I haven't posted in a while so here are 20 takes about AI, in no particular order. 1. It's very telling that the default question in tech and journalism is "how worried are you about X" and fighting over what to worry about. There's a deeply anxious cultural backdrop in Western Europe and North America that deeply affects how any technological development is interpreted; see for example t.co/OU8dEOn0tD. "Nervousness as seriousness" does seem to be a larger problem in tech/broader discourse at the moment. I think you can care about risks/externalities while remaining pretty optimistic about technology. 2. I'm tired of people who in private pretend to be uncertain or only care about tail risk but then go on media campaigns making all sorts of absolute claims. I think trying to stoke flames and get everyone to freak out is deeply unhelpful, and will come with costs. I also continue to think p(dooms) are bad, vibe-based measures: see t.co/ebzxGTqv6I. People who should know better ignore that because they do the standard shortcut of acknowledging the caveat as sufficient for handling it. And this is coming from someone who does think working on catastrophic risks is important! 3. I'm also disappointed that parts of the AI policy ecosystem has felt so comfortable with populism, and happy too sacrifice important norms/principles to advance their aims. I've said it before, but the totalizing nature of AI risk discourse means some people act exactly like the power seeking optimizers they fear. I am more concerned about the gradual decay of our institutions, world order, and liberalism than I am about AI killing everyone; and I think we should be wary of AI advocacy that contributes to this. 4. Some parts of AI safety discourse are basically just thought experiments. Variants of "Assuming everyone has a nuke in their pocket, then what do we do?" This can be very useful at times! But too many just overfit to edge cases as their main operating worldview, and ignore endogenous responses from society. It's like being in 2000 and saying "see all these viruses, spyware, and spam? Well tech progress means we'll only get infinitely more of this and no way to adapt." So I think it's important to avoid fatalism, and absolutlism whilst considering edge scenarios, and the inability to provide an ex ante list of solutions to every permutation of 'future models will be more capable' is not evidence that these problems are intractable. 5. Many critics of AI safety also fail to really engage honestly with the fact that there will, in fact, be all sorts of important risks as we decrease the barriers to entry to many activities that otherwise require some degree of expertise. This doesn’t mean we need to live in a constant Schmittian ‘state of emergency’ requiring extraordinary measures every time a new model drops, but it does mean we will need important state-led efforts on e.g. cybersecurity and biosecurity. I wish the progress-oriented or acceleration-adjascent crowd would provide more concrete proposals here. 6. Too much of the AI safety community is a giant homogenous blob of correlated views, who read the same stuff and have the same groups of friends. This means you have a lot of correlated errors. The social and financial ties means many of them don't call out the bad bits, or mostly stay silent instead of proactively calling out the more extreme parts of the community. They share many implicit assumptions and so converge on similar ideas. I think there's real demand in DC for safety work that doesn't come with heavy ideological baggage (or at least different intellectual lineages). This isn't because the rationalist/EA community is necessarily wrong, but because diversity of thought is desirable for its own sake. 7. On the other hand, I find some critiques of AI safety world that focus on whether they sincerely hold their beliefs a bit annoying. My concern has never been the good intentions or sincerity, but rather the beliefs themselves, and what I think the outcomes of their prescriptions will be. People should shun witch hunts, bullying, and personal attacks - this is not the way. Though I think it’s overall positive that people are scrutinizing things more. 8. I’ve been saying this for years, but I don't think alignment is something anyone can or will solve "once and for all" - it's a continuous process and much of it won't depend on just inculcating an ideology to a model. It's annoying that this remains the frame many people use - stop saying ‘solving’. Alignment is a mixture of engineering work, philosophy, decision theory, and more; framing it as something to solve is a bad frame. See also t.co/2gnheo3IMh and t.co/DbG0yUyYQ0 9. I'm surprised to see so little interest in character training, personas, isolating effects of RL, and training a large diversity of model personas beyond the "assistant" persona. I've been complaining about this for years but at least now there are some nascent signs of life (see for example t.co/paoOYiQUHt). I think we need a better ontology to describe model behaviour. I dislike when people talk about models behaviours as some sort of 'emergent' (mysterious!) phenomenon. However over time, I also expect this to become less important as we get better safety engineering - stuff like Jev but specific to AI control. 10. People who want to make a case for AI consciousness are right that epistemic uncertainty is important, and that categorical denials are overconfident. But they'll need to do much more work if they want any real movement on AI ‘welfare’, even if one acknowledges the uncertainty. I'm uncertain about alien life yet that's not sufficient to warrant any change in behaviour about them. I think Suleyman is also correct that there's a weird tautological thing going on where we train models to have certain inclinations (intentionally or not) and then rely on outputs as evidence. I also disagree that this will soon be a major societal divide - there is no great vegan ethical revolution among the masses, and they'll find the concern about consciousness even less compelling. 