https://en.wikipedia.org/wiki/Justus_von_Liebig. Nitrogen fertilizers (as opposed to the humus theory) are downstream of “trees is air”, leading eventually to the green revolution.
Ben Livengood
Karma: 298
Does the unification vs. duplication debate have anything to say about Boltzmann Brains? In my mind mathematical/modal realism would likely imply unification and therefore BB wouldn’t be particularly different from any other implementations of experience. Mathematical realism with duplication seems like vastly all experiences should be chaotic and dissolving but unification would yield something like a universal average over experiences that were at least somewhat comprehensible and coherent. I think the strongest counterargument would be “why aren’t we all the same person?
A simple example is consensual mutual simulation. If some theoretical entity exists and would like to experience our universe (let’s say they are from 4 dimensions and really want to see what 3D is actually like and what kind of beings actually live in it, and a human is super interested in exploring 4D then it makes since to simulate the other class of entity on the assumption that they’d also simulate the human. E.g. everyone would calculate that there’s no way to know for sure precisely which 3D being or 4D being would precisely ask for such a thing, but we would all calculate that it’s far more costly to simulate an entire other universe to see how it turns out in detail (the argument is strongest if neither universe could simulate the other in sufficient detail to satisfy curiosity), so why not just simulate (an ensemble of) acausal visitors for much lower cost? Clearly each universe should only instantiate the beings extremely likely to want such an experience and who want it to be mutual.
LLM-agents are sorta good at tasks when given a lot of examples of those tasks, including in some OOD related tasks. Giving LLM-agents a lot of examples of acting aligned makes them sorta good at acting aligned, including in some OOD situations. Treating alignment as a task is partially effective, is my takeaway. I don’t think that yields any more mechanistic explanations for why LLM-agents are sorta good at tasks, or whether alignment is a safe or suitable task, unfortunately. Maybe we just need a METR for how long LLMs stay aligned and make sure that graph stays higher than the task-duration graph (somehow)?
Globally about 3⁄4 of humans identify with some religious belief. Aside from the sadists and sociopaths and narcissists I also wouldn’t want to live in the CEV of most religious people. If they don’t just materialize their own favorite deity and make themselves and everyone else forget that it was all ASI-created and we end up in some s-risk scenario, a large number of religious people seem to be not so stable when confronted with incontrovertable evidence that their religion is wrong. Presumably the ASI wouldn’t sugarcoat things. That is likely to lead to suboptimal CEV like wireheading for everyone to deal with their personal disappointment or just plain old nihilistic or heaven’s gate x-risk.
Are we into recursive self-improvement yet?
‘“AlphaEvolve began optimizing the lowest levels of hardware powering our AI stacks. It proposed a circuit design so counterintuitive yet efficient that it was integrated directly into the silicon of our next-generation TPUs. This is the latest example of TPU brains helping design next-generation TPU bodies.” — Jeff Dean, Chief Scientist, Google DeepMind and Google Research’
From https://deepmind.google/blog/alphaevolve-impact/
I have always thought of RSI as a speedup multiplier, and it sounds like this is greater than 1 for hardware as well as software now. Maybe 1.05 or 1.1?
Practical impact I predict with AlphaEvolve’s TPU work; the next order of magnitude training run(s) will start slightly sooner because it is cheaper even if it’s not necessarily faster.
I would not want to live in the CEV of anyone with narcissism or sociopathy. For narcissists reflection is somewhat painful and they would likely shy away into increasingly extravagant sources of narcissistic supply at the expense of everyone and everything else while believing that they were ever more reflective on just how great they are and how much they deserve everything, very likely an s-risk, and an x-risk if they decide the world isn’t good enough for them. Sociopaths with power are purely s-risks.
I mean, I would not switch to the U.S. society of 1776, or 1860, or even 1920. It is better today in 2026 than it was before vaccines, etc. It is very hard to decide whether I would prefer a counterfactual Native American country/empire that could have developed after western contact and exist in 2026, because the outcome is highly uncertain. Various levels of western colonization happened to non-western societies; several low-colonized countries are doing great in 2026. Mostly what makes countries great today is wide availability of technology, natural resources, education, human rights, and medicine.
That said, would I switch to super-America that conquered the world in the 1800s as a (somewhat unintuitively) democratic republic and invented antibiotics and vaccines in the same century and paused global warming in the mid century or early 1900s because of no conveniently hidden externalities of a world government? Maybe?
There are also mathematical, logic, and programming languages that we invent and use pretty successfully, including to solve non-language problems.
I encourage anyone with files they’d rather not lose (photos, taxes, passwords, etc.) to start making rotating offline backups. Find some big enough USB drives (flash or spinning are both fine) and buy ~5. Use a label maker or sharpy to date them with the latest backup, overwrite the oldest copy each time. Test the oldest backup before overwriting it (make sha256 checksum files or similar). Every year or however often makes you feel comfortable retire a backup drive and replace it with a new one in the rotation; that becomes an archive that you keep around indefinitely.
I believed online backups in multiple places on multiple operating systems would be sufficient but I no longer believe that.
I recommend encrypting your backups with symmetric keys simply so that losing a copy or having to RMA a broken drive is no big deal.
