this happening). It would probably be easier to corrupt the investigation. Or the perpetrators might just hope that there would be no investigation.
There is a 10-day waiting period to purchase guns in California[5], so Balaji would probably have started planning his suicide before his hiking trip (I doubt someone like him would own a gun for recreational purposes?).
I think it's this piece that was published before his death.
Epistemic status: highly uncertain: my impressions from searching with LLMs for a few minutes.
It's fairly common for victim's families to contest official suicide rulings. In cases with lots of public attention police generally try to justify their conclusions. So we might expect the police to publicly state if there is footage of Balaji purchasing the gun shortly before his death. It could be that this will still happen with more time or public pressure.
1. ^
https://www.fastbound.com/ffl-bound-book-software-features/dros/
2. ^
https://giffords.org/lawcenter/state-laws/background-check-procedures-in-california/
3. ^
https://www.gunpolicy.org/firearms/citation/quotes/7066
4. ^
https://apnews.com/article/gun-stores-firearms-mass-shootings-credit-cards-abe3a28ea7117340d9a4a8bcde3693fe
5. ^
https://giffords.org/lawcenter/state-laws/gun-dealers-in-california/
- Ilya Sutskever had two armed bodyguards with him at NeurIPS.
Some people are asking for a source on this. I'm pretty sure I've heard it from multiple people who were there in person but I can't find a written source. Can anyone confirm or deny?
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I don't understand how Ilya hiring personal security counts as evidence, especially at large events like a conference. Famous people often attract unwelcome attention, and having professional protection close by can help deescalate or deter random acts of violence, it is a worthwhile investment in safety if you can afford it. I see it as a very normal thing to do. Ilya would be vulnerable to potential assassination attempts even during his tenure at OpenAI.
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Thank you, this is very interesting and it seems like you did a valuable public service in compiling it
What do you think of the motive that he was counterfactually going to testify in a very damaging way, or that he had very damaging evidecne/data that was deleted?
Anthropic is reportedly lobbying against the federal bill that would ban states from regulating AI. Nice!
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2"Despite their extreme danger, we only became aware of them when the enemy drew our attention to them by repeatedly expressing concerns that they can be produced simply with easily available materials."
Ayman al-Zawahiri, former leader of Al-Qaeda, on chemical/biological weapons.
I don't think this is a knock-down argument against discussing CBRN risks from AI, but it seems worth considering.
The trick is that chem/bio weapons can't, actually, "be produced simply with easily available materials", if we talk about military-grade stuff, not "kill several civilians to create scary picture in TV".
-4
You sound really confident, can you elaborate on your direct lab experience with these weapons, as well as clearly define 'military grade' vs whatever the other thing was?
How does 'chem/bio' compare to high explosives in terms of difficulty and effect?
Well, I have bioengineering degree, but my point is that "direct lab experience" doesn't matter, because WMDs in quality and amount necessary to kill large numbers of enemy manpower are not produced in labs. They are produced in large industrial facilities and setting up large industrial facility for basically anything is on "hard" level of difficulty. There is a difference between large-scale textile industry and large-scale semiconductor industry, but if you are not government or rich corporation, all of them lie in "hard" zone.
Let's take, for example, Saddam chemical weapons program. First, industrial yields: everything is counted in tons. Second: for actual success, Saddam needed a lot of existing expertise and machinery from West Germany.
Let's look at Soviet bioweapons program. First, again, tons of yield (someone may ask yourself, if it's easier to kill using bioweapons than conventional weaponry, why somebody needs to produce tons of them?). Second, USSR built the entire civilian biotech industry around it (many Biopreparat facilities are active today as civilian objects!) to create necessary expertise.
The difference with high explosives is that high explos...
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This seems incredibly reasonable, and in light of this, I'm not really sure why anyone should embrace ideas like making LLMs worse at biochemistry in the name of things like WMDP: https://www.lesswrong.com/posts/WspwSnB8HpkToxRPB/paper-ai-sandbagging-language-models-can-strategically-1
Biochem is hard enough that we need LLMs at full capacity pushing the field forward. Is it harmful to intentionally create models that are deliberately bad at this cutting edge and necessary science in order to maybe make it slightly more difficult for someone to reproduce cold war era weapons that were considered both expensive and useless at the time?
