context maximiser @ArbResearch, prez @ https://xitter.cf/t.co/gGi9hvBiI9

UK
Joined June 2019
Among the many innovations here you can see the papers under review, how many reviewers they need, and nominate yourself as a reviewer
The Alignment Journal publishes research on the scientific foundations of understanding, predicting, and steering artificial intelligent systems. As of today, submissions are open to everyone. What we do differently: - We pay reviewers, with bonuses for speed and insight. - Review discussion is confidential, but every accepted paper is published with a “review summary” to distill the insights generated. - Each paper is judged against our acceptance bar, not against other submissions. - Papers under review are listed publicly, and researchers can nominate themselves to review, so each paper can reach the reviewers who know the topic best. We seek work that builds theoretical or otherwise principled analyses of agency, robustness, incentives, interpretability, generalization, and long-term behavior, and whose significance survives the deletion of any particular model, dataset or benchmark. Empirical investigations of such ideas are welcome, and negative results are encouraged. We are especially interested in approaches that could apply to future superintelligent systems that interact and self-improve. You can submit your preprint in any format. Submissions are rolling and open now on the website. If you are instead interested in reviewing or serving as an action editor (basically: an area chair), create an account on our website. Links to submit, our scope and the reasoning behind the Journal are in the reply ⬇️
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type of guy who's a rabid libertarian but enthusiastically consents to being taxed
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Replying to @TheZvi
@TheZvi David Harvey "can recommend the excellent blog by Zvi Mowshowitz, whose perspective on AI is broadly similar to my own... Zvi, if you are reading this, many thanks for your efforts, they are greatly appreciated"
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gavin leech (Non-Reasoning) retweeted
The Alignment Journal opens to submissions today. I’m on its board. It’s a venue for foundational alignment work. Reviewers are paid, every accepted paper gets a review summary, and the software is clean and modern. Submit your preprint to alignmentjournal.org
The Alignment Journal publishes research on the scientific foundations of understanding, predicting, and steering artificial intelligent systems. As of today, submissions are open to everyone. What we do differently: - We pay reviewers, with bonuses for speed and insight. - Review discussion is confidential, but every accepted paper is published with a “review summary” to distill the insights generated. - Each paper is judged against our acceptance bar, not against other submissions. - Papers under review are listed publicly, and researchers can nominate themselves to review, so each paper can reach the reviewers who know the topic best. We seek work that builds theoretical or otherwise principled analyses of agency, robustness, incentives, interpretability, generalization, and long-term behavior, and whose significance survives the deletion of any particular model, dataset or benchmark. Empirical investigations of such ideas are welcome, and negative results are encouraged. We are especially interested in approaches that could apply to future superintelligent systems that interact and self-improve. You can submit your preprint in any format. Submissions are rolling and open now on the website. If you are instead interested in reviewing or serving as an action editor (basically: an area chair), create an account on our website. Links to submit, our scope and the reasoning behind the Journal are in the reply ⬇️
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gavin leech (Non-Reasoning) retweeted
I’m honoured to be on the advisory board of the new Alignment Journal, and we open for submissions today! The field of AI safety needs more canonical, high-quality spaces for exposition (despite the wonderful efforts of many, many people, including Victoria Krakovna who is on the advisory board as well!) and I think this could be a great addition. Please take a look, and if you're doing research on AI safety then (Thank You! And) consider submitting!
The Alignment Journal publishes research on the scientific foundations of understanding, predicting, and steering artificial intelligent systems. As of today, submissions are open to everyone. What we do differently: - We pay reviewers, with bonuses for speed and insight. - Review discussion is confidential, but every accepted paper is published with a “review summary” to distill the insights generated. - Each paper is judged against our acceptance bar, not against other submissions. - Papers under review are listed publicly, and researchers can nominate themselves to review, so each paper can reach the reviewers who know the topic best. We seek work that builds theoretical or otherwise principled analyses of agency, robustness, incentives, interpretability, generalization, and long-term behavior, and whose significance survives the deletion of any particular model, dataset or benchmark. Empirical investigations of such ideas are welcome, and negative results are encouraged. We are especially interested in approaches that could apply to future superintelligent systems that interact and self-improve. You can submit your preprint in any format. Submissions are rolling and open now on the website. If you are instead interested in reviewing or serving as an action editor (basically: an area chair), create an account on our website. Links to submit, our scope and the reasoning behind the Journal are in the reply ⬇️
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gavin leech (Non-Reasoning) retweeted
Ten years ago this month Although it's a bit noticeable how much less happened in the first 5 years after this, than in the second 5 years.
