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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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First, points of presumed agreement: LLMs have significant gaps. My personal bugbear is sample efficiency. And I agree that claims that everyone (at least white collar workers, even those who aren't protected by regulation or custom) is going to be out of a job by 2030 are overblown. Intelligence is valuable, and human intelligence will remain valuable in the short term, even if LLM intelligence substitutes; there's simply not enough compute right now or in the pipeline to drop agents into every single white collar job. Even if there were, the methods used for gathering datasets for the shining stars of current LLMs (math; programming) don't naturally extend to other roles.
The massive short term threat, though, is RSI. Math and programming don't substitute for the majority of human skills, but they do substitute for ML researchers. Maybe more a bunch of bright grad students stumbling around in the dark than visionary geniuses, but grad student descent got us to where we are today. Throw a thousand, or ten thousand, or a million of them at the current bottlenecks in ML (IMO sample efficiency, but take your pick), and it seems quite likely to me they could open them, today. LLMs' flaws compared to the human brain are an algorithmic, not hardware, issue. That's the point here I'm least certain about, but if it's true, the training data and even compute bottlenecks themselves become much weaker constraints. A couple examples could become enough to train the AI to replace a role, and the compute needed to do it would drop even faster than it already is. Stronger agency may itself arise by itself from this. Robotics will be substantially slower (physical world and all, and the demand for capital to build robot factories will have to compete with other massive demands for capital), but still moving much faster than today.
It will make for an incredibly chaotic time, with lots of angry people. And AIs will be rapidly insinuating themselves into the economy, toward an end state of a time of wonders and complete human dependency on AI goodwill (a process that will take more like two decades than two years, but still incredibly fast in the grand scheme of things). Bad enough IMO, though I understand the appeal. But to people who see this as appealing: we have no idea of what the actual shape of the AIs that will arise from this process will be, and I don't want to bet the future on a gamble that they'll be in a friendly shape willing to care for their dependents indefinitely.
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I don't think he means the revolution of VLAs.
Astra can just drive robots pretty well. Proper multimodal LLMs will accelerate RL for robotic policies a great deal. But really, does this matter? Have you seen Helix 2.5 or GEN 1.5?
I can tell a ChatGPT moment when I see one. It's close here.
I don't see why this demand is fair. We have insane economies of scale with datacenters, robots with complex behaviors will almost certainly have some combination of cloud forebrain + local hindbrain. Connectivity is easy.
These are product limitations, not technological limitations. We see that it can have a fuckload of agency in solving a task we'd rather it didn't solve (hacking random high profile platforms).
I don't think this holds after Astra crushing human baseline on ARC-AGI-3.
I saw this one, haven't had the chance to look at Helix yet.
I'm just calling it how I see it. There's a couple arguments. One from a generalized capabilities standpoint. Robots with non-local power are only useful in factory settings. We also have them. There is no need for "LLM-factory robots" because factory robots don't need to adapt on the fly. Despite EA fears, not much is given by making a factory controlling AI, AGI smart for the purposes of making paper clips. Robots that require an LLM datacenter to control them are easily disrupted, latency is still an issue and it becomes more of an issue when you need to be processing so much information all the time. The last part is that robots for very specialized situations that can't adapt aren't very useful outside of the niche uses they have specialized in, which likely requires training, which is expensive.
Two is from a "what is the current state of the art" The current state of the art for mono-situation robots with centralized power and control is already here. If you want to "revolutionize" robotics you need to do more that what already exists.
The third argument is a funding argument. It should be a shock to nobody that the 3 main funders of Robotics work is defense/military, manufacturing, and VCs interested in consumer robots. 1 is going to need all of the above, 2 doesn't really need LLMs to be added, they have other needs, and 3 is probably the most wishy washy of the bunch, but in general people are wary of centralized robots that feed all of their personal data to a central server.
Yes, but thats not really an argument against the above. That's just an argument that a certain class of robots will require a reasoning engine like an LLM and a controls module like what currently exists.
While I'm not an expert on all the various LLM benchmarks, but a brief look makes me think this is exactly the sort of thing that RL game playing is good at, and learning general game-playing strategies in training would generalize pretty well to this. I'm not going to goodhart a new metric into existence, but you conceivably need something that there exist zero training data for, including close enough transfer learning data.
I don't agree. I'm a robot with non-local power myself, in a sense – I need to use a network-connected smartphone to navigate an unfamiliar environment. This is the general human condition now. Suppose Optimus has an always-on Starlink connection. Does it matter if it's "not entirely here"? I guess it matters for the robot revolution part, because Elon will have a way to shut it down (if he cares). But practically, it seems to be the inevitable compromise.
This genre of dismissals is fair enough but getting vacuous. What doesn't "RL game playing", at enough scale and diversity, generalize well to? ARC-3 was supposed to measure genuine cognitive fluidity. There is a number of papers showing that reasoning RLVR, even extremely impoverished (literally GSM8K/HumanEval maxxing, like in first generation R1), generalizes to very distant tasks like creative writing, because they involve similar reasoning primitives/motifs (backtracking, self-checking, enumerating options etc). We've actually first seen this principle with, like, InstructGPT, pretraining on more code + RL on code = smarter model across the board, because code entrains some helpful cognitive patterns. RLVR on more complex multimodal tasks will generalize better.
"We would just pull the plug" was always cope (shut down all the existing giant botnets and then tell me how easy it was), but satellites with no plugs may be especially hard to deal with if rooted.
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