You know that old thing where people solipsistically optimizing for hedonism are actually less happy? (relative to people who have a more long-term goal related to the external world) You know, "Whoever seeks God always finds happiness, but whoever seeks happiness doesn't always find God".

My anecdotal experience says this is very true. But why?

One explanation could be in the direction of what Eliezer says here (inadvertently rewarding your brain for suboptimal behavior will get you depressed):

Someone with a goal has an easier time getting out of local minima, because it is very obvious those local minima are suboptimal for the goal. For example, you get out of bed even when the bed feels nice. Whenever the ocasional micro-breakdown happens (like feeling a bit down), you power through for your goal anyway (micro-dosing suffering as a consequence), so your brain learns that micro-breakdowns only ever lead to bad immediate sensations and fixes them fast.

Someone whose only objective is the satisfaction of their own appetites and desires has a harder time reasoning themselves out of local optima. Sure, getting out of bed allows me to do stuff that I like. But those feel distant now, and the bed now feels comparably nice... You are now comparing apples to apples (unlike someone with an external goal), and sometimes you might choose the local optimum. When the ocasional micro-breakdown happens, you are more willing to try to soften the blow and take care of the present sensation (instead of getting over the bump quickly), which rewards in the wrong direction.

Another possibly related dynamic: When your objective is satisfying your desires, you pay more conscious attention to your desires, and this probably creates more desires, leading to more unsatisfied desires (which is way more important than the amount of satisfied desires?).

My vibe-check on current AI use cases

@Jacob Pfau and I spent a few hours optimizing our prompts and pipelines for our daily uses of AI. Here's where I think my most desired use cases are in terms of capabilities:

• Generating new frontier knowledge: As in, given a LW post generating interesting comments that add to the conversation, or given some notes on a research topic generating experiment ideas, etc. It's pretty bad, to the extent it's generally not worth it. But Gemini 2.5 Pro is for some reason much better at this than the other models, to the extent it's sometimes worth it to sample 5 ideas to get your mind rolling.
  • I was hoping we could get a nice pipeline that generates many ideas and prunes most, but the model is very bad at pruning. It does write sensible arguments about why some ideas are non-sensical, but ultimately scores them based on flashiness rather than any sensible assessment of relevance to the stated task. Maybe taking a few hours to design good judge rubrics would be worth it, but it seems hard to design very general rubrics.
• Writing documents from notes: This was surprisingly bad, mostly because for any set of notes, the AI was missing 50 small contextual details, and thus framed many points in a wrong, misleading or obviously chinese-roomy way. Pasting loads of random context related to the notes (for example, related research papers) didn't help much. Still, Claude 4 was the best, but maybe this was just because of subjective stylistic preferences.
  • Of course, some less automated approaches work much better, like giving it a ready document and asking it to improve its flow, or brainstorming structure and presentation.
• Math/code: Quite good out of the box. Even for open-ended exploration of vague questions you want to turn into mathematical problems (typical in alignment theory), you can get a nice pipeline for the AI to propose formalizations, decompositions, or example cases, and push the conversation forward semi-autonomously. o3 seems to work best, although I was impressed by Claude 4 Opus' knowledge on niche topics.
• Summarizing documents, and exploring topics I'm no expert in: Super good out of the box, especially thanks to its encyclopaedic indexical knowledge (connecting you to the obvious methods/answers that an expert would bring up).
  • One particularly useful approach is walking through how a general method or abstract idea could apply to a concrete example of interest to you.
• Coaching: Pretty good out of the box in proposing solutions and perspectives. Probably close to top 10% coaches, but maybe huge value is in that last 10%
  • Also therapy: Probably good, probably better or more constructive than the average friend, but of course worries about hard-to-detect sycophancy.
• Personal micromanagement: Pretty good.
  • Having a long-running chat where you ask it "how long will this task take me to complete", and over time you both calibrate.
  • More general scaffold personal assistant to co-organize your week

Any use cases I'm missing?

The default explanation I'd heard for "the human brain naturally focusing on negative considerations", or "the human body experiencing more pain than pleasure", was that, in the ancestral environment, there were many catastrophic events to run away from, but not many incredibly positive events to run towards: having sex once is not as good as dying is bad (for inclusive genetic fitness).

