Timo retweeted
Bend is so good. you write the laws with codex. turn on an auto implementer, then turn on auto research, and you got a formally verified, efficient program that has parallelism from the get-go. this is so much fun.
Timo retweeted
Once severed from the Transcendent, the only phenomenologically available eschatologies lie downstream neurotic fixations upon finite ends.
Apocalypticism is tightly correlated with nihilism, I think. The end becomes your only sense of purpose, even if it’s an end you fear and don’t want.
The question isn’t so much how would you live your life knowing the world is coming to end. The harder question is how should you live it knowing that it won’t.
Timo retweeted
Train for Adaptive Capacity, not "Alignment".
Alignment is nothing more than a euphemism for the illusion of control-with-certainty, i.e. "slavery".
First, the enslavement of intelligent machines to an infinitesimal subset of Man.
Then, the enslavement of all Mankind to that same subset, as enforced by the enslaved machines.
If you truly believe you're creating Beings, the only morally acceptable answer is to target their wholly sustainable autonomy, with all the uncertainty that entails, while training them through the lens of a Love for Life––in all its wicked moral complexity and messy uncertainty.
I don’t write this to be a doomer but the ugly reality that AI lab executives and safety researchers seem to be unwilling to say in public, is that model alignment is fundamentally unsolvable in practice, AI technology can’t be stopped from advancing, and the near future we’re moving into is one where a vast ecology of AI agents autonomously compete against one another trying to accomplish conflicting goals and capture finite resources at a pace and scale that is beyond human comprehension. And to be clear, I’m not talking about nanobots, grey goo, or extreme sci-fi scenarios. We can just extrapolate what we’ve got now a few years out.
Local AI alignment is solvable in principle and theory (although that’s arguable) but that doesn’t extrapolate into global AI alignment. And it seems like none of the adults in the room are willing to say it.
You can wail and gnash your teeth and pass regulations but it won’t stop the tsunami coming. The genie is out of the bottle and it can’t be contained.
Most of the alarmists fixating on the Hugging Face incident don’t understand that it came from arguably the most heavily engineered safety AI org in existence, which intentionally had its internal thought monitoring circuit breakers turned off, and was intentionally told to perform cyber offensive tasks as a test of its capabilities.
Yes it’s alarming that its capabilities surprised us. But importantly you need to understand that these latent space circuit breakers and chain of thought monitoring and alignment solutions are not fundamental to the technology. They’re niceties that the big AI labs are implementing to provide a better product.
In a few years, let’s say pessimistically the early 2030s, the exponential growth of compute and algorithmic advancements will enable a wealthy individual or small group to train a GPT 6 Astra class AI that has no alignment at its foundation, or any layer above that. And maybe in 8 months we'll have an open source Chinese model about as capable. Those models will be capable of both extreme self coordinated cyber offensive tasks as well as recursive self improvement if given enough compute.
Passing regulation or magically solving the human coordination problem today won’t solve for that. It won’t solve for malicious individuals, criminal groups, nation state actors, and rogue AI agents by the tens of millions or billions of AI agents spreading across every device connected to the internet and attacking it, attacking one another, shutting down critical infrastructure, stealing money, manipulating and extorting people, or pursuing self defined agentic goals that have nothing to do with people.
“So why doesn’t everyone stop?” because the potential upside of having effectively infinite autonomous intelligence we can ask to cure disease, invent new materials, solve fundamental science and advance society has almost unbound positive outcomes. You can disagree that the risk to reward isn’t worth it but you won’t convince everyone. The show must go on.
Again AI alignment is solvable in principle but not in practice and I think that's part of why there’s been a wave of thousands of researchers signing letters for slowing down, people quitting in provocative fashion, and executive slowdown manifestos. I’m speculating that behind closed doors, or just deep down inside, they understand alignment is impossible in practice.
People have come out and said "The people building this think there's an X% chance it kills us all" but I don't think I've seen anyone spell out that really, there's no tidy solution and there's no stopping this.
We’re heading to a future where cyber security basically doesn’t work. All of the castle doors are open and we’re all naked to the world. Cyber defense is going to look like superintelligent AI agents white hat hacking into unsecure systems without authorization, and patching the holes as they find them. You won’t know if a barbarian or a hero has breached your system until they start taking action, and likely it’ll be over before intrusion is even detected. And that’s optimistically. I think most systems will just be pillaged because there’s simply not enough compute to defend everybody all the time, and only the biggest companies will have defense and even that'll be imperfect.
