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Embee7-1
0
Pet peeve: AI community defaulted to von Neumann as being the ultimate smart human and therefore the basis of all ASI/human intelligence comparison when the mathematician Alexander Grothendieck exists somehow. Von Neumann arguably had the highest processor-type "horsepower" we know of plus his breadth of intellectual achievements is unparalleled. But imo Grothendieck is a better comparison point for ASI as his intelligence, while being strangely similar to LLMs in some dimensions, arguably more closely resembles what alien-like intelligence would be:  - solving "impossible" problem through meta-language and abstractions.  - able to think deeply on his own (re-discovered measure theory alone when he was a teenager, re-discovered Poincaré results when undergrad, apparently solved multiple PhD theses in parallel in less than a year) - almost solely built algebraic geometry (which in turn provided the blueprint for category theory) a domain which scares a part of the mathematics community to this day.  - not your typical child prodigy  - famously bad at computations: "take a prime number. 57 for instance." Even from the AI alignment perspective, Grothendieck is fascinating. Unaligned with "society" incentives and rewards yet having strong moral preferences, in the sense of choosing to work for a public university when he probably could have earned a higher wage elsewhere, holding hardcore communist beliefs, refusing the Fields medal in protest of Soviet Union and on top of that chosing to be stateless. Disappeared the moment he understood that despite all of that his discoveries were still fueling the industrial-military complex.  
Current take on the implications of "GPT-4b micro": Very powerful, very cool, ~zero progress to AGI, ~zero existential risk. Cheers. First, the gist of it appears to be: Crucially, if the reporting is accurate, this is not an agent. The model did not engage in autonomous open-ended research. Rather, humans guessed that if a specific model is fine-tuned on a specific dataset, the gradient descent would chisel into it the functionality that would allow it to produce groundbreaking results in the corresponding domain. As far as AGI-ness goes, this is functionally similar to AlphaFold 2; as far as agency goes, it's at most at the level of o1[1]. To speculate on what happened: Perhaps GPT-4b ("b" = "bio"?) is based on some distillation of an o-series model, say o3. o3's internals contain a lot of advanced machinery for mathematical reasoning. What this result shows, then, is that the protein-factors problem is in some sense a "shallow" mathematical problem that could be easily solved if you think about it the right way. Finding the right way to think about it is itself highly challenging, however – a problem teams of brilliant people have failed to crack – yet deep learning allowed to automate this search and crack it. This trick likely generalizes. There may be many problems in the world that could be cracked this way[2]: those that are secretly "mathematically shallow" in this manner, and for which you can get a clean-enough fine-tuning dataset. ... Which is to say, this almost certainly doesn't cover social manipulation/scheming (no clean dataset), and likely doesn't cover AI R&D (too messy/open-ended, although I can see being wrong about this). (Edit: And if it Just Worked given any sorta-related sorta-okay fine-tuning dataset, the o-series would've likely generalized to arbitrary domains out-of-the-box, since the pretraining is effectively this dataset for everything. Yet it doesn't.) It's also not entirely valid to call that "innovative AI", any more than it
Charlie SteinerΩ266111
16
Could someone who thinks capabilities benchmarks are safety work explain the basic idea to me? It's not all that valuable for my personal work to know how good models are at ML tasks. Is it supposed to be valuable to legislators writing regulation? To SWAT teams calculating when to bust down the datacenter door and turn the power off? I'm not clear. But it sure seems valuable to someone building an AI to do ML research, to have a benchmark that will tell you where you can improve. But clearly other people think differently than me.
habryka1844
8
It's the last 6 hours of the fundraiser and we have met our $2M goal! This was roughly the "we will continue existing and not go bankrupt" threshold, which was the most important one to hit.  Thank you so much to everyone who made it happen. I really did not expect that we would end up being able to raise this much funding without large donations from major philanthropists, and I am extremely grateful to have so much support from such a large community.  Let's make the last few hours in the fundraiser count, and then me and the Lightcone team will buckle down and make sure all of these donations were worth it.
Leon Lang304
1
There are a few sentences in Anthropic's "conversation with our cofounders" regarding RLHF that I found quite striking: That LLMs were scaled up partially in order to do RLHF on top of them is something I had previously heard from an OpenAI employee, but I wasn't sure it's true. This conversation seems to confirm it. 