Do you think that crippling 'wmd relevance' of LLMs is doing harm, neutral, or good?
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My honest opinion is that WMD evaluations of LLMs are not meaningfully related to X-risk in the sense of "kill literally everyone." I guess current or next-generation models may be able to assist a terrorist in a basement in brewing some amount of anthrax, spraying it in a public place, and killing tens to hundreds of people. To actually be capable to kill everyone from a basement, you would need to bypass all the reasons industrial production is necessary at the current level of technology. A system capable to bypass the need for industrial production in a basement is called "superintelligence," and if you have a superintelligent model on the loose, you have far bigger problems than schizos in basements brewing bioweapons.
I think "creeping WMD relevance", outside of cyberweapons, is mostly bad, because it is concentrated on mostly fake problem, which is very bad for public epistemics, even if we forget about lost benefits from competent models.
1
Are you open to writing more about this? This is among top 3 most popular arguments against open source AI on lesswrong and elsewhere.
I agree with you you need a group of > 1000 people to manufacture one of those large machines that does phosphoramidite DNA synthesis. The attack vector I more commonly see being suggested is that a powerful actor can bribe people in the existing labs to manufacture a bioweapon while ensuring most of them and most of rest of society remains unaware this is happening.
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I'm trying to write post, but, well, it's hard.
Many people wrongly assume that the main way to use bioweapons is to create small amount of pathogen to release it in environment with outbreak as an intended outcome. (I assume that where your sentence about DNA synthesis comes from.) The problem is that creating outbreaks in practice is very hard, we, thankfully, don't know reliable way to do that. In practice, the way that bioweapons work reliably is "bomb-saturate the entire area with anthrax such that first wave of death is going to be from anaphylactic shock rather than infection" and to create necessary amount of pathogen you need industrial infrastructure which doesn't exist, because nobody in our civilization cultivates anthrax at industrial scale.
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I wrote about something similar previously: https://www.lesswrong.com/posts/Ek7M3xGAoXDdQkPZQ/terrorism-tylenol-and-dangerous-information#a58t3m6bsxDZTL8DG
I agree that 1-2 logs isn't really in the category of xrisk. The longer the lead time on the evil plan (mixing chemicals, growing things, etc), the more time security forces have to identify and neutralize the threat. So all things being equal, it's probably better that a would be terrorist spends a year planning a weird chemical thing that hurts 10s of people, vs someone just waking up one morning and deciding to run over 10s of people with a truck.
There's a better chance of catching the first guy, and his plan is way more expensive in terms of time, money, access to capital like LLM time, etc. Sure someone could argue about pandemic potential, but lab origin is suspected for at least one influenza outbreak and a lot of people believe it about covid-19. Those weren't terrorists.
I guess theoretically, there may be cyberweapons that qualify as wmd, but those will be because of the systems they interact with. It's not the cyberweapon itself, it's the nuclear reactor accepting commands that lead to core damage.
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I'd love a reply on this. Common attack vectors I read on this forum include 1. powerful elite bribes existing labs in US to manufacture bioweapons 2. nation state sets up independent biotech supply chain and starts manufacturing bioweapons.
https://www.lesswrong.com/posts/DDtEnmGhNdJYpEfaG/joseph-miller-s-shortform?commentId=wHoFX7nyffjuuxbzT
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1. This has been an option for decades, a fully capable LLM does not meaningfully lower the threshold for this. It's already too easy.
2. This has been an option since the 1950s. Any national medical system is capable of doing this, Project Coast could be reproduced by nearly any nation state.
I'm not saying it isn't a problem, I'm just saying that the LLMs don't make it worse.
I have yet to find a commercial LLM that I can't make tell me how to build a working improvised explosive (I can grade the LLMs performance because I've worked with the USG on the issue and don't need a LLM to make evil).
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Makes sense, thanks for replying.
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Do you have a link/citation for this quote? I couldn't immediately find it.
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I first encountered it in chapter 18 of The Looming Tower by Lawrence Wright.
But here's a easily linkable online source: https://ctc.westpoint.edu/revisiting-al-qaidas-anthrax-program/
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If you combine the fact that power corrupts your world models with the general startup person being power hungry as well as AI Safety being a hot topic, you also get a bunch of well meaning people doing things that are going to be net-negative in the future. I'm personally not sure that the VC model actually even makes sense for AI Safety Startups given some of the things I've seen in the space.
