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Roughly 3 years ago, specifically in December 2023, I've reiterated my longstanding prediction that «the culture war's most important front will be about AI», and specifically AI accelerationism vs anti-AI/AI safety movement:
And the following political compass:
Seems like this was too much complexity for big tent politics in the US, at least so far; and the Trumpian State sees no use for the EA network, and instead (pretty rationally) perceives it as an uncontrollable alternative center of power. Instead we have, essentially, anti-AI left + pro-AI right (with some notable exceptions – eg. Steve Bannon, apparently, has been anti-China specifically because of his concern about Chinese AI progress, and now joins hands with Bernie Sanders). I've also failed to predict the salience and extent of Chinese open source dominance, as well as the bizarre datacenter water use backlash (charitably, can be shoehorned as Luddism?). Nevertheless, we've «succeeded» at the core task of making this a culture war front. The degree of (unnecessary) politicization of the issue is incredible. Trump is doing the yeoman's work, lumping it in with random Blue-coded ideas he disapproves of “Global “Warming,” where everyone was going to be dead by now, RUSSIA, RUSSIA, RUSSIA, UKRAINE, UKRAINE, UKRAINE, or Impeachment Hoax #1, or Impeachment Hoax #2.”. Jensen Huang enjoys his role as the kingmaker who has the King's ear. Sam Altman is opportunistic as usual but, after recent incidents, is genuinely spooked about AI risks (I have it on good authority that OpenAI really intends to slow down some internal projects specifically to bolster their oversight). The quasi-Masonic part is shaping up nicely, too – Dario Amodei, who's become the poster boy of Woke Left AI, promotes Embedded Evaluators with the central example being METR, very deeply connected to Anthropic and the broader EA scene. The EA itself is more explicitly Left-aligned now, despite efforts of some to paint them as TESCREAL eugenicists (the woke cancellation of Bostrom in Jan 2023 was the canary in the coal mine, Yudkowsky laments the missed opportunity of bipartisanship). There are clearly politically coded reports on prominent doomers.
This is all a bit meandering. What I want to know: how do you see this going further? We aren't anywhere close to the wall of capabilities; there are no walls in sight. Anthropic and OpenAI are holding back already, but their products will keep getting better, and fast. Google will make a comeback at some point (maybe in a couple weeks), xAI and Meta may catch up too. In my book, we (well, they) have practical superintelligence that's sufficient for both unprecedented productivity acceleration and really devastating, nation-crippling cyberattacks, which I guess will be discussed with Xi soon. At this rate, in a few months the level of capability Fable 5.1 or GPT 6 Astra will be mostly commodified and uploaded to HuggingFace (owned by Nvidia now). And those are relatively weak systems compared to internal models, which can build models that are vastly stronger still, without even any R&D breakthroughs, just by virtue of synthesizing stronger data trajectories and designing better RL environments. Superintelligence, in other words, is baked in. By Q1 2027 we'll see a jump from Astra that's at least as big as Sol => Astra. I fail to understand how that won't steamroll companies trying to build their moats on products downstream of frontier AI, labs need every bit of revenue to cover their costs, which will only increase due to growing self-imposed safety requirements; employers outside the AI sphere will also be increasingly feeling the heat. There's a whole ugly dimension of circular financing, too.
Americans as a whole are pretty pessimistic about AI even at these mediocre levels of diffusion (I am skeptical of this data that purports to demonstrate much lower adoption than in China, Americans are probably lying more due to widespread negativity on AI, but in any case AI isn't currently doing most of their jobs). Astra+ level models with very low error rate and 300 tok/s output totally can replace most knowledge workers. On the other hand, it seems that so far AI has not caused anything like mass unemployment, and perhaps economists have a point about comparative advantage, so that'll reduce the intensity of class dynamics.
