If you stop now, you're just left behind, because there's no way everyone will stop.
At this point the only logical course of action in an adversarial situation is to double down and keep researching, otherwise some other country or culture with different (and possibly worse) values ends up dominating the technology and you're left behind in the dust.
The genie is out of the bottle, there's not putting it back in.
It’s quite clear that OpenAI has a significant lead over everyone else. The only other country outside the west that even has a chance at developing something better than GPT-4 soon is China. China has a pretty cautious culture as well so it’s quite possible that a bilateral moratorium can be negotiated with them.
ADDED: Even without considering X-risks, China’s rulers cannot be pleased with the job displacement risks that GPT-4 plus Plugins may cause, not to mention a more powerful model.
They have trained a huge number of college graduates and even now there are significant unemployment/underemployment issues among them.
ADDED 2: If you think many companies can do it, please identify a single company outside the US/UK/China with the capability to train an equivalent of GPT-3.5 from scratch.
Another example is Meta only finishing OPT-175B, a near equivalent of GPT-3, two years after it.
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Added to reply:
GPT-4 got much better results on many benchmarks than PaLM, Google's largest published model [1]. PaLM itself is probably quite a bit better than LamDa in several tasks, according to a chart and a couple of tables here: https://arxiv.org/abs/2204.02311
It's unclear that Google currently has an internal LLM as good as GPT-4. If they do, they are keeping quiet about it, which seems quite unlikely given the repercussions.
[1] GPT-4's benchmark results vs PaLM: https://openai.com/research/gpt-4
OpenAI doesn’t publish as much as Google so we don’t really know how long or in what periods they were ahead.
And there’s no organization outside the US/UK/China with the same caliber of AI engineering output as Google.
OpenAI is not a country, it's a company.
GPT models on par with GPT-4 can be trained, well, by companies. You don't need nation-state levels of resources.
GPT-2 and GPT-3 are the same algorithm based on the same open source library. GPT-4 most likely is as well. You can literally fork the repo and if you have enough VRAM, cuda cores, and time, you will get GPT-4. High Schoolers could do it. Amateurs are already replicating LLaMA, which is more complex than GPT and not even a month old. (it's just smaller = fewer GPUs required)
Another example is Meta only finishing OPT-175B, a near equivalent of GPT-3, two years after it.
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GPT-4 got much better results on many benchmarks than PaLM, Google's largest published model [1]. PaLM itself is probably quite a bit better than LamDa in several tasks, according to a chart and a couple of tables here: https://arxiv.org/abs/2204.02311
It's unclear that Google currently has an internal LLM as good as GPT-4. If they do, they are keeping quiet about it, which seems quite unlikely given the repercussions.
[1] GPT-4's benchmark results vs PaLM: https://openai.com/research/gpt-4
Since the release of the Attention paper, they havent come up with any groundbreaking idea, that was five years ago. Where is their research? All they seem to have are technical descriptions with scarce details, deceiving tactics, fiddling with parameters, and an abundance of pointless ethical debates. Can we even call this "research"?
Can you list 1-2 research organizations, in any field, with more important output in 5 years? Bonus points if outside the US/UK/the west per context above.
It is not remotely intellectually challenging to go to the moon. It just takes rocket fuel. Newton solved motion hundreds of years ago, and now high schoolers compute it in physics class.
if their lead was significant they wouldn't have admitted to not releasing more info about GPT-4 in their paper due to commercial reasons. What ever secret sauce they have apparently isn't that significant or they wouldn't be afraid to talk about it
Were Newtonian's Laws of Motion a significant progress?
- International treaties to avoid nuclear proliferation and development, as other commenters have pointed out
- National moratoriums on gain-of-function research
- Regulations on biotechnology related to human cloning and human gene editing, and industry/academic norms set by conferences such as the Asilomar conference
- International treaties on climate change, such as the Montreal Protocol on CFCs and the hole in the ozone layer
Even if we assume that international coordination fails, 2 facts give us some breathing room to pause (not stop!) large experiments to at least figure out what we're doing and how to adapt society:
1. American companies are ahead of Chinese companies (I assume that's what you mean by "other country or culture") right now by at least a few years. The rest of the industry is barely catching up to GPT-3 , which came out in 2020.
2. China cannot unilaterally continue the race right now because their compute supply chain critically depends on technologies that the West has monopolies over, like sub-10nm silicon fabrication, advanced GPU technologies, and ultraviolet lithography. We're already using this lever, actually! For example, Nvidia exports of A100 and H100 GPUs are no longer allowed to China at scale.
...you do realize that countries haven't signed that right, and nothing stops them from pulling out either, right?
Those things, as well as vital testing as a gate for deployment, aren't being upheld, and that's exactly what makes promises grandiosely destructive and keeps outcomes harmfully and wildly unpredictable.
I think in months to come we'll find out that many of the great new Ai products launched will prove themselves to simply be inauthentic-ly scripted fraud-based solutions backed by hidden human intervention because of this environment of unchecked expansion and eager over-deployment.
your suggestion is that stopping nuclear proliferation shouldn't have even been attempted, despite the fact it actually worked pretty well
It seems likely there exists a fully distributed training algorithm and a lot of people are thinking about and I suspect a coordinated training network, perhaps with a reward system, can hopefully be created. Lots of GPUs out there and we just need to figure out how to coordinate them better and shard all the training data.
It's quite relevant to your argument, interested on your thoughts.