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.
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.
I think to a large extent you feel like it's a skill issue. It's not that the capability is not there. It's that you just haven't found a way to string it together of what's available.
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.
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.
But then all you need to connect that to the outside world is like an optical fiber, right? An optical fiber cable which you can string up on poles, you can run underground, you could even use microwave links if you really wanted to.
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.
I do think that string theorists were a bit overly ambitious… Not overly ambitious, but a little bit overly arrogant in the beginning, thinking they could solve many problems that they weren’t going to solve.
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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