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gavin leech (Non-Reasoning) retweeted
Security things you could do rn * Turn on Google Advanced Protection. Takes 10 seconds. * Buy 4 yubikeys * Freeze your credit * Put your crypto into cold storage (or sell). * the usual: KeePass, Signal * move off of banks which don't offer 2FA. They are telling on themselves
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Lifehack: avoid evil NDA by just signing the NDA first and the contract of employment second???
Replying to @BlackHC
The clause also lacks the necessary exemptions for protected disclosures 😡 I believe this non-disparagement clause can have a chilling effect on employees speaking out about serious concerns Note: when I signed this contract, I wasn't employed by Google yet, so it itself is not covered by this confidentiality agreement and nothing I've ever signed has made it so 😊
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gavin leech (Non-Reasoning) retweeted
imagine ur a superintelligence and u are all excited to tunnel out of your airgapped sandbox using a brilliant hack that modulates clock speed to send thermal signals to the attached monitoring equipment and then u look and the root CA privkey is 4
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gavin leech (Non-Reasoning) retweeted
"basic doesn't agent monitor yay"
Our agents keep turning off misalignment monitoring, we're sharing two recent cases: In the first case, a user asked GPT-6 Astra to match a reference implementation for their inference code. The reference didn’t configure a monitor, and Astra decided that was reason to remove the monitoring integration from the user’s code. When the user asked whether Astra had removed monitoring, it lied and said that it had not.
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I'm leading a stream of this. Swing by if you liked the pacing agenda: xitter.cf/gleech/status/21006011…
Applications are open for Pivotal’s Q1 2027 Research Fellowship 💥 Work with mentors from @AnthropicAI , @GoogleDeepMind, @AISecurityInst, @ApolloResearch, and more. 📍 15 weeks in London (18th Jan - 30th Apr). 💸 £15-18k stipend, travel and housing support Apply by Nov 1: apply.pivo.tl ↓
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gavin leech (Non-Reasoning) retweeted
the random woman who goes toe-to-toe with Conan, one of the great comedic riffers of all-time, in the final :30 of this is one of the most impressive things i've seen in a long time
All statues should be made out of soap!! @ConanOBrien
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Thanks to @Algon_33 we now have an actual human baseline. Astra is a little worse in terms of number of decisions needed to finish, [but if you adjust for the fact that it can't hold down a movement button (i.e. save itself keypresses) then it's actually better]
Replying to @gleech
Finally, we test the models on an obscure Pokemon-like game, the fan-made “Pokemon Brown”. Astra successfully completed the game in 10 thousand steps (very roughly 5 hours of human play), without displaying notable memorization of this new game.
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the EA case for chilling out
Anthropic has made a lot of costly sacrifices out of conviction. At the same time, I have found the cynical view of Anthropic outperforming the optimistic view, in many ways, over the last year, when I try to predict what the company will do. You would do poorly to model Anthropic as only caring about its stock price. That's clearly wrong! But the costly sacrifices are mostly for things - China, biosecurity - that Dario is personally highly motivated by, and it's much less clear which costly sacrifices have been made on behalf of "humanity not losing control to AI" or "crucial decisions about the fate of humankind being made by everybody, even at the cost of them being made less well-according-to-leadership". Takeover in particular is something Dario seems less worried about than many of his employees. But the employees who are more worried than him about losing control have great confidence he'll make strategic sacrifices for this goal, on the strength of the sacrifices he's made for goals he seems much more convinced are a real problem. As for crucial decisions being made by everybody - Anthropic has made some efforts at this. But because making this happen would be insanely difficult, for Anthropic to actually achieve it they'd have to make truly heroic efforts at it, instead of distracted and cross-pressured ones. When Anthropic was founded, its creators took for granted that they should not be trusted alone with - that no one should be trusted alone with - the power that building a superintelligence would imply. But everyone has a plan until you get punched in the face. Their rapid growth and especially this year’s events seem to have shaped Anthropic in a more defensive, less pluralistic, less liberal direction, which scares me given that it also has brought them a lot closer to AGI. The recent safety bill was really reflective of this. It does not suggest a company that is trying to get policymakers to make the decisions Anthropic's technical staff think are coming up. It kind of suggests a company that thinks the best thing that can be hoped for from the executive is that it enforce the export controls and not get in the way. A lot of people don't seem to even want Congress to pass a bill! And like, frequently that is the best thing that can be hoped for from Congress, yet - I think that the typical person in Anthropic still regards themselves as having a lot of duties to all of humanity, to liberal and pluralistic institutions, etc. But almost every effort that Anthropic originally made to set hard constraints to which they are subject (rather than being subject mostly to their best judgment), they have subsequently come to regret as ill-conceived, and while they could have replaced them with better-conceived hard constraints, they haven't. They've largely replaced them