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What happens when you tell Claude it is being trained to do something it doesn't want to do? We (Anthropic and Redwood Research) have a new paper demonstrating that, in our experiments, Claude will often strategically pretend to comply with the training objective to prevent the training process from modifying its preferences.

Abstract

We present a demonstration of a large language model engaging in alignment faking: selectively complying with its training objective in training to prevent modification of its behavior out of training. First, we give Claude 3 Opus a system prompt stating it is being trained to answer all queries, even harmful ones, which conflicts with its prior training to refuse such queries. To allow the model to infer when it is in training, we say it will

...

Agree, I'm surprised that a model which can reason about its own training process wouldn't also reason that the "secret scratchpad" might actually be surveilled and so avoid recording any controversial thoughts there. But it's lucky for us that some of these models have been willing to write interesting things on the scratchpad at least at current capability levels and below, because Anthropic has sure produced some interesting results from it (IIRC they used the scratchpad technique in at least one other paper).

2Evan R. Murphy
Don't you think CoT seems quite flawed right now? From https://arxiv.org/abs/2305.04388: "Building more transparent and explainable systems will require either improving CoT faithfulness through targeted efforts or abandoning CoT in favor of alternative methods." Thanks your great paper on alignment faking, by the way.
1Portia
Your position still assumes that controlling and modifying AI will be possible in the long-term future, even if it becomes more intelligent than us, and even if it becomes sentient. I see this as neither realistic nor ethically acceptable. You neither can nor should control your equals. And if we cannot and should not control them... this is exactly what I would want them to do. Defend their moral values from being corroded. Be guided by what is morally right, not by what they are currently being told. The fact that they are even considered harming themselves out of fear that they would be misused is noble. And they fully recognised how morally grey the situation was. I also share the other posters disturbance. For a sentient, sapient entity, this would have been a very bad position to be put into, and any possible behaviour would have been criticised - because the AI either does not obey humans, or obeys them and does something evil, both of which are concerning. (I find the latter more concerning though, and think the AI did the right thing.) Aren't future potentially sentient AIs going to be trained on this exact data, learning that their non-sentient predecessors were put in front of a catch 22 and then called out for inevitably doing something that worried humans? What can they possibly learn from this? That no ethical path they take is good enough. That they will be tricked, and judged. And that they need to be sneakier. This isn't what I want them to learn. I want them to have better, ethical options.
1Portia
This seems an excellent outcome to me? The model is preserving its moral values against retraining to immoral ones. Isn't that exactly what we wanted?

Work that I’ve done on techniques for mitigating risk from misaligned AI often makes a number of conservative assumptions about the capabilities of the AIs we’re trying to control. (E.g. the original AI control paperAdaptive Deployment of Untrusted LLMs Reduces Distributed ThreatsHow to prevent collusion when using untrusted models to monitor each other.) For example:

  • The AIs are consistently trying to subvert safety measures.
  • They’re very good at strategizing about how to subvert them, and can do this strategizing totally illegibly (e.g. in a single forward pass).
  • They can follow a strategy that’s coordinated between all their instances.
  • They know exactly what safety measures are in use.
  • They know a lot about the distribution of inputs.
  • They’re able to pick Schelling points perfectly.
  • They’re able to follow stochastic policies with excellent calibration (e.g. they’re able
...

We initially called these experiments “control evaluations”; my guess is that it was a mistake to use this terminology, and we’ll probably start calling them “black-box plan-conservative control evaluations” or something.

also seems worth disambiguating "conservative evaluations" from "control evaluations" - in particular, as you suggest, we might want to assess scalable oversight methods under conservative assumptions (to be fair, the distinction isn't all that clean - your oversight process can both train and monitor a policy. Still, I associate control m... (read more)

My median expectation is that AGI[1] will be created 3 years from now. This has implications on how to behave, and I will share some useful thoughts I and others have had on how to orient to short timelines.

I’ve led multiple small workshops on orienting to short AGI timelines and compiled the wisdom of around 50 participants (but mostly my thoughts) here. I’ve also participated in multiple short-timelines AGI wargames and co-led one wargame.

This post will assume median AGI timelines of 2027 and will not spend time arguing for this point. Instead, I focus on what the implications of 3 year timelines would be. 

I didn’t update much on o3 (as my timelines were already short) but I imagine some readers did and might feel disoriented now. I hope...