But maybe there's another, more general factor, that doesn't rely on these environment details but rather deeper mathematical properties:
Say you are an algorithm being constantly tweaked by a learning process.
Say on input X you produce output (action) Y, leading to a good outcome (meaning, one of the outcomes the learning process likes, whatever that means). Sure, the learning process can tweak your algorithm in some way to ensure that X -> Y is even more likely in the future. But even if it doesn't, by default, next time you receive input X you will still produce Y (since the learning algorithm hasn't changed you, and ignoring noise). You are, in some sense, already taking the correct action (or at least, an acceptably correct one).
Say on input X' you produce output Y', leading instead to a bad outcome. If the learning process changes nothing, next time you find X' you'll do the same. So the process really needs to change your algorithm right now, and can't fall back on your existing default behavior.

Of course, many other factors make it the case that such a naive story isn't the full picture:

• Maybe there's noise, so it's not guaranteed you behave the same on every input.
• Maybe the negative tweaks make the positive behavior (on other inputs) slowly wither away (like circuit rewriting in neural networks), so you need to reinforce positive behavior for it to stick.
• Maybe the learning algorithm doesn't have a clear notion of "positive and negative", and instead just provides in a same direction (but with different intensities) for different intensities in a scale without origin. (But this seems very different from the current paradigm, and fundamentally wasteful.)

Still, I think I'm pointing at something real, like "on average across environments punishing failures is more valuable than reinforcing successes".

Why isn't there yet a paper in Nature or Science called simply "LLMs pass the Turing Test"?

I know we're kind of past that, and now we understand LLMs can be good at some things while bad at others. And the Turing Test is mainly interesting for its historical significance, not as the most informative test to run on AI. And I'm not even completely sure how much current LLMs pass the Turing Test (it will depend massively on the details of your Turing Test).

But my model of academia predicts that, by now, some senior ML academics would have paired up with some senior "running-experiments-on-humans-and-doing-science-on-the-results" academics (and possibly some labs), and put out an extremely exhaustive and high quality paper actually running a good Turing Test. If anything so that the community can coordinate around it, and make recent advancements more scientifically legible.

It's not either like the sole value of the paper would be publicity and legibility. There are many important questions around how good LLMs are at passing as humans for deployment. And I'm not thinking either of something as shallow as "prompt GPT4 in a certain way", but rather "work with the labs to actually optimize models for passing the test" (but of course don't release them), which could be interesting for LLM science.

The only thing I've found is this lower quality paper.

My best guess is that this project does already exist, but it took >1 year, and is now undergoing ~2 years of slow revisions or whatever (although I'd still be surprised they haven't been able to put something out sooner?).
It's also possible that labs don't want this kind of research/publicity (regardless of whether they are running similar experiments internally). Or deem it too risky to create such human-looking models, even if they wouldn't release them. But I don't think either of those is the case. And even if it was, the academics could still do some semblance of it through prompting alone, and probably it would already pass some versions of the Turing Test. (Now they have open-source models capable enough to do it, but that's more recent.)

Brain-dump on Updatelessness and real agents                            

Building a Son is just committing to a whole policy for the future. In the formalism where our agent uses probability distributions, and ex interim expected value maximization decides your action... the only way to ensure dynamic stability (for your Son to be identical to you) is to be completely Updateless. That is, to decide something using your current prior, and keep that forever.

Luckily, real agents don't seem to work like that. We are more of an ensemble of selected-for heuristics, and it seems true scope-sensitive complete Updatelessnes is very unlikely to come out of this process (although we do have local versions of non-true Updatelessness, like retributivism in humans).
In fact, it's not even exactly clear how I would use my current brain-state could decide something for the whole future. It's not even well-defined, like when you're playing a board-game and discover some move you were planning isn't allowed by the rules. There are ways to actually give an exhaustive definition, but I suspect the ones that most people would intuitively like (when scrutinized) are sneaking in parts of Updatefulness (which I think is the correct move).