Speaking from personal experience I found that it's easy to skimp out on operational infrastructure like a value aligned board or a more proper incentive scheme. You have no time so instead you start prototyping a product yet that means you get this path dependence where if you succeed, you suddenly have a lot less time. As a consequence the culture changes because the incentives are now different. You start hiring people and things become more capability focused. And voila, you're now in a capabilities/AI safety startup and it's unclear what it is.
So get a good board and don't commit to something unless you have it in contract form or similar that you will have at least a PBC structure if not something even more extreme as the underlying company model. The main problem I've seen here is if your co-founder(s) is/are being cagey about it, I would move on to new people at least if you care about safety.
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I think what you're saying is that they need to be aligned.
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Best way to start an AI safety startup is get enough high status credentials and track record that you can ask your investors to go fuck themselves if they ever ask you to make revenue. Only half-joking. Most AI research (not product) companies have no revenue today, or are trading at an insane P/S multiple.
Silicon Valley episode: No revenue
The next PauseAI UK protest will be (AFAIK) the first coalition protest between different AI activist groups, the main other group being Pull the Plug, a new organisation focused primarily on current AI harms. It will almost certainly be the largest protest focused exclusively on AI to date.
In my experience, the vast majority of people in AI safety are in favor of big-tent coalition protests on AI in theory. But when faced with the reality of working with other groups who don't emphasize existential risk, they have misgivings. So I'm curious what people here will think of this.
Personally I'm excited about the protest and I've found the organizers of Pull the Plug to be very sincere and good to work with, but I've also set things up so that the brands of PauseAI UK and Pull the Plug are clearly distinct, so that our messaging remains clearly focused on the risks of future AI. For example, we have a separate signup page and we have our own demands focused on decelerating frontier development.
1"In my experience, the vast majority of people in AI safety are in favor of big-tent coalition protests on AI in theory"
is this true? I think many people (myself included) are worried about conflationary alliances backfiring (as we see to some extent in the current admin)
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I only have anecdata but I've talked to quite a few people and most people say it's is a good idea to use the myriad of other concerns about AI as a force multiplier on shared policy goals.
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Speaking only for myself, here: There's room for many different approaches, and I generally want people to shoot the shots that they see on their own inside view, even if I think they're wrong. But I wouldn't generally endorse this strategy, at least without regard for the details of how the coalition is structured and what it's doing.
I think our main problem is a communication problem of getting people to understand the situation with AI
* that model capabilities are steadily increasing;
* that the labs are aiming at literal superintelligence, no really, something more capable than any human alive, and then even better than that; that the labs are explicitly aiming to do an RSI, which looks increasingly likely to succeed;
* that there is not a known science of reliably controlling or shaping the motivations of superhuman AIs.
* that there are competitive pressures for all of the labs and all of the countries to beat their competitors, so slowing down or pausing requires international coordination.
These are slippery points to get across specifically because audiences tend to slip into visualizing something other than "actual strategic superintelligence", that is automating science and technological progress and capable of strategically outmaneuvering adversaries—even when I talk with people from the labs, they often tend to gravitate to a fuzzier vision that has the form factor of the current AI chatbots / agents, but is much more competent.
Most of the time, I'm trying to land these points, despite the slipperiness, and talking about present-day harms that don't have a through-line to the core alignment problems seem like more of a distraction than a help.
If we already had developed policies that would substantially improve the situation and were politically feasible, and we just needed to get a big enough coalition to get them implemented, I would feel differently.
But insofar as we have policies substantially help, they're rather radical (on the
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From https://pulltheplug.uk/:
I think this would probably be a disaster, given how misinformed and unwise large parts of the broad public have been on many other scientific issues (e.g. vaccines, GMOs, nuclear power).
The rest of their views doesn't inspire much confidence in their epistemics either:
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Citizen assemblies often involve selecting a small number of delegates who are then informed about the all of the details of the issue in depth, including by expert testimonies, which the delegates have the affordance to do because they're being paid for their time.
My understanding is that this works pretty well for coming to reasonable policy.
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Who's behind Pull the Plug? I don't see any details about it on their website.