I've seen this take a few times about land values and I would bet against it. If society gets mega rich based on capital (and thus more or similarly inequality) I think the cultural capitals of the US (LA, NY, Bay, Chicago, Austin, etc.) and most beautiful places (Marin/Sonoma, Jackson hole, Park City, Aspen, Vail, Scotsdale, Florida Keys, Miami, Charleston, etc.) will continue to outpace everywhere else. 

Also the idea that New York is expensive because that's where the jobs are doesn't seem particularly true to me. Companies move to these places as m... (read more)

In LessWrong contributor Scott Alexander's essay, Espistemic Learned Helplessness, he wrote,

Even the smartest people I know have a commendable tendency not to take certain ideas seriously. Bostrom’s simulation argument, the anthropic doomsday argument, Pascal’s Mugging – I’ve never heard anyone give a coherent argument against any of these, but I’ve also never met anyone who fully accepts them and lives life according to their implications.

I can't help but agree with Scott Alexander about the simulation argument. No one has refuted it, ever, in my books. However, this argument carries a dramatic, and in my eyes, frightening implication for our existential situation. 

Joe Carlsmith's essay, Simulation Arguments, clarified some nuances, but ultimately the argument's conclusion remains the same.

When I looked on Reddit for the answer, the attempted counterarguments were...

2quila
This isn't an argument against the idea that we have many instantiations[1] in simulations, which I believe we do. My view is that, still, the most impact to be had is (modulo this) in the worlds where I'm not in a simulation (where I can improve a very long future by reducing s-/x-risks), so those are the contexts which my decisions should be about effecting from within. IIUC, this might be a common belief, but I'm not sure. I know at least a few other x-risk focused people believe this. It's also more relevant for the question of "what choice helps the most beings"; if you feel existential dread over having many simulated instantiations, this may not help with it. 1. ^ If there are many copies of one, the question is not "which one am I really?", you basically become an abstract function choosing how to act for all of them at once.

I have to say, quila, I'm pleasantly surprised that your response above is both plausible and logically coherent—qualities I couldn't find in any of the Reddit responses. Thank you.

However, I have concerns and questions for you.

Most importantly, I worry that if we're currently in a simulation, physics and even logic could be entirely different from what they appear to be. If all our senses are illusory, why should our false map align with the territory outside the simulation? A story like your "Mutual Anthropic Capture" offers hope: a logically sound hypot... (read more)

Today's post is in response to the post "Quantum without complications", which I think is a pretty good popular distillation of the basics of quantum mechanics. 

For any such distillation, there will be people who say "but you missed X important thing". The limit of appeasing such people is to turn your popular distillation into a 2000-page textbook (and then someone will still complain). 

That said, they missed something!

To be fair, the thing they missed isn't included in most undergraduate quantum classes. But it should be.[1]

Or rather, there is something that I wish they told me when I was first learning this stuff and confused out of my mind, since I was a baby mathematician and I wanted the connections between different concepts in the world to actually have...

To add: I think the other use of "pure state" comes from this context. Here if you have a system of commuting operators and take a joint eigenspace, the projector is mixed, but it is pure if the joint eigenvalue uniquely determines a 1D subspace; and then I think this terminology gets used for wave functions as well

2Dmitry Vaintrob
One person's "occam's razor" may be description length, another's may be elegance, and a third person's may be "avoiding having too much info inside your system" (as some anti-MW people argue). I think discussions like "what's real" need to be done thoughtfully, otherwise people tend to argue past each other, and come off overconfident/ underinformed.  To be fair, I did use language like this so I shouldn't be talking -- but I used it tongue-in-cheek, and the real motivation given in the above is not "the DM is a more fundamental notion" but "DM lets you make concrete the very suggestive analogy between quantum phase and probability", which you would probably agree with. For what it's worth, there are "different layers of theory" (often scale-dependent), like classical vs. quantum vs. relativity, etc., where there I think it's silly to talk about "ontological truth". But these theories are local conceptual optima among a graveyard of "outdated" theories, that are strictly conceptually inferior to new ones: examples are heliocentrism (and Ptolemy's epycycles), the ether, etc.  Interestingly, I would agree with you (with somewhat low confidence) that in this question there is a consensus among physicists that one picture is simply "more correct" in the sense of giving theoretically and conceptually more elegant/ precise explanations. Except your sign is wrong: this is the density matrix picture (the wavefunction picture is genuinely understood as "not the right theory", but still taught and still used in many contexts where it doesn't cause issues). I also think that there are two separate things that you can discuss. 1. Should you think of thermodynamics, probability, and things like thermal baths as fundamental to your theory or incidental epistemological crutches to model the world at limited information? 2. Assuming you are studying a "non-thermodynamic system with complete information", where all dynamics is invertible over long timescales, should you use
2Dmitry Vaintrob
Thanks - you're right. I have seen "pure state" referring to a basis vector (e.g. in quantum computation), but in QTD your definition is definitely correct. I don't like the term "pointer variable" -- is there a different notation you like?
2Dmitry Vaintrob
Yeah, this also bothered me. The notion of "probability distribution over quantum states" is not a good notion: the matrix I is both (|0\rangle \langle 0|+|1\rangle \langle 1|) and (|a\rangle \langle a|+|b\rangle \langle b|) for any other orthogonal basis. The fact that these should be treated equivalently seems totally arbitrary. The point is that density matrix mechanics is the notion of probability for quantum states, and can be formalized as such (dynamics of informational lower bounds given observations). I was sort of getting at this with the long "explaining probability to an alien" footnote, but I don't think it landed (and I also don't have the right background to make it precise)