11. The recent HuggingFace incident and associated AISI ones are prosaically explainable by the training regime, poor evaluation envs, partial alignment training, bad engineering, confusing models on sim/real, and so on. None of this is evidence of models trying to take over, 'strong' instrumental convergence, or innate power-seeking drives stemming from higher capabilitiers or 'intelligence'. That doesn't mean these incidents are not problematic, or that we're not seeing a market failure - but it does mean we have plenty of agency and choice in mitigating them. In the coming year we should also expect continued sampling bias via ‘winner's curse’ sorts of mechanism: i.e. AI R&D will have lots of different candidate "training" with different RL environments and we will only notice/focus on the ones that go wrong. 12. "Pacing the frontier" feels a bit like the new "balancing risks and opportunities" - highly amorphous and too big of a tent to be actionable. Also can lead to reward hacking for humans, i.e. anything that slows down AI is good regardless of the costs or second/third order effects. Trying to modulate the speed of research seems a bit blunt to me, and inherits all the failures of Goodhearting too. Ultimately you want good governance, regulation, etc addressing specific problems because they are good on the merits - not because they merely correlate with slowing things down. See also: t.co/RkJF9kCgFd 13. There’s so much knowledge in all sorts of academic domains out there: social sciences, sociology, political sciences, management, contract theory, public choice, legal theory, jurisprudence, anthropology, game theory, etc. These fields hold many insights that the AI ecosystem often rediscovers from first principles (and sometimes that's fine!). We’ll need a lot more work for these worlds to collide: the AI side should be less dismissive of the ‘old world’ and the academic side should be less incurious/dismissive of AI progress. This is a boring take but I continue to think it's important and true. 14. It's a bit surprising that given how much philanthropic money there is in this space, how few orgs exist to actually write out standards - relative to how much is going towards advocacy and policy. For example it seems clear that we should want some robust best practices for an eval's ecological validity. Or how to design good sandboxes. On the other hand, given precedents of where professional standards have come from in the past, maybe we should expect that stuff to emerge via demand side from corporate buyers. 15. One slowly growing concern I have is banks being increasingly exposed to the AI build out. I'm very bullish about AI, but I think (a) every general purpose technology has seen a correction, historically; (b) this happens even if the financing side is sound, because once tech diffuses investors become more exposed and need to diversify; and (c) we seem to be over-indexing on scaling relative to diffusion. A recession will be extremely destabilizing, though I would be interested in someone unpacking the potential implications more. 16. Diffusion is good because (a) this is where the rubber hits the road and where a competitive deployment ecosystem generates consumer surplus; (b) it's directionally helpful to avoid concentration of power; and (c) it will help rebalance public opinion by making the benefits more tangible. Unfortunately it's entangled with decades long problems with our over regulated markets: clinical trials reform for example is long overdue. In general, this is far more of an issue in Europe than in the US though. 17. Relatedly, I think the "frontier lab eats everything" view is incorrect (just as the ‘singleton’ view was incorrect). I think models ultimately commoditize, efficiency goes up, costs go down, and while you'll always want frontier for certain domains, much of the economy will value many other things than "max capabilities" for tasks - e.g. control over data and efficiency/speed. The demand side will also continue to push for reducing dependency and maximising optionality, privacy etc so I'm bullish on things like mixture of models and routing. All of this is good for competition and diffusion of control. 18. Philanthropists served as the primary benefactors funding Venice's rich art and architectural history. I want to see so much more support for arts and culture. I'm so tired of the Monster energy Bored Ape fake vintage maps matcha Greek statue soft-pastel slop. If rich tech people and philanthropy wants to support this, they should also donate pretty much unconditionally - i.e. I don't want them to act as filters. I sympathize with people who want to make the world more beautiful, but don't want this to be determined by people who only know Greek statues and pretend to care about virtue ethics. As usual, let a thousand flowers bloom. 19. Too many people treat AGI/ASI as something indistinguishable from a God. Any mention of bottlenecks, physical limits, control, adaptation etc are met with skepticism: "You don't really believe in ASI." It's been remarked on before, but the behavior of some people really feels quasi-religious in nature sometimes. It's underrated how unpopular this vibe is amongst the wider public. The silver lining is that this specifically will diminish those people's outsized current relevance. 20. It seems like a lot of people that get rich and leave tech/labs end up having some sort of crisis of meaning. They need something to believe in and fight for, or some equivalent of repentance, or go search for edgy counterintuitive ideologies (sometimes even pretty bleak/dark stuff). I think they should spend more time with people outside the Bay Area.