On LessWrong, the frontpage algorithm down-weights older posts based on the time-since-posted, not the time-since-frontpaged. So, if a post doesn't get frontpaged until a few days after posting, then it's unlikely to get many views.
LessWrong has an autofrontpager that works a reasonable amount of the time. Otherwise, posts have to be manually frontpaged by a person. In my experience, this was always quite quick, but my most recent post was not frontpaged until 3 days after it was posted, so AFAICT it never actually appeared on the frontpage (unless you clicked "Load More").
I think the solution is to downweight posts based on the time-since-frontpaged.
If you downweigh posts based on the time-since-frontpaged then posts get a huge boost when they have a delay of getting frontpaged (since they then first show to everyone who has personal blog enabled on their frontpage, and can accumulate karma during this time, and then when they have their effective date reset have a huge advantage over posts that were immediately frontpaged, because the karma provides a much longer visibility window).
I don't really have a great solution to this problem. I think the auto-frontpager helps a lot, though of course only if we can get the error rate sufficiently down.
1I'd be happy, if the auto-frontpager is ~instant, to get the option "delay publishing until human review" if it declines frontpage. Whether something gets ~50% less karma than it would by default is a pretty major drop in the effectiveness of what is often many hours of work, I'd be fine with waiting a day or two to avoid that usually.
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That makes sense! I'll think about it, though probably fitting that complexity into the publishing process isn't worth it.
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Relatedly, I've been thinking about building a schedule-this-post-for-publication feature. If I publish a post at 10pm, it's often better to publish the next morning for visibility. My guess is this would be useful for Inkhaven Residents who finish writing near-midnight.
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If I could schedule, the frontpage review happened before publishing, and the schedule UI had "delay publishing until frontpage"[1] as a checkbox, this would be ~solved.
1. ^
I'd prefer this to "delay publishing until human review", as ~half a dozen times in the past few years I've appealed via Intercom and had a human-reviewed page retroactively frontpaged (usually a resource, which LW team's priors seem to be something like 'this won't be maintained' but will because I optimize a bunch for not leaving stale projects).
Examples which Rafe requested when I mentioned this: the following were all marked as personal blog until I intercom'd in and asked for a re-assessment
https://www.lesswrong.com/posts/JsqPftLgvHLL4Pscg/new-weekly-newsletter-for-ai-safety-events-and-training
https://www.lesswrong.com/posts/dEnKkYmFhXaukizWW/aisafety-community-a-living-document-of-ai-safety
https://www.lesswrong.com/posts/vxSGDLGRtfcf6FWBg/top-ai-safety-newsletters-books-podcasts-etc-new-aisafety (nudge didn't work for this one)
https://www.lesswrong.com/posts/MKvtmNGCtwNqc44qm/announcing-aisafety-training
https://www.lesswrong.com/posts/JRtARkng9JJt77G2o/ai-safety-memes-wiki
https://www.lesswrong.com/posts/x85YnN8kzmpdjmGWg/14-ai-safety-advisors-you-can-speak-to-new-aisafety-com
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Perhaps it could use time-since-frontpaged only if the karma is below some threshold.
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Or even better, at the the time when a post is frontpaged, check if will actually appear on the frontpage. If it is too old and has too little karma to be seen, then use the time-since-frontpaged.
LLM hallucination is good epistemic training. When I code, I'm constantly asking Claude how things work and what things are possible. It often gets things wrong, but it's still helpful. You just have to use it to help you build up a gears level model of the system you are working with. Then, when it confabulates some explanation you can say "wait, what?? that makes no sense" and it will say "You're right to question these points - I wasn't fully accurate" and give you better information.
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What if you say that when it was fully accurate?
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Then it will often confabulate a reason why the correct thing it said was actually wrong. So you can never really trust it, you have to think about what makes sense and test your model against reality.
But to some extent that's true for any source of information. LLMs are correct about a lot of things and you can usually guess which things they're likely to get wrong.
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Not OP but IME it might (1) insist that it's right, (2) apologize, think again, generate code again, but it's mostly the same thing (in which case it might claim it fixed something or it might not), (3) apologize, think again, generate code again, and it's not mostly the same thing.
Announcing PauseCon, the PauseAI conference.
Three days of workshops, panels, and discussions, culminating in our biggest protest to date.