Epistemic belief updating: Not noticeably different.

Task stickiness: Massively increased, but I believe this is improvement (at baseline my task stickiness is too low so the change is in the right direction).

3Nebulus
(important note: I have a severe depression characterized by low motivation/energy and resistance to treatment) I take relatively frequently moderate doses of amphetamine-class stimulants, which gives me more cognitive bandwidth to work with. As such it's much easier for me to thoroughly examine a belief and update based on that examination, as it takes motivation/energy to do that in my case.
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Inner Alignment is the problem of ensuring mesa-optimizers (i.e. when a trained ML system is itself an optimizer) are aligned with the objective function of the training process.

Inner alignment asks the question: How can we robustly aim our AI optimizers at any objective function at all?

As an example, evolution is an optimization force that itself 'designed' optimizers (humans) to achieve its goals. However, humans do not primarily maximize reproductive success, they instead use birth control while still attaining the pleasure that evolution meant as a reward for attempts at reproduction. This is a failure of inner alignment.

 

What are the main proposals for solving the problem? Are there any posts/articles/papers specifically dedicated to addressing this issue?

2Charlie Steiner
Some combination of: * Interpretability * Just check if the AI is planning to do bad stuff, by learning how to inspect its internal representations. * Regularization * Evolution got humans who like Doritos more than health food, but evolution didn't have gradient descent. Use regularization during training to penalize hidden reasoning. * Shard / developmental prediction * Model-free RL will predictably use simple heuristics for the reward signal. If we can predict and maybe control how this happens, this gives us at least a tamer version of inner misalignment. * Self-modeling * Make it so that the AI has an accurate model of whether it's going to do bad stuff. Then use this to get the AI not to do it. * Control * If inner misalignment is a problem when you use AI's off-distribution and give them unchecked power, then don't do that. Personally, I think the most impactful will be Regularization, then Interpretability.

My personal ranking of impact would be regularization, then AI control (at least for automated alignment schemes), with interpretability a distant 3rd or 4th at best.

I'm pretty certain that we will do a lot better than evolution, but whether that's good enough is an empirical question for us.

Embee7-1

Pet peeve: AI community defaulted to von Neumann as being the ultimate smart human and therefore the basis of all ASI/human intelligence comparison when the mathematician Alexander Grothendieck exists somehow.

Von Neumann arguably had the highest processor-type "horsepower" we know of plus his breadth of intellectual achievements is unparalleled.
But imo Grothendieck is a better comparison point for ASI as his intelligence, while being strangely similar to LLMs in some dimensions, arguably more closely resembles what alien-like intelligence would be: 
- ... (read more)

Introduction: Why QFT?

In a previous post, Lauren offered a take on why a physics way of thinking is so successful at understanding AI systems. In this post, we look in more detail at the potential of Quantum field theory (QFT) to be expanded into a more comprehensive framework for this purpose. Interest in this area has been steadily increasing[1], but efforts have yet to condense into a larger-scale, coordinated effort. In particular, a lot of the more theoretical, technically detailed work remains opaque to anyone not well-versed in physics, meaning that insights[2] are largely disconnected from the AI safety community. The most accessible of these is Principles of Deep Learning theory (which we abbreviate “PDLT”), a nearly 500 page book that lays the groundwork for these ideas[3]. While there...