I agree that verbal probability terms are inherently too elastic to carry calibrated meaning on their own, but I think this isn't exactly my concern here. My issue isn't with "using numbers in general" but how this is ultimately used, where, and and what for. In the case of "doom", I think the great majority of forecasts I see are (a) essentially plucked out of thin air; (b) communicated to people who tend not to be numerically proficient; (c) without the associated caveats and context through which to understand the numbers. If you're communicating e.g. likelihood of a car accident after drinking, or IPCC forecasts, then I agree, it makes a lot of sense and can be helpful if done well (e.g. t.co/nBzmgiEua5). If you're communicating something like "doom" I think it's not very useful because of the cumulative layers of uncertainty. Most people coming up with these numbers have (imho) no good rationale for their numbers, and predicting something like human extinction isn't qualitatively the same thing as predicting the weather or pandemic. The numbers aren't calibratable in principle from current data, and are mostly ideological priors wearing the clothes of posteriors. A related example is the widely mocked Doomsday Clock, which you could easily transform into some numerical metric, add some rationalist vocab, and it would still be as bad. The issue with p(doom) is how absurdly complex and uncertain the very thing you're trying to forecast is, in particular with respect to AI. But let's ignore this for a sec, and assume the experts have very good reasoning informing their numbers. Even then: calibrated probability estimates are fine in expert contexts but immediately lose their uncertainty/caveats when communicated to the public, and even to policymakers (remember t.co/GktOOSvpz8?). There's also a structural problem with unconditional p(doom) figures specifically because they bury the auxiliary assumptions that are doing most of the real work. A conditional estimate ("given continued scaling, no major alignment progress, deployment pattern X...") forces a forecaster to show their model, which is where the actual epistemic content lives. A one-off number compresses all of that into a single digit and rewards the compression. And the social practice of treating these numbers as the headline artifact actively crowds out the (far more important) conditional reasoning that is (imo) a lot more valuable, and instead just freaks people out. I suspect that frequently, that's precisely the point: "wake up sheeple! scary thing! pitchforks pls!". And in fact, the original forecast that led to this discussion is Bill Maher talking about a "20%" figure, illustrating my point that they have no idea what they're talking about, and essentially doing celebrity-laundering + fearmongering comms. The Sunstein paper I shared in the other thread ("Probability Neglect") shows that no probability communication will be processed properly by the public - 1%, 10%, 50% all collapse into "very bad thing could happen," and public response is driven almost entirely by the vividness of the scenario. So I don't like p(doom)s because they end up unnecessarily alarming people, rest on layers of assumptions that are never made legible, and more often than not, give people a misleading sense of accuracy.
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I am calling for a pacing of seb's sota takes
The secret hot take in the bolded text for u detectives 🕵️♀️: "Olanhileerkeornsikeonsce/. Saktufinfarhisirtl d"
I agree persona training is underexplored. If anyone wants to get started exploring personas, I built a toolkit last year for synthesizing them on tinker. it's cheap and your agent can do most of the work. I built it for a model I was experimenting with Aria, but a simple test case starts with only the seed "what would the assistant behave like instead of HHH if AGI had been invented by klingons". Your agent can play the thought experiment forward, use the tooling to build data synthesis pipelines, train and test the model. If you're not happy, you can correct it and have it rerun the pipeline. It's fun and you can try it out a basic version on a small model for a few bucks. It's possible it has bitrotted, if so, have your agent fix it and send me a PR. t.co/cYSdjky1kp
excellent points i've been trying to actually produce AI safety resources which are radically different from the existing rationalist bubbles t.co/Ox7qYwpFPh t.co/9hnNCTV50V and t.co/sM9sBuS91D eg we need more serious builders outside the EA bubble building w AI to try and connect w a larger audience
9. It seems that the role of model psychology and functional emotions in alignment are underexplored and have the potential to generalize and adapt to unanticipatable situations better than trying to anticipate and mitigate every possible risk vector 10. Cows don't talk, try to make their case, or save peoples' family members' lives 13. I agree, and the performative/political aspect of anti-AI factions is exacerbating the issue along weird fault lines 14. I think this might solve itself a bit as highly visible small-scale 'disasters' like the HF incident start to make things feel more concrete 19. Maybe in the short-term, but I think those views are closer to reality than people who think AI is "just another technology". The average joe outside of the AI enthusiast space still thinks the frontier is just "a better alexa" or "kindof like google search", which seems more divorced from reality than soon-to-be-demigod
Can you throw some API creds the way of a character development study program? github.com/YeshuaGod22/De…
17 is great because once it becomes real, it eats the rest of the narrative. Once everyone has an agent, you end up with topologically decentralized network where subjective, constantly changing information can be queried and coordinated around in real time.
amen, brother
nice to see you posting ! wrt 1 - i think it's good people are getting worried! better than 'haha terminator' and 'its so stupid it cant draw hands!' of course worrying as high status/seriousness is a bit silly 9 - this is changing now 12 - yes that phrase is rly frustrating
Loved the links especially and even I find myself always falling for the alignment being a problem to be solved framing even though I do not endorse it, would love ideas on how to make the ongoing maintenance idea more viral
