Happy to see that our work on diffusion model objectives (https://t.co/0qJRyangWX) is starting to get noticed, with e.g. Stable Diffusion 3 (https://t.co/vHxqwLH0eY) building on our result that the flow matching objective can be simply understood as a special case of diffusion.
To be clear, bitemark evidence is junk science. There has never been any scientific research to support the idea that bites on human skin can be matched to the teeth of one person, to the exclusion of everyone else.
The greater the confidence in a clear view of the battlespace, the greater the reliance on military strategies that could benefit from that clarity, even though they might not be the best ones to meet political objectives.
I think we're at this like in amazing moment in the world where the bottleneck is not the progress of the AI models, the bottleneck is diffusing that through the rest of the world and and helping the world adapt to this amazing technology that already exists. Like I think if the models didn't improve at all from today, there would still be like decades and decades of like total upheaval and change in the economy and how the world operates and and everything around us
Take Eroom’s Law: the number of new drugs approved per dollar of R&D has fallen for decades, even as our scientific tools have grown vastly more powerful, the exact opposite of what the existence of more raw capability would predict.
But the maritime world is about negative objectives. It's preventing things from bad things from happening. And um you can never prove that you prevented anything.
does that mean the robotics industry will also be generating trillions of dollars of revenue? My answer there is yes, but there will be the same extremely fast but not infinitely fast diffusion. So, will robotics be be revolutionized? Yeah, maybe tack on another year or two.
And then I also think um you know right now for matching it's like everything's done at literally the last minute. It's like someone dies, an organ comes available and like you're just kind of calling around trying to find like what patient is available to get this organ and you don't have that much time to make the optimal match, right?
By contrast, AI is increasingly matching the general cognitive profile of humans, which means it will also be good at the new jobs that would ordinarily be created in response to the old ones being automated.
ultimately I think what matters is the use of AI in their economy to create economic value right I mean that's the uh the diffusion theory which is ultimately it's not the leading sector but it's the ability to use the leading technology to create your own comparative advantage right so that I think will fundamentally be the core driver
I've worked a lot of companies right but I'm wrong all the time and I think consumer behavior can be very fickle and especially when you work at a company you become a power user naturally. So sometimes you you may forget like what the actual user experience is for a brand new user.
The US will lead for some time on breakthroughs on disruptive technologies, the zero to one technologies that ultimately change the world. But innovation is a process. It goes from invention to production and commercialization and diffusion, diffusing technology throughout all parts of the economy. And on those two stages, I think that China has a unique advantage, even if it still can’t do the zero to one breakthroughs, because in the end, how much this technology is adopted by the countries and by the various parts of the economy is fundamentally crucial to how much productivity will be unleashed.
Some models could be used both for good and ill, yes. Diffusion models in particular could be used by bad actors to hallucinate toxins or other harmful proteins.
So, Japan has a law which you're allowed to train on any training data and copyrights don't apply if you want to train a model, A. B, Japan has 9 gigawatts of curtailed nuclear power. C, Japan is allowed under the AI diffusion rule to import as many GPUs as they'd like.
The cost to discover and develop a drug today is orders of magnitude higher than in the 1950s. Despite this, the probability that a drug entering clinical trials will eventually reach the market has hardly improved in the intervening years.
Whether one does find a good spouse or not depends more on one's orientation towards actually building relationships, and whether they can identify non-trivial traits in a partner that would contribute to that.
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.
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