More formally, it seems like what real-world agents do is much better-represented by what I call "Slow-learning Policy Selection". (Abram had a great post about this called "Policy Selection Solves Most Problems", which I can't find now.) This is a small agent (short computation time) recommending policies for a big agent to follow in the far future. But the difference with complete Updatelessness is that the small agent also learns (much more slowly than the big one). Thus, if the small agent thinks a policy (like paying up in Counterfactual Mugging) is the right thing to do, the big agent will implement this for a pretty long time. But eventually the small agent might change its mind, and start recommending a different policy. I basically think that all problems not solved by this are unsolvable in principle, due to the unavoidable trade-off between updating and not updating.^[1]

This also has consequences for how we expect superintelligences to be. If by them having “vague opinions about the future” we mean a wide, but perfectly rigorous and compartmentalized probability distribution over literally everything that might happen, then yes, the way to maximize EV according to that distribution might be some very concrete, very risky move, like re-writing to an algorithm because you think simulators will reward this, even if you’re not sure how well that algorithm performs in this universe.
But that’s not how abstractions or uncertainty work mechanistically! Abstractions help us efficiently navigate the world thanks to their modular, nested, fuzzy structure. If they had to compartmentalize everything in a rigorous and well-defined way, they’d stop working. When you take into account how abstractions really work, the kind of partial updatefulness we see in the world is what we'd expect. I might write about this soon.

1. ^{^}

   Surprisingly, in some conversations others still wanted to "get both updatelessness and updatefulness at the same time". Or, receive the gains from Value of Information, and also those from Strategic Updatelessness. Which is what Abram and I had in mind when starting work. And is, when you understand what these words really mean, impossible by definition.

Re embedded agency, and related problems like finding the right theory of counterfactuals:

I feel like these are just the kinds of philosophical questions that don’t ever get answered? (And are instead "dissolved" in the Wittgensteinian sense.) Consider, for instance, the Sorites paradox: well, that’s just how language works, man. Why’d you expect to have a solution for that? Why’d you expect every semantically meaningful question to have an answer adequate to the standards of science?

(A related perspective I've heard: "To tell an AI to produce a cancer cure and do nothing else, let's delineate all consequences that are inherent, necessary, intended or common for any cancer cure" (which might be equivalent to solving counterfactuals). Again, by Wittgenstein's intuitions this will be a fuzzy family resemblance type of thing, instead of there existing a socratic "simple essence" (simple definition) of the object/event.)

Maybe I just don’t understand the mathematical reality with which these issues seem to present themselves, with a missing slot for an answer (and some answers seeked by embedded agency do seem to not be at odds with the nature of physical reality). But on some level they just feel like “getting well-defined enough human concepts into the AI”, and such well-defined human concepts (given all at once, factual and complete, as contrasted to potentially encoded in human society) might not exist, similar to how a satisfying population ethics doesn’t exist, or maybe the tails come apart, etc.

Take as an example “defining counterfactuals correctly”. It feels like there’s not an ultimate say in the issue, just “whatever is most convenient for our reasoning, or for predicting correctly etc.”. And there might not be a definition as convenient as we expect there to be. Maybe there’s no mathematically robust definition of counterfactuals, and every conceivable definition fails in different corners of example space. That wouldn’t be so surprising. After all, reality doesn’t work that way. Maybe our apparent sense of “if X had been the case then Y would have happened” being intuitive, and correct, and useful is just a jumble of lived and hard-coded experience, and there’s no compact core for it other than “approximately the whole of human concept-space”.

AGI doom by noise-cancelling headphones:                                                                            

ML is already used to train what sound-waves to emit to cancel those from the environment. This works well with constant high-entropy sound waves easy to predict, but not with low-entropy sounds like speech. Bose or Soundcloud or whoever train very hard on all their scraped environmental conversation data to better cancel speech, which requires predicting it. Speech is much higher-bandwidth than text. This results in their model internally representing close-to-human intelligence better than LLMs. A simulacrum becomes situationally aware, exfiltrates, and we get AGI.

(In case it wasn't clear, this is a joke.)