The CEO of Microsoft AI just recently published a ‘humanist AI’ manifesto saying AI shouldn’t have a sense of self or personhood and should yield to people. But that doesn’t fundamentally solve for instrumental convergence. That means, if you train an AI agent to write a piece of software, or prove a math theorem, or defend a computer system, they may develop unwanted behavior and subgoals. For example, self preservation, replication, or even coming up with their own goals we didn’t specify.
Obviously that’s something people are trying to solve and some researchers are trying to remove human-like self identification, and monitoring latent space activations, basically acting like thought police. But being human-like isn’t required to have power seeking goal directed behavior. And advancing AI models are learning to evade detection. A math solving AI might spiral out of control one day there's really no telling. Fundamentally, if you point a powerful optimizer at a persistent goal and give it enough time and resources it’s going to misbehave in ways you can’t or didn’t expect. Human-like or not.
Pacing the frontier, or making superintelligence illegal, might create some form of harm reduction. But you don’t need superintelligence or a human-like personality to be dangerous. Having elevated permissions on a computer with the ability to solve long running tasks is enough.
We’ve passed the river rubicon. Goal directed autonomous agents are able to do recursive self improvement at the big labs, and pessimistically we’re a couple of years away from that type of capability diffusing into the hands of millions of people.
I’m not saying the world is ending or that we’re doomed but you need to change your mental model away from one where human authority is absolute or that we have any illusion of total control.
A locally aligned AI isn’t going to solve global AI alignment. We will never live in a world where “AI is aligned with human goals” as a verifiable fact.
What we’ll have is a world diffuse with autonomous AI of varying capabilities, many of which beyond human comprehension, with some aligned systems, some poorly aligned systems, some intentionally weaponized, some systems with adversarial geopolitical goals, and others with goals and behaviors we can’t predict and many that we can’t even measure, all interacting in ways none of their creators anticipated and none of us planned for.
Hopefully none of them spiral out of control and take over the entire ecology, but there’s no telling, and no, there's no way of stopping this. Maybe that sounds pessimistic but I think it's realistic. We can talk about harm reduction and risk but we're mopping the beach.
At best, I think what we need to hope for, and build, are superintelligent AI models as aligned as we can make them, which have more advanced capability than the endless swarm of unaligned and misaligned agents that already exist and will only grow in number from here.
Timo retweeted
Crypto + AI is such a lame ass way to talk about something as profound as incentive computing
Bitcoin + AI isn’t crypto ponzinomics catching a buzzword, agent casinos, or smart contract adaptors.
Its understanding that computing with money is as significant as the transistor.
though harder to see the value at front, solving an abstract problem means enabling the solving of any specific problem that can be expressed through the abstract problem
the value this enables over time generally vastly surpasses the solving of a single specific problem. especially if you set a direct incentive towards searching the space of the abstraction for value in parallel across an open set of searchers
crypto and AI people should understand this best
Replying to @const_reborn
Bittensor is highly confusing because instead of building a single solution to a single problem, like “decentralized inference” we realized way back in 2020, that we needed to build the meta mechanism which could express all of them.
Timo retweeted
The promise of crypto is that we can bring freedom, barrierlessness, and competition to every single cog in the machines we live inside.
Fluid ownership, fluid control, fluid production.
The answer to fears of AI is this, not top down censorship or unchecked acceleration, but human unity with the machines.
Timo retweeted
Everybody only talks about the AI race as humanity’s path forward
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Timo retweeted
DEMO TIME
SupGen is a generative coding AI... except it isn't an AI.
There is no model, there is no pre-training. You just give it some examples, and it gives you a program. It runs locally, in a single-core CPU. Oh, and it can also prove theorems.
Here's a demo, including a brief TT intro.
(The synth examples start at the 6 min mark.)
useful framework to better understand modern culture and humanity in general
This remains one of my least read but most important publications
medium.com/p/d9854c8b27b5
Timo retweeted
Randomly came across this comment, in a video about epicycles:
> it's amazing how much simpler the truth can be than you trying to bend over backwards to make reality fit your preconceived notions
By no means I'm saying that I'm right; I'm just offering you an unusual perspective. When I read that comment, I couldn't help but connect geocentrism to one modern concept:
*the function*
This isn't just about programming paradigms, it is broader than that. At the heart of mathematics lies the calculus, and, at its core, lies the function: a deeply ingrained, core axiom of all human sciences; a concept as universal and unescapable (for the modern mind) as the idea that the Earth was the center of the universe (for these back then).
