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The Internet seems to agree. I wonder why I remember "edit time addition".
ebenezer-dukakis on Stephen Fowler's ShortformI downvoted this comment because it felt uncomfortably scapegoat-y to me. If you think the OpenAI grant was a big mistake, it's important to have a detailed investigation of what went wrong, and that sort of detailed investigation is most likely to succeed if you have cooperation from people who are involved. I've been reading a fair amount about what it takes to instill a culture of safety in an organization, and nothing I've seen suggests that scapegoating is a good approach.
Writing a postmortem is not punishment—it is a learning opportunity for the entire company.
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Blameless postmortems are a tenet of SRE culture. For a postmortem to be truly blameless, it must focus on identifying the contributing causes of the incident without indicting any individual or team for bad or inappropriate behavior. A blamelessly written postmortem assumes that everyone involved in an incident had good intentions and did the right thing with the information they had. If a culture of finger pointing and shaming individuals or teams for doing the "wrong" thing prevails, people will not bring issues to light for fear of punishment.
Blameless culture originated in the healthcare and avionics industries where mistakes can be fatal. These industries nurture an environment where every "mistake" is seen as an opportunity to strengthen the system. When postmortems shift from allocating blame to investigating the systematic reasons why an individual or team had incomplete or incorrect information, effective prevention plans can be put in place. You can’t "fix" people, but you can fix systems and processes to better support people making the right choices when designing and maintaining complex systems.
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Removing blame from a postmortem gives people the confidence to escalate issues without fear. It is also important not to stigmatize frequent production of postmortems by a person or team. An atmosphere of blame risks creating a culture in which incidents and issues are swept under the rug, leading to greater risk for the organization [Boy13].
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We can say with confidence that thanks to our continuous investment in cultivating a postmortem culture, Google weathers fewer outages and fosters a better user experience.
https://sre.google/sre-book/postmortem-culture/
If you start with the assumption that there was a moral failing on the part of the grantmakers, and you are wrong, there's a good chance you'll never learn that.
alexander-gietelink-oldenziel on Alexander Gietelink Oldenziel's ShortformIn my mainline model there are only a few innovations needed, perhaps only a single big one to product an AGI which just like the Turing Machine sits at the top of the Chomsky Hierarchy will be basically the optimal architecture given resource constraints. There are probably some minor improvements todo with bridging the gap between theoretically optimal architecture and the actual architecture, or parts of the algorithm that can be indefinitely improved but with diminishing returns (these probably exist due to Levin and possibly.matrix.multiplication is one of these). On the whole I expect AI research to be very chunky.
Indeed, we've seen that there was really just one big idea to all current AI progress: scaling, specifically scaling GPUs on maximally large undifferentiated datasets. There were some minor technical innovations needed to pull this off but on the whole that was the clinger.
Of course, I don't know. Nobody knows. But I find this the most plausible guess based on what we know about intelligence, learning, theoretical computer science and science in general.
programcrafter on Why you should learn a musical instrumentWhat I didn't know is how immediately thought-provoking it would be to learn even the most basic things about playing music. Maybe it's like learning to program, if you used a computer all the time but you never had one thought about how it might work.
That comparison is also thought-provoking) Thinking for a minute yielded that programming may be considered quite similar to playing music, but differs that in programming you do not need to do most things in any specific order. For example, if I have a dataset of a competition participants, it doesn't matter whether I deduplicate names or remove disqualified entries first.
erich_grunewald on Ilya Sutskever and Jan Leike resign from OpenAIEverything that happened since then has made it clear that this is not the case; that all these big flashy commitments like Superalignment were just safety-washing [LW · GW] and virtue signaling. They were only going to do alignment work inasmuch as that didn't interfere with racing full-speed towards greater capabilities.
It's not clear to me that it was just safety-washing and virtue signaling. I think a better model is something like: there are competing factions within OAI that have different views, that have different interests, and that, as a result, prioritize scaling/productization/safety/etc. to varying degrees. Superalignment likely happened because (a) the safety faction (Ilya/Jan/etc.) wanted it, and (b) the Sam faction also wanted it, or tolerated it, or agreed to it due to perceived PR benefits (safety-washing), or let it happen as a result of internal negotiation/compromise, or something else, or some combination of these things.
If OAI as a whole was really only doing anything safety-adjacent for pure PR or virtue signaling reasons, I think its activities would have looked pretty different. For one, it probably would have focused much more on appeasing policymakers than on appeasing the median LessWrong user. (The typical policymaker doesn't care about the superalignment effort, and likely hasn't even heard of it.) It would also not be publishing niche (and good!) policy/governance research. Instead, it would probably spend that money on actual PR (e.g., marketing campaigns) and lobbying.
I do think OAI has been tending more in that direction (that is, in the direction of safety-washing, and/or in the direction of just doing less safety stuff period). But it doesn't seem to me like it was predestined. I.e., I don't think it was "only going to do alignment work inasmuch as that didn't interfere with racing full-speed towards greater capabilities". Rather, it looks to me like things have tended that way as a result of external incentives (e.g., looming profit, Microsoft) and internal politics (in particular, the safety faction losing power). Things could have gone quite differently, especially if the board battle had turned out differently. Things could still change, the trend could still reverse, even though that seems improbable right now.
alexander-gietelink-oldenziel on Alexander Gietelink Oldenziel's ShortformMy timelines were not 2026. In fact, I made bets against doomers 2-3 years ago, one will resolve by next year.
I agree iterative improvements are significant. This falls under "naive extrapolation of scaling laws".
By nanotech I mean something akin to drexlerian nanotech or something similarly transformative in the vicinity. I think it is plausible that a true ASI will be able to make rapid progress (perhaps on the order of a few years or a decade) on nanotech. I suspect that people that don't take this as a serious possibility haven't really thought through what AGI/ASI means + what the limits and drivers of science and tech really are; I suspect they are simply falling prey to status-quo bias.
ebenezer-dukakis on Stephen Fowler's Shortformendorsing getting into bed with companies on-track to make billions of dollars profiting from risking the extinction of humanity in order to nudge them a bit
Wasn't OpenAI a nonprofit at the time?
alexander-gietelink-oldenziel on D0TheMath's ShortformCan somebody explain to me what's happening in this paper ?
ebenezer-dukakis on Stephen Fowler's Shortformadversarial dynamics present in such a strategy
Are you just referring to the profit incentive conflicting with the need for safety, or something else?
I'm struggling to see how we get aligned AI without "inside game at labs" in some way, shape, or form.
My sense is that evaporative cooling is the biggest thing which went wrong at OpenAI. So I feel OK about e.g. Anthropic if it's not showing signs of evaporative cooling.
gwern on Language Models Model UsYes, I've never had any difficulty replicating the gwern identification: https://chatgpt.com/share/0638f916-2f75-4d15-8f85-7439b373c23c It also does Scott Alexander: https://chatgpt.com/share/298685e4-d680-43f9-81cb-b67de5305d53 https://chatgpt.com/share/91f6c5b8-a0a4-498c-a57b-8b2780bc1340
One interesting thing is that the extensive reasoning it gives may not be faithful. Notice that in identifying Scott Alexander's recent Reddit comment, it gets his username wrong - that username does not exist at all. And in my popups comment, I see no mention that points to LessWrong, but since I was lazy and didn't copyedit that comment, it is much more idiosyncratic than usual; so what I think ChatGPT-4o does there is immediately deduce that it's me from the writing style & content, infer that it could not be a tweet due to length or a Gwern.net quote because it is clearly a comment on social media responding to someone, and then guesses it's LW rather than HN, and presto.3