Tweet: https://x.com/PauseAI/status/1915773746725474581
Apply now: https://pausecon.org
The next international PauseAI protest is taking place in one week in London, New York, Stockholm (Sunday 9th Feb), Paris (Mon 10 Feb) and many other cities around the world.
We are calling for AI Safety to be the focus of the upcoming Paris AI Action Summit. If you're on the fence, take a look at Why I'm doing PauseAI.
When I go on LessWrong, I generally just look at the quick takes and then close the tab. Quick takes cause me to spend more time on LessWrong but spend less time reading actual posts.
On the other hand, sometimes quick takes are very high quality and I read them and get value from them when I may not have read the same content as a full post.
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Interesting. I am concerned about this effect, but I do really like a lot of quick takes. I wonder whether maybe this suggests a problem with how we present posts.
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Quick takes are presented inline, posts are not. Perhaps posts could be presented as title + <80 (140?) character summary.
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I think the biggest problem with how posts are presented is it doesn’t make the author embarrassed to make their post needlessly long, and doesn’t signal “we want you to make this shorter”. Shortforms do this, so you get very info dense posts, but actual posts kinda signal the opposite. If its so short, why not just make it a shortform, and if it shouldn’t be a shortform, surely you can add more to it. After all, nobody makes half-page lesswrong posts anymore.
This. The struggle is real. My brain has started treating publishing a LessWrong post almost the way it'd treat publishing a paper. An acquaintance got upset at me once because they thought I hadn't provided sufficient discussion of their related Lesswrong post in mine. Shortforms are the place I still feel safe just writing things.
It makes sense to me that this happened. AI Safety doesn't have a journal, and training programs heavily encourage people to post their output on LessWrong. So part of it is slowly becoming a journal, and the felt social norms around posts are morphing to reflect that.
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In some ways the equilibrium here is worse, journals have page limits.
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I'd love to see the reading time listed on the frontpage. That would make the incentives naturally slide towards shorter posts, as more people would click and it would get more karma. Feels much more decision relevant than when the post was posted.
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Naive idea:
Get an LLM to generate a TLDR of the post and after the user finishes reading the post, have a pop-up "Was opening the post worth it, given that you've already read the TLDR?".
xAI claims to have a cluster of 200k GPUs, presumably H100s, online for long enough to train Grok 3.
I think this is faster datacenter scaling than any predictions I've heard.
They don't claim that Grok 3 was trained on 200K GPUs, and that can't actually be the case from other things they say. The first 100K H100s were done early Sep 2024, and the subsequent 100K H200s took them 92 days to set up, so early Dec 2024 at the earliest if they started immediately, which they didn't necessarily. But pretraining of Grok 3 was done by Jan 2025, so there wasn't enough time with the additional H200s.
There is also a plot where Grok 2 compute is shown slightly above that of GPT-4, so maybe 3e25 FLOPs. And Grok 3 compute is said to be either 10x or 15x that of Grok 2 compute. The 15x figure is given by Musk, who also discussed how Grok 2 was trained with less than 8K GPUs, so possibly he was just talking about the number of GPUs, as opposed to the 10x figure named by a team member that was possibly about the amount of compute. This points to 3e26 FLOPs for Grok 3, which on 100K H100s at 40% utilization would take 3 months, a plausible amount of time if everything worked on almost the first try.
Time needed to build a datacenter given the funding and chips isn't particularly important for timelines, only for catching up to the frontier (as long as it's 3 months vs. 6 m...
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Can we assume that Gemini 2.0, GPT-4o, Claude 3.5 and other models with similar performance have a similar compute?
For Claude 3.5, Amodei says the training time cost "a few $10M's", which translates to between 1e25 FLOPs (H100, $40M, $4/hour, 30% utilization, BF16) and 1e26 FLOPs (H100, $80M, $2/hour, 50% utilization, FP8), my point estimate is 4e25 FLOPs.
GPT-4o was trained around the same time (late 2023 to very early 2024), and given that the current OpenAI training system seems to take the form of three buildings totaling 100K H100s (the Goodyear, Arizona site), they probably had one of those for 32K H100s, which in 3 months at 40% utilization in BF16 gives 1e26 FLOPs.