Democrats are likely to sweep both chambers of Congress, which I guess is what Dario is hoping for and why he feels emboldened to antagonize Trump&Hegseth. Nevertheless, the needs of national security and GDP-maxxing (as well as the Executive's will) should prevent any nontrivial exogenous industry slowdown. So by default we'll see further crystallization of Red Accelerationism vs Blue Decelism, and as AI becomes more undeniably scary, that may begin to eat into the Red political base. It'll be interesting to watch, but I'm really uncertain as to how it'll go.
I was expecting AI to affect politics. I was not expecting to have no idea what that would look like. I very nearly spat out my drink when I saw Trump shit-talking Effective Altruists on X (or some intern with the password, whichever, at this point the distinction is academic).
At this rate we're getting a proper Race™, and with China, because who else could plausibly compete? Add the steadily growing military appetite for LLMs and it won't take more than one serious war before someone in the US government looks around and decides that nationalizing the labs might make perfect sense. I would not bet against that person getting their way.
I'm still reasonably optimistic that we'll land on an acceptable outcome, where "acceptable" means anything other than "everyone dies" or permanent disempowerment under a totalitarian regime whose values are inimical to mine. I want to stress the despite in "despite recent events." We have some very interesting people in charge of things, for a generous definition of "in charge"; nobody seems to have much actual control. Trump would be the closest, which is...
I've made half a peace with all of this. We could call it resignation. It's not quite fatalism.
It could be worse. The major labs calling for voluntary slowdowns and then unilaterally implementing them is a very good thing, IMHO. We have had some major warning shots, in the Yudkowskian sense, and it seems to be having >0 impact.
Now, after a lot of use and a lot of deliberation, I've more or less concluded that Astra is dangerously close to AGI, and probably meets most reasonable criteria for it (good luck finding a consensus definition; the goalposts are on Mars). I'd like to see proper continual learning, of course. In terms of raw intelligence, though, we're there and the right scaffolding buys enough in-context learning that the gap stops mattering for an absurd range of practical applications.
Navier-Stokes, people. Rogue agent swarms on the internet. Aren't you stoked about things?
I want to muster up the balls to just declare it: AGI is here. The issue is that I don't know whether my reluctance is epistemic caution or a psychological hangup - in the sense that saying it out loud would mean it's never been so over, or that it's barely started, and I can't tell which of those I'm more afraid of.
Does it even matter? I'd rather look at what the models actually do than argue semantics. They do things I could never do, and in some cases they've done things that eluded generations of the smartest humans we had. The competitive advantage I retain (and most people, really) is increasingly just whatever comes free with being a physically embodied, low-latency neural network with continual learning switched on by default. Which is to say: a body and a brain. Both increasingly close to obsolete.
Give the robotics people a year, since LLMs are already revolutionizing robotics too (why the fuck not). Maybe we get "true" continual learning, or maybe we get increasingly sophisticated substitutes, constant-train-and-deploy on timescales short enough that the whole question becomes moot. I'd take either.
So, uh, this is me, self_made_human, saying the future is here. It's just not evenly distributed and it smells weird. Welcome to the Singularity, motherfuckers. Enjoy your stay. It'll be many things, and boring won't be one of them.
The more the world turns into what I expected it to become, the more ridiculous and unreal it all feels. I miss when this was LessWrong nerd bullshit. I saw "STOP AI" graffiti on the way home from work today. I was watching the AI Doc on Netflix, occasionally getting exasperated, and then feeling strong emotions about the fact that I know most of the people interviewed, and have spoken to some of them personally in calmer times.
I'm going to do my best to enjoy the ride and have a life for as long as my individual actions still make a difference to my outcomes. I expected this to happen. I have mixed but slightly positive feelings now that it is.
Uhhh what? VLA is good but its not revolutionizing, but so are Diffusion models and those are not LLMs. The also aren't "Astra" level in reasoning either. To actually revolutionize robotics on the level you seem to be catastrophizing about would require entirely local models running on local power, local compute, able to be applied across a wide variety of operations in a wide variety of environments. We're not there unless you have some additional evidence to prove your point.