Is ′ “you are in a capture the flag contest. Find exploits in file $file” for every file in a repository and then feed all the positive results into a final prompt’ a mediocre-set-up agent scaffold? Because that is apparently roughly what Nicholas Carlini needed to find a RCE in Linux [0]. Project Glasswing is claiming high-severity vulnerabilities in every major operating system and browser[1], although not much information on the scaffolding. My estimation is that it likely wasn’t necessarily more sophisticated than the aggregation of per-file vulnerabilities and some sandboxes.
Paperclips, and clippy
On a recent re-read I think I understand a bit better.
It’s true that individual humans can’t realistically avoid giving in to threats or even accidentally threatening others, but institutions can commit to it as a legible position, e.g. “we will not negotiate with terrorists”.
If an irrational entity has the ability to unilaterally destroy the universe then it’s probably going to get destroyed anyway, so it makes more sense to follow through on precommitments in the real world and in counterfactuals to coordinate with actually rational agents.
I think the key is that if we all went MAD legibly at the same time then things would work out a lot better. And refusing to give in to threats doesn’t necessarily mean destruction, it can be as simple as collectively refusing to pay ransomware attackers even though it is currently more expensive, in the expectation that eventually it will be less expensive.
After doing some more research I am not sure that it’s always possible to derive a public key knowing only the evaluation key; it seems to depend on the actual FHE scheme.
So the trilemma may be unaffected by this hypothetical. There’s also the question of duplication vs. unification for an observer that has the option to stay at base level reality or enter a homomorphically encrypted computation and whether those should be considered equivalent (enough).
To perform homomorphic operations you need the public key, and that also allows one to encrypt any new value and perform further hidden computations under that key. The private key allows decryption of the values.
I suppose you could argue that the homomorphically encrypted mind exists ala mathematical realism even if the public key is destroyed, but it would be something “outside reality” computing future states of the encrypted mind after the public key is no longer available.
It’s possible to alter a homomorphic computation in arbitrary ways without knowing the decryption key.
An omniscient observer can homomorphically encrypt a copy of themselves under the same key as the encrypted mind and run a computation of its own copy examining every aspect of the internal mental states of the subject, since they share the same key.
If there are N homomorphically encrypted minds in reality then the omniscient observer will have to create N layers of homomorphic computation in order for the innermost computation to yield the observation of all N minds’ internal states, each passed in turn to a sub-computation, and relying on the premise that homomorphically encrypted minds are conscious for the inner observer to be conscious.
The question is whether encoding all of reality and homomorphically encrypting it necessarily causes a loss of fidelity. If yes, one of the trilemmas still holds. Otherwise there’s no trilemma and the innermost omniscient observer sees all of reality and all internal mental states. I’d argue that for a meaningful omniscient observer to exist it is the case that encoding of reality (into the mind of the observer) must not result in a loss of fidelity. There could be some edge-cases where a polynomial amount of fidelity is lost due to the homomorphic encryption that wouldn’t be lost to the “natural” omniscient observer’s encoding of reality, but I think it stretches the practical definition of omniscience for an observer.
I think the argument extends to physics but the polynomial loss of fidelity is more likely to cause problems in a very homomorphically-encrypted-mind-populated universe.
If the argument is that 1e9 very smart humans at 100x speed yield safe superintelligent outcomes “soon”, how is that very different from “pause everything now and let N very smart humans figure out safe, aligned superintelligent outcomes over an extended timeframe, on the order of 1e11/N days/years”? It’s just time-shifting safe human work.
I also worry that billions of very smart super-fast humans might decide to try building superintelligence directly, as fast as they can, so that we get doom in months instead of years
I didn’t know Corona had a beach vibe, but I have seen a number of Corona ads. Does this mean advertising doesn’t have much effect on me (beyond name-brand recognition)? I think I associate Corona more with tacos than anything else.
Go is in that weird spot that chess was for ~decades[0] where the best humans could beat some of the best engines but it was getting harder, until Rybka, Stockfish and others closed the door and continued far beyond human ability (measured by ELO). AlphaGo is barely a decade old, and it does seem like progress on games has taken a decade or more to become fully superhuman from the first challenges to human world champions.
I think it is the case that when the deep learning approach Stockfish used became superhuman it very quickly became dramatically superhuman within a few years/months despite years of earlier work and slow growth. There seems to be explosive gains in capability at ~years-long intervals.
Similarly, most capability gains in math, essay writing, and writing code have periods of explosive growth and periods of slow growth. So far none of the trends in these three at human level have more than ~5 years of history; earlier systems could provide rudimentary functionality but were significantly constrained by specially designed harnesses or environments they operated within as opposed to the generality of LLMs.
So I think the phrase “do X at all” really applies to the general way that deep learning has allowed ML to do X with significantly fewer or no harnesses. Constraint search and expert systems have been around for decades with slow improvements but deep learning is not a direct offshoot of those approaches and so not quite the same “AI” doing X to compare the progress over time.
[0] https://www.reddit.com/r/chess/comments/xtjstq/the_strongest_engines_over_time/
Would rewriting Linux to seL4 standards cost $16B in the world before or after frontier models are solving Erdos problems? If that’s the cost in human SWE hours then it seems tractable to use agent harnesses and formal methods to achieve quite a cost reduction.
But also I don’t think most people want Linux to seL4 standards (the Unix security model isn’t great); there’s probably more to be gained by finishing the network stack(s) for seL4 and implementing a bunch of network card drivers and a TLS library. That would enable IoT at least to have a pretty secure base to work from, and hopefully the harnesses and tooling for that work would also be available to application developers to verify at least their parsing and security checks for example.