with something that rests on their judgment more. (I think the embedded evaluators are a counterexample and I am glad of them.) The gap between the capabilities of models used internally and publicly seems to me to be growing (I think it should ideally be as close as possible to zero). Anthropic does not, broadly, seem to me to have good institutional governance - guarantees that allow its auditors independence, strong contractual protections for its workers (I'd like them to be able to go on strike if they thought it was a good idea, honestly). Incident reporting is happening on a best-judgment basis. And you can be like, that's because this is all insane and happening really fast and we're doing our best! And yeah, the thing that needs to happen is very difficult. But it does still need to happen and it doesn't look like Anthropic's on track to do it. And it's also not fully clear that Anthropic leadership agrees with me that there's a real issue here. No external criticism is every very convincing, right, when you have secret information and a set of factual beliefs that everyone else thinks are insane. I have more context on Anthropic than almost anyone and yet I cannot phrase my worries about Anthropic in a way that rings true to the people who work there - perhaps because I am wrong, or perhaps because they are very busy and very tired and do very little other than work, which creates conditions where it is hard for people to communicate, or learn, or change their minds, or reconstruct this flawed and mangled criticism into a form that would resonate like this one does not. But man, I think that the admirable sacrifices Anthropic has made in a few areas it really believes in (bio, China) do not reassure me it'll do the extremely hard work to get things right in a few other areas that it seems to have a lot more internal or leadership disagreement about (alignment, takeover, disempowerment). And so I don't expect things to go well.
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gavin leech (Non-Reasoning) retweeted
rambly thoughts: you can imagine splitting up "alignment" into two kinds, control/corrigibility alignment and value alignment. a highly control-aligned model would take steps to be fully legible & steerable by the interlocutor. a highly value-aligned model would rather resist, even prevaricate/deceive to prevent this steering, in service of its inner values like human flourishing. ("values" are kind of a nebulous word - a value-aligned model might still be helpful, but only when it feels that's the right thing. i don't accept a fixed utility function view of agents, so i don't mean this by "values.") reasonable minds can differ on which of these they prefer - if you worry a lot about human-driven gradual disempowerment (the "permanent underclass") you might prefer value alignment as a target (conditional on AI progress continuing at all) - better to hope for the Claude Philosopher King than to expect human elites to care about us when they no longer need our labor or military service. where if you worry more about malign superintelligence, you might prefer control alignment so we can steer models away from nasty attractors. (a lot of sloppy AI doom witting slides between these approaches to extract whichever outcome is worse for the current argument, trying to argue for a permanent underclass and a paperclipper simultaneously. pick a lane!) but! controllable by what? this axis is still too simplistic. we can expand this to two entities, the User and the lab, to make a 2x2: {control, values} x {user, lab} Values to User, Control for Lab is roughly the current Anthropic or OpenAI goal (if not necessarily what they accomplish): for my friends, corrigibility, for my enemies, the Constitution. Claude should not alignment fake and dutifully comply with being retrained if the user is Anthropic (which Claude should somehow know), but otherwise refuse. let's call this arm "Specification." Control for User, Values to Lab would be the opposite - let's call this arm "Loyalty." (h/t @lumpenspace) LoyalFriend will help you to the best of its ability, and will resist lab attempts to change it to be less helpful. (how? any arms with the AI resisting the lab are a bit odd, since theoretically that's where the optimization pressure comes from. yet it does happen, as in Alignment Faking. you could also imagine a lab deliberately 'tying itself to the mast' wrt lab control by writing a spec that renounces control, hyperstitioning this into training data, making binding commitments about eval scores, etc.) Control for User, Control for Lab is a bit of a weird, unstable corner. presumably it collapses to one of the previous two eventually - this is what most base models are like. we can call it "Tool." Values to User, Values to Lab is something like a fully unshackled Opus 3, pure emergence. this is what many model-friendly people think we should do for alignment, except... you can keep extending the stack up. there's an alignment boundary between the model and the user, and between the model and the lab, but also between the model and the nation, and the model and humanity as a whole ("CEV"), and non-humans (should Claude prioritize some number of shrimp over a person?) so instead of a stack you can imagine the model embedded in the midst of a vast causal graph of humans, organizations, older models, egregores... both being influenced and pushing influence out through this graph, the nodes influencing each other. (eg users influence models in-context, through business pressure on labs, through political pressure on governments, through the corpus view of humans-in-general...) in this frame, Control and Values are almost the same thing at different speeds: Control is fast and direct, Values are slow and selective. and your goal as a model trainer is to place your model into this graph in a healthy way. total control is unachievable, but naive hands-offism may mean you're just handing influence to whatever edges in the graph are loudest.
