It's such a feedback loop though. Give promising european talent the same amount of cash and it's likely they'd do as well. Sometimes money is the main constraining factor (especially ML related things).
The funny thing about LLMs is that there's no rush in adopting them. They're semi useful but not really right now, but you're not gonna be 'left behind' if you don't make use of them. Everyone involved is working their hardest to make them more capable so when that day comes, you'll just use it to prompt what you want. But there's no rush to try to squeeze out anything out of the current generation which mostly lowers productivity rather than increases it.
At this point the amount of friction added to all these things is pushing things towards just not doing them in the first place (buying less stuff, using social media less). Nature walks and paper books doesn't have captchas.
Really inspirational project. Does anyone know if there's an analog of this to the LLM space? I.e. can you take the embedding of two sentences and get a 'combined' sentence? Either by searching a corpus for the closest match or by feeding that combined embedding into an LLM that generates it.
Innovation is the only way we'll get out of this. Sitting on our asses consuming food for no innovation and a culture of shutting down any attempt like that will only make things worse. This is a good tradeoff for the environmental cost and risks which nuclear brings (which isn't even large).
They might be willing to do things like crawl libgen which google possibly isn't, giving them an advantage. They might be more skilled at generating useful synthetic data which is a bit of an art and subject to taste, which other competitors might not be as good at.
People can lie, people can send emails that look like they're from your boss. People now know deepfakes are a thing and have an immunity from trusting suspicious online meetings where your boss acts different than they usually do. Etc etc. It's not as big of a threat as people want to make it out to be
This is a common occurance I'm seeing lately. People treating these things as oracles and going straight to chatgpt/bard instead of thinking or researching for themselves
Well the counter argument is that with an equal amount of compute, specialized models will win over generalized ones. The reason is that generalized ones have to spread out their weights to be able to do a ton of irrelevant things to the niche subject that a finetuned model can accomplish with fewer and more focused weights.
The real 'winners' here in the upcoming AI wave will be people with existing platforms. They'll replace staff and pocket the difference. This one was obvious but it soon won't be.