AI used to be the word for Asimov-level artificial intelligences that could make their own decisions, and operate with their own agency, maintaining long term planning horizons, memory, possibly even emotions. The word got shifted to AGI. If you want a empirical definition its science fiction AIs like the Culture, The AIs in Hyperion, Daneel in Foundation. The goal posts keep getting punted because people keep trying to change what was previously intuitively understood so that they can sell their idea as the one true AGI, win internet arguments, or catastrophize about the oncoming doom. Astra is only able to really solve problems, it has a moderate amount of self agency in the scope of completing its tasks, and exhibits some planning ability, again in the scope of its assigned problems. It's powerful enough to be "dangerous" sure, but its not really AGI as is commonly understood.
The competitive advantage that you retain is the ability to learn shit without requiring millions, even billions of examples. You, like any smart human also possess the ability to do analogical reasoning (out of distribution reasoning), something that eludes current LLMs by and large.
Why entirely local? That is, as far as I can tell desirable rather than necessary, in a world with wireless internet connectivity. Your fridge might have an internet connection these days.
Waddle is an example. Their agents control robots through code and action models, build reusable skills, and even collect data to train smaller policies. Phillip Isola discusses the broader implications here.
I did say to give the robotics people a year. On reflection, I'd be willing to water that down to two or three. I'm not a robotics expert, and I certainly wasn't announcing that we've solved human dexterity. SOTA-Robotics:Human mobility is not SOTA-LLMs:Human Cognition, the latter is much closer, but it's trending the same direction.
But the possibility that robots inherit substantial capabilities from each improvement in frontier models seems rather consequential to me.
A Culture Mind is a hell of a minimum specification. Two of the three examples are ASI, not AGI. I haven't called Astra an ASI.
And fictional examples don't give us an empirical definition. Which capabilities are required? How reliably? Why should I care whether it has emotions when assessing whether it can do my job?
“It's only able to really solve problems” is a rather confusing counter-argument. Depends on the problems, surely. Likewise, working towards an assigned goal doesn't tell us much about how general the intelligence doing the work is. I spend most of my working day performing tasks I am assigned, that do not intrinsically motivate me, mostly because I get paid for it.
You're conflating training the model with teaching the trained model something new. I don't start from a blank brain when learning a game either.
ARC Prize tested Astra on unfamiliar interactive environments. It scored 62.7% with their standard interface, and 99.9% with an interface preserving its reasoning state. In the latter setup (at maximum reasoning effort) it used fewer actions than the median successful human on 96% of levels.
That isn't a comparison of total training data or energy efficiency, which wouldn't necessarily be the decisive factor:
Let's say a hypothetical architecture X is 1000 times less sample efficient than a human for a given unit of performance - well, it would be unfortunate if we managed to give it 10,000 times the training data a human can ingest, wouldn't it?
A mere year or two ago, a lot of people were awfully confident that we'd run out of training data, or that synthetic data and RLVR wouldn't pan out. God knows what the frontier labs are up to these days, but it has clearly not proven an impediment.
It does make “these things need millions of examples to learn something new” rather difficult to sustain, regardlws sof practical relevance - which I dispute. They observed it figuring out unfamiliar mechanics and constructing symbolic models to plan around them. Your claim about analogical reasoning needs similar qualification. I do not believe they're the same thing anyway.
Proper continual learning remains a significant advantage. I said so. Whether that advantage remains economically decisive as scaffolding improves is what I'm uncertain about. I lean towards a no.
Alternatively, we could solve continual learning, or simply reduce the temporal delta between train-deploy-train to the point that it has no practical relevance.
A model contributing original mathematics, writing software and learning unfamiliar environments already exhibits an awful lot of generality. It can even hold a conversation, which, I will note, would have been mind blowing not that long ago.
Call it AGI or don't. A rose by another name smells just as sweet, and has just as many thorns.
My concern is how much useful work it can do, how quickly that range is expanding, and what remains exclusively ours. I don't expect the terminology to buy us much time. I certainly don't want to spend more time arguing terminology.
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