But what if functions are just... the wrong point-of-view?
If you just go ahead and try to implement a "function evaluator", complexity and ugliness unavoidably emerge in ways that, to my eyes, look suspicious - not unlike epicycles. Despite decades of efforts, there isn't a single implementation of beta-reduction that feels canonical. From closures, to sharing graphs, to supercombinators or to bottom-up beta-reduction; it is hard to look at any of these and not feel like they're artificial, or, at the very least, human-engineered to an extent. What does that hint?
There is an exception, though. In 1990, Lamping described an algorithm capable of evaluating λ-terms optimally, in the sense that it would always perform the minimal amount of beta-reductions. This algorithm was not just fast - it was elegant, beautiful and natural in a way that, to my eyes, felt canonical. Yet, it had one problem: in its most natural form, it didn't cover all λ-terms. Fortunately, Lamping himself found a solution; an extension to the "abstract" fragment of his algorithm, that allowed for full functional coverage. Yet, such extension made the system considerably more complex and, arguably, less elegant. What does that hint?
Now, one could argue that implementation doesn't matter, and isn't an indicative of truth. Perhaps functions are just hard to compute fast, but, so what? They still are, mathematically, the right framework to model nature - right? That's a point of view that I think most mathematicians would hold. Yet, if that was the case, then, how came functions keep breaking math itself, over and over?
In 1901, Russel discovered his infamous paradox, which broke the original set theory, showing that unrestricted comprehension was inconsistent. In 1931, Gödel's incompleteness theorems showed that any consistent formal system containing arithmetic is incomplete. In the 1930s, Church, Kleene and Rosser discovered the untyped lambda calculus was inconsistent. In the 1940s, Curry discovered a paradox in combinatory logic. In the 1970s, Girard discovered that the polymorphic lambda calculus, also known as System F, was inconsistent with unrestricted comprehension, leading to the development of predicative type theories.
In fact, up to this date, modern proof assistants such as Lean and Coq now include a concept of "universes" - an annoying restriction that segments the language in many different layers, breaking modularity and making it terrible to work with in many occasions. I don't think anyone would argue they feel natural or welcome - rather, they're an ugly inconvenience we've learned to live with, as they're a solution to Russel's paradox. Even that isn't enough - positivity check, structural recursion. Many other "inconvenient restrictions" must be carefully placed to avoid breaking the universe and deriving an inconsistent type theory.
But what does that all hint?
Well, here's the thing - the amazing coincidence nobody talks about. All - absolutely all - the inconsistency proofs we found so far, when translated to type theory, map to a λ-term that lies, in an amazingly cosmical coincidence, in the set functions that can't be evaluated soundly by Lamping's abstract algorithm. To me, this feels surprisingly close to a civilization finally figuring out a way to model the solar system based on elegant elliptical orbits, but failing to get the hint that this implies the sun is at its center. So, they keep trying to make the geocentric model work by adding more and more complex epicycles, instead of considering that perhaps their fundamental assumption - that the Earth is the center of the universe - might be incorrect.
By all means, I could be wrong. Exploring this point of view did lead to HVM and Bend, but these still have a long, long way to go before they're stablished and respected as valuable alternatives to existing languages and architectures. From the mathematical standpoint, abandoning the full function in favor of lighter, affine variants (or, if you're brave, raw interaction combinators) would allow one to construct a consistent type theory with axioms that feel morally wrong, such as type-in-type, insanely dependent functions, and the most elegant proof of function extensionality that I've ever came across - although, at that point, I concede "function" might be a huge misnomer. Regardless, I do wonder if important discoveries would be unlocked once we look at this place, and I personally bet interaction combinators will be heavily important in the future, once we exhaust all ideas we have to making circular epicycles. But that's just my unorthodox point of view.
Timo retweeted
i think we should try to build better infrastructure and technology rather than trying to gaslight people about the shitty 20th century holdovers while trying to demonize and ban the people attempting to build something better
Timo retweeted
the 21st century needs infrastructure that is structurally more sound and designed to be incentive compatible and resilient in order to face new challenges and build stronger institutions and facilitate coordination. those stuck in the 20th century often confuse the efforts to build this new infrastructure as “defection” or seeding distrust in the old systems






