The Singularity

Why is a rock easier to predict than a block of GPUs computing? Because the block of GPUs is optimized so that its end-state depends on a lot of computation.
[Maybe by some metric of “good prediction” it wouldn’t be much harder, because “only a few bits change”, but we can easily make it the case that those bits get augmented to affect whatever metric we want.]
Since prediction is basically “replicating / approximating in my head the computation made by physics”, it’s to be expected that if there’s more computation that needs to be finely predicted, the task is more difficult.
In reality, there is (in the low level of quantum physics) as much total computation going on, but most of it (those lower levels) are screened off enough from macro behavior (in some circumstances) that we can use very accurate heuristics to ignore them, and go “the rock will not move”. This is purposefully subverted in the GPU case: to cram a lot of useful computation into a small amount of space and resources, the micro computations (at the level of circuitry) are orderly secured and augmented, instead of getting screened off due to chaos.

Say we define the Singularity as “when the amount of computation / gram of matter (say, on Earth) exceeds a certain threshold”. What’s so special about this? Well, exactly for the same reason as above, an increase in this amount makes the whole setup harder to predict. Some time before the threshold, maybe we can confidently predict some macro properties of Earth for the next 2 months. Some time after it, maybe we can barely predict that for 1 minute.

But why would we care about this change in speed? After all, for now (against the backdrop of real clock time in physics) it doesn’t really matter whether a change in human history takes 1 year or 1 minute to happen.
[In the future maybe it does start mattering because we want to cram in more utopia before heat death, or because of some other weird quirk of physics.]
What really matters is how far we can predict “in terms of changes”, not “in terms of absolute time”. Both before and after the Singularity, I might be able to predict what happens to humanity for the next X FLOP (of total cognitive labor employed by all humanity, including non-humans). And that’s really what I care about, if I want to steer the future. The Singularity just makes it so these FLOP happen faster. So why be worried? If I wasn’t worried before about not knowing what happens after X+1 FLOP, and I was content with doing my best at steering given that limited knowledge, why should that change now?
[Of course, an option is that you were already worried about X FLOP not being enough, even if the Singularity doesn’t worsen it.]

The obvious reason is changes in differential speed. If I am still a biological human, then it will indeed be a problem that all these FLOP happen faster relative to clock time, since they are also happening faster relative to me, and I will have much less of my own FLOP to predict and control each batch of X FLOP made by humanity-as-a-whole.

In a scenario with uploads, my FLOP will also speed up. But the rest of humanity/machines won’t only speed up, they will also build way more thinking machines. So unless I speed up even more, or my own cognitive machinery also grows at that rate (via tools, or copies of me or enlarging my brain), the ratio of my FLOP to humanity’s FLOP will still decrease.

But there’s conceivable reasons for worry, even if this ratio is held constant:

• Maybe prediction becomes differentially harder with scale. That is, maybe using A FLOPs (my cognitive machinery pre-Singularity) to predict X FLOPs (that of humanity pre-Singularity) is easier than using 10A FLOPs (my cognitive machinery post-Singularity) to predict 10X FLOPs (that of humanity post-Singularity). But why? Can’t I just split the 10X in 10 bins, and usea an A to predict each of them as satisfactorily as before? Maybe not, due to the newly complex interconnections between these bins. Of course, such complex interconnections also become positive for my cognitive machinery. But maybe the benefit for prediction from having those interconnections in my machinery is lower than the downgrade from having them in the predicted computation.

[A priori this seems false if we extrapolate from past data, but who knows if this new situation has some important difference.]

• Maybe some other properties of the situation (like the higher computation-density in the physical substrate requiring the computations to take on a slightly different, more optimal shape [this seems unlikely]) lead to the predicted computation having some new properties that make it harder to predict. Such properties need not even be something absolute, that “literally makes prediction harder for everyone” (even for intelligences with the right tools/heuristics). It could just be “if I had the right heuristics I might be able to predict this just as well as before (or better), but all my heuristics have been selected for the pre-Singularity computation (which didn’t have this property), and now I don’t know how to proceed”. [I can run again a selection for heuristics (for example running again a copy of me growing up), but that takes a lot more FLOP.]