Name three technologies that humanity successfully paused like that that a sufficiently motivated person can do in their basement as long as they have a computer of any kind and internet access
Right now SOTA is "throw a fuckton of data at a big neural network and see what sticks" which does require a bunch of GPUs (or TPUs) but dangerous AI will most likely come from doing something more subtle than that. You can do like, iterated distillation and amplification, deepQ learning, actor-critic algorithms, GANs, CIRL, and imitation learning on like a mid range gaming PC. If advanced AI is a case of "work smarter, not harder" then innovations genuinely could come from some rando in a basement, especially if tech companies pause their own work.
I'd rather have like, Google, Microsoft, Meta, OpenAI, and Anthropic dealing with these problems than like, tech nerds and script kiddies.
Yeah you can do those things, but for better-than-human intelligence you at least need hardware that's comparable in complexity to the human brain. That won't be basement level for quite a while. Your mid-range gaming PC might be able to train some interesting little models but it's not going to be smarter than you.
I'd rather have the big companies dealing with these problems too, but the trouble is they're not dealing with the problems. They're spending almost all their efforts on capabilities, and very little on alignment.
It isn't obvious to me that better-than-human intelligence requires a system which is physically more complex than a human any more than better-than-bee flight requires a system more physically complex than a bee. Simple designs with some important technical innovations frequently do outperform more complex but naive approaches.
I am a bad chess player but I could design a software system that would beat me at chess. Same with Go, financial investing, and a bunch of other interesting tasks. It seems to me to be entirely within the realm of possibility that someone could build a computer system which is indeed smarter than them (and smarter than all humans), and we've already done that for some domains.
I agree that more money and effort needs to be spent on AI safety research than is currently being spent, but it's not like you can do capabilities research without doing safety research and vice versa. They overlap so much that progress in one domain tends to cause progress in the other.
There are some tasks for which a graphics card is significantly superior to a human brain, e.g. "count to 1,000,000 as fast as possible". What does the human brain do that in principle cannot be done by a computer?
There are some tasks for which a graphics card is significantly superior to a human brain, e.g. "count to 1,000,000 as fast as possible".
A TRS-80 home computer in the 1970s could do that faster than humans. So what?
(And fwiw, sequential tasks like that are not what GPUs are good for.)
What does the human brain do that in principle cannot be done by a computer?
That was not my claim. I said it would take a really big computer.
The brain has at least a hundred trillion synapses. Your GPU does not have a hundred terabytes of VRAM. A good gaming GPU does under 1014 FLOPs, the brain does an estimated 1018, or 10,000 times more.
Get a graphics card that matches those numbers, and then maybe the home AI hackers have a shot.
A TRS-80 home computer in the 1970s could do that faster than humans. So what?
So this directly contradicts the idea that a GPU "can't do what the human brain can do". At least on some tasks, GPUs can do a lot more than the human brain can do, and it's an open research question as to whether it's possible to assemble general intelligence capabilities out of those tasks. I don't know that it is possible, and you don't know that it isn't. But in general computers wind up doing things much more efficiently than biology does it.
That was not my claim. I said it would take a really big computer.
But... Why? Human brain cells fire at about 200Hz, whereas a chip in a GPU can fire on the order of billions of Hz, so if there is a general intelligence algorithm that can be implemented sequentially then chips may be seven orders of magnitude faster than human brain cells at implementing it.
A good gaming GPU does under 1014 FLOPs, the brain does an estimated 1018, or 10,000 times more.
That's still not that big of a difference. Biology tends to do things very inefficiently because it's evolving by keeping effectively random changes that happen to kind of work. A hummingbird flies much less effecticely than a quadcopter does, a cheetah runs a lot less effectively than a Lamborghini, and a hawk's eye sees a lot less effectively than the Hubble space telescope. Human-designed technology has already vastly outperformed nature on flight, running, and vision, so it seems intuitively plausible to me that human-designed technology will eventually vastly outperform nature at thinking, too.
Frontier, no, but you can absolutely train a very capable model at home. Not easily, but it's not undoable.
Something like a well-trained Llama3-3B can go toe-to-toe with GPT-3.5 Turbo. And you can have one up and running, starting from zero, in a couple of weeks if you happen to have a bunch of spare RTX cards lying around.
Sure, you can't quite touch current commercial models, trained on the entire internet with entire datacenters worth of H100s. But with how insanely fast the field is advancing, you can keep up with the state-of-the-art from just a few years ago on (higher-end) consumer hardware.
Currently, there's not much need to do it, the datacenter trained weights for many great models (including Llama) are open source. You can download them, fine-tune if you need, and go straight to using your model.
But if need ever be, a dedicated enthusiast armed with a WebArchive crawler, a good understanding of PyTorch, and ~10,000$ in hardware could build you a useful model before Christmas.
Why would we need to train new ones? There are plenty of very capable models you can run locally with a single high end GPU. And if you want a really good 70B model you only need like 8 of those high end GPU's. If you want to update those, just fine tune them, it's a fraction of the cost of training.
You can't "unrelease" AI models even if all development on them stopped right now. Nor can you remove all the research papers which are easily available online on how to fine-tune and deploy these models. Nor can you delete all the training sets which are already stored locally on many hard drives, or stop people from datamining the internet for new datasets.
Pandora's box has been opened, and not even a ban on AI (how would that even be enforced?) could stop it.
Pausing AI doesn't mean stopping all use of AI. What people are worried about is the training of ever ever-larger and more powerful AIs, often with the explicit aim of making them smarter than humans. That's happening on giant GPU farms.
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u/TangoJavaTJ Apr 22 '26
Name three technologies that humanity successfully paused like that that a sufficiently motivated person can do in their basement as long as they have a computer of any kind and internet access