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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.
In the jungle book there is a scene - Shir Han is dragging Baloo with his tail and this legendary exchange takes place:
If we slow down now - we will certainly feel the teeth.
Qwen 3.8 flash next abliterated exists. That can be run albeit slowly on consumer hardware and my hunch is that it is enough of a both foundation and power multiplier - for the mythical beast that is ASI to eventually emerge. The cat is out of the bag and stopping now
Also one of the things the Yuddites seem incapable of doing is figuring out that superintelligence does not mean omnipotence. The laws of physics will still work and some other too. Internet is trivial to be made more secure, not every infrastructure needs to be connected to it anyway.
If the frontier labs slow down - the research will continue. My hunch tells me that we are really far away from the limits of IQ per watt, per weight, per wafer, per harness. All the world militaries and terrorist groups have great interest in making full fledged AI work in the limited hardware a drone possesses.
The datacenter big ASI have always been safe. You just put a lot of semtex in the foundations of the DC with dead man switch. The other one - the fast nimble one that could fit on consumer hardware is the dangerous one. And I don't want it to emerge in Yemen or Sudan.
The question is 2028. 2026 won't be important because the red tribe will hold the presidency and if some of the conservative judges have functioning brains will let themselves be replaced in the lame duck if they lose the senate. But who knows what the job market will be in 2028 and if the effects on AI will be suddenly felt by then.
Personally I am accelerationist. I have been since reading the Lord of Light.
Closeness to sam altman is pure coincidental.
From yesterday you could run the dense qwen 3.8 on 1080ti. The research will continue.
Eh, in the sense that the AI can do independent target discrimination, sure, but that's already a solved problem (LRASM can do this, for instance) although it can be iterated out further.
World militaries and terrorist groups have no interest in making sure their drone processor can write poetry, or whatever.
I can only assume that you haven't of the Vogons.
I am in no way specialist on how paperclip maximizers work, but to me the actual capabilities of the models are mostly dressing over the underlying tech. And the fact that they use the inefficient human language is mostly artefact of the google babelfish experiments.
To make drones properly lethal you need meshed swarm coordination, ability to create and adjust tactics on the fly, without much centralization in conditions of severe jamming. So they do have incentive to cram as big models as possible in as limited hardware.
Surely that sort of thing is in the domain of the CIA!
You're sort of missing my point - the goal here is to cram the most optimized models - that is, the models that are going to best allow them to do meshed swarm coordination, or whatever. Larger is not always better; better is better.
I have no particular reason to think that the direction LLMs research is currently developing is good at all for that sort of thing. Cramming Astra into a drone is going to result in something that's much slower and less decisive than a simple deterministic script, because LLMs are compute-hungry. That's not to say that neural networks won't be helpful - computer vision is great, for instance.
But LLMs are not the only form of artificial intelligence, and for most military tasks a strong general-purpose LLM is both overengineered and poorly suited for the mission, especially the mission of terminal target determination. For intelligence synthesis, they likely have some promise.
You have the right of it.
The additional issue is data, to a shock of nobody paying attention. Military data is near non-existent. And the data that does exist is low-resolution, or tightly controlled by non-ML/AI folks who have been trained to have negative desire to share (The Security Clearance process over time selects for a type). LLMs are the Ford F150 of ML models. Half the battle is often getting a very specific tank configuration that is actively trying to hide in foliage from the very CV models you are trying to detect it with. You have N=3 samples of this tank sitting on a tarmac somewhere and it turns out the ability of your model to extrapolate that picture to 3.0 GSD satellite or drone footage is abysmal. And that is before it even starts attempting adversarial countermeasures.
There are of course solutions, but they don't work as well as folks imagine they do, including generative image generation. Most of the examples of drone based hits are FPVs or FPVs with a "wait" situation where the drone hovers, detects a general class and is then given approval by a human analyst who manually reviews the vid/image.
Very interesting! I actually thought that open-source imagery would be better resourced than that (although of course it's pretty easy to mess with the profile of a tank with a can of spray paint or a camouflage mesh, which I guess is part of the problem).
Always very heartening to have an industry professional tell me I haven't gone completely off the rails, thanks.
There are what a couple hundred grainy low res videos of Ukr drones targeting Russian tanks + other vehicles? It's well below the threshold to train an ATR model on data quantity, without even getting into quality. Stock images are from non-operational angles (you for the most part aren't level with targets, its an isometric or overhead view, NERF to solve that is expensive and has dubious performance improvements, and having worked in the generation of low-sample military images, it can actually be quite hard to secure exemplars in EO imagery. SAR imagery just sucks, speckle is multiplicative noise which makes it nearly impossible to remove and hard to detect around.
Humans are obviously much easier to target, however then we get into friendly fire and the fairly disturbing optics of AI drones targeting your own soldiers... There are logistic issues around distributing RFID and similar tags to prevent it, that to my knowledge are being worked through. But the main goal of drone warfare is destroying expensive vehicles with cheap drones.
Training datasets for the ATR stuff are being developed but its sort of an incumbent's advantage, with active known defense contractors having most of the customer connections to be able to get the data, and the resources to label it. Unfortunately they are also slow AF, which is why defense tech startups sorta eat their lunch in certain spaces. Transfer learning is something I've seen recently, taking civilian jeeps with SAM-esque models and applying them to humvees etc. It's always much easier to train on your own nations hardware anyway. Though it gives bad optics to Brass, who don't like it, and an open question on whether it will transfer.
For Russian (or American) kit, I figured you would have good most-angle photos from parades, demos, and such. You're right though that these are mostly taken from the human level.
I imagine that another obvious issue distributing RFID tags to your troops is that they can be used by both sides. I realize that in theory they will only respond to coded radio signals that in theory only one side can transmit, but if you're putting them everywhere and then putting the transponder in something as failure prone as a kill-drone your enemy's going to be reverse-engineering your IFF on like day 2 of the war.
I've speculated, based on my knowledge of machine imaging, that we'll see "wartime camo" become a thing in future wars, with "peacetime camo" designed to be something maximally different from operational paint schemes to confuse ATR. Apparently the Russians are already using a variety of disruptive paint schemes in Ukraine. We can only hope it results in making razzle-dazzle great again!
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Wow my field has made it onto TheMotte, spooky. It's not quite so simple as big LLM models on limited hardware. There are a lot of networking problems on swarm interfacing if you want something that is truly adaptable. A lot of swarm robotics is essentially "dumb" control algorithms with software permutations, because LLM-agent hallucinations are rough + LLM sizes require truly monstrous hardware on drones. There's work on fitting them to Jetson Orin Nanos' but it's a slog just from the amount of compute needed + performance of smaller quantized models etc.
This is generally a case against the LLM + Drone combination, too much extra stuff not needed. Part of the problem with current AI research is that it is all in on LLMs but the branch off of not-LLMs but with LLM-like capabilities for reasoning for tactics or strategy are not there. If you want the reasoning you get poetry on your drone too. It actually goes beyond mesh networks as well, as a mesh network is every node connected to every node. You actually want localized connection networks that do multi-hop message passing. Much more resistant to jamming.
Idk maybe I should write an effort over-post on drone swarms, I love this subject.
raises paw Interested!
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I'd love to read it, if you ever do.
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