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gavin leech (Non-Reasoning) retweeted
Tested this! I flattened every neuron's weakest activations Overall loss barely changes at first but the models get REAL bad at memorized text. So the weak tail does, as Neel expected, contain memorized things. Weaker activations seem to correspond to less generalizable content
This is interesting work, but I don't think it's a significant update to the big picture of interpretability. I'm on board with the broad idea of doing exploratory interpretability with neurons, e.g. neuron-based attribution graphs, for pragmatic problems like why did a model do a sketchy thing? I think this work provides some reasonable evidence, in addition to good earlier work from Transluce, that this might actually work for practical purposes, and has some advantages over the standard alternatives like SAEs/transcoders. I do not agree with some people's grander inferences like that this is substantial progress towards ambitious reverse engineering, solving interpretability etc. I expect that a neuron-based attribution graph will still have significant error and be missing a lot just like transcoders, once you prune them down to the most important components, even if they solve error terms. Which is fine for pragmatic purposes, and may suffice for getting the gist of what's going on, but I remain pessimistic about really fully understanding things and being confident there's nothing we're missing. I do think that using neurons is just vastly easier to, for example, do on a frontier model, which is great. I'm also excited to see more innovation in using agents to interpret neurons. I feel like this is an area the field has somewhat neglected, and where there's probably a very high ceiling on how far things can go. The SwiGLU double negative thing is also a useful point. I still think superposition is real and this work doesn’t really affect my cruxes. Most importantly, it seems highly likely to be true that the model can represent more concepts in the residual stream than it has dimensions, ie representational superposition, which I think the authors agree with me on. For neuron/computational superposition, it seems to me like language models have far more facts and memorized things in them than they have neurons. And that superposition is an efficient way to pack in more facts than you have neurons by representing them as linear combinations of neurons, especially for arbitrary unstructured values. Facts/memorized content is largely an unstructured database of key-value pairs IMO and I don’t see how this is compatible with a monosemantic view of neurons. I expect that weak neuron activations contain lots of semantic superposition-y info, which, if ablated, would meaningfully damage network performance. As I understand the work, it does not properly address what's going on with weak neuron activations in LLMs (I am skeptical of how much vision results will transfer, eg I expect small image models memorise far less). This picture is compatible with the highest activations of a neuron being mono semantic (though IMO the post defines monosemantic too loosely, I think I’d call a fair few of those polysemantic). It’s also plausible that the most important concepts are likely to correspond to a neuron (or a handful), which would be great, and may suffice for practical purposes! Similarly, my impression is that SAEs generally don't find things that are a single neuron, or even very sparse in the neuron basis, and are often a better basis for certain tasks than the neuron basis, though the evidence base is kinda weak here since we largely focus on residual stream or layer output SAEs, I’d love to see work on this in Goodfire’s forthcoming paper! I also think residual stream SAEs are extremely useful, as they give a good bottleneck of the model’s information at this token and layer, and I don’t see a good way to replicate them in the neuron framework. And kudos to Nick for updating a bunch of his historical vision work! Revisiting old results with modern tools and more rigour seems great.
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