Gemini 2.0 was released concurrently with the announcement of general availability of 100K TPUv6e clusters (the instances you can book are much smaller), so they probably have several of them, and Jeff Dean's remarks suggest they might've been able to connect some of them for purposes of pretraining. Each one can contribute 3e26 FLOPs (conservatively assuming BF16). Hassabis noted on some podcast a few months back that scaling compute 10x each generation seems like a good number to fight through the engineering challenges. Gemini 1.0 Ultra was trained on either 77K TPUv4 (according to The Information) or 14 4096-TPUv4 pods (acc...
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Thank you. In conditions of extreme uncertainty about the timing and impact of AGI, it's nice to know at least something definite.
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It seems that we are already at the GPT 4.5 level? Except that reasoning models have confused everything, and increasing OOM on output can have the same effect as ~OOM on training, as I understand it.
By the way, you've analyzed the scaling of pretraining a lot. But what about inference scaling? It seems that o3 has already used thousands of GPUs to solve tasks in ARC-AGI.
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The 200k GPU number has been mentioned since October (Elon tweet, Nvidia announcement), so are you saying that that they managed to get the model trained so fast is what beat the predictions you heard?
Crossposted from https://x.com/JosephMiller_/status/1839085556245950552
1/ Sparse autoencoders trained on the embedding weights of a language model have very interpretable features! We can decompose a token into its top activating features to understand how the model represents the meaning of the token.🧵
2/ To visualize each feature, we project the output direction of the feature onto the token embeddings to find the most similar tokens. We also show the bottom and median tokens by similarity, but they are not very interpretable.
3/ The token "deaf" decompos...
Claude 3.7's annoying personality is the first example of accidentally misaligned AI making my life worse. Claude 3.5/3.6 was renowned for its superior personality that made it more pleasant to interact with than ChatGPT.
3.7 has an annoying tendency to do what it thinks you should do, rather than following instructions. I've run into this frequently in two coding scenarios:
- In Cursor, I ask it to implement some function in a particular file. Even when explicitly instructed not to, it guesses what I want to do next and changes other parts of the code as well
BBC Tech News as far as I can tell has not covered any of the recent OpenAI drama about NDAs or employees leaving.
But Scarlett Johansson 'shocked' by AI chatbot imitation is now the main headline.
LessWrong LLM feature idea: Typo checker
It's becoming a habit for me to run anything I write through an LLM to check for mistakes before I send it off.
I think the hardest part of implementing this feature well would be to get it to only comment on things that are definitely mistakes / typos. I don't want a general LLM writing feedback tool built-in to LessWrong.
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Don't most browsers come with spellcheck built in? At least Chrome automatically flags my typos.
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LLMs can pick up a much broader class of typos than spelling mistakes.
For example in this comment I wrote "Don't push the frontier of regulations" when from context I clearly meant to say "Don't push the frontier of capabilities" I think an LLM could have caught that.
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There's a good overview of his views expressed in this manifold thread.
Basically:
* Caloric restriction works, however it impedes his productivity ("ability to think").
* Exercise isn't effective in promoting weight loss or reducing weight gain due to compensatory metabolic throttling during non-exercise times
* His fat metabolism is poor, because his fat cells are inclined to leach glucose and triglycerides from his bloodstream to sustain themselves rather than be net contributors, and the effect is that muscle loss makes up the difference, leading to unfavourable implications for body composition, energy, and overall health. In essence "good genetics" for fat loss = "Fat cells that are readily and efficiently broken down for energy", whereas "bad genetics" for fat loss = "fat cells that are resistant to being used as energy".
If you missed it, Veo 3, Google's text-to-video model was just launched and is very impressive. And the videos have audio now.
https://www.reddit.com/r/ChatGPT/comments/1krmsns/wtf_ai_videos_can_have_sound_now_all_from_one/
There are two types of people in this world.
There are people who treat the lock on a public bathroom as a tool for communicating occupancy and a safeguard against accidental attempts to enter when the room is unavailable. For these people the standard protocol is to discern the likely state of engagement of the inner room and then tentatively proceed inside if they detect no signs of human activity.
And there are people who view the lock on a public bathroom as a physical barricade with which to temporarily defend possessed territory. They start by giving t...
Rationalist twitter rage-bait recipe:
Rationalist: *reasonable, highly decoupling point about the holocaust*
Everyone: *highly coupling rage*
Rationalist: *shocked pikachu face*