it is simply ridiculous that a ruinously expensive policy can be budget-neutral if the expense consists of wasted time rather than wasted money. But that's still a cost!
I think three of us have total conviction about the scaling law. That that I think we do. I do think the exact architecture choices and data mixtures is where the the devils are in the details.
It's like if I put electrodes in M1, you will probably be using a computer in an hour. Um and so it's just it's easier, it's more amenable to biology in many ways.
If you are working on material science, if you are working on pharmaceuticals, if you are working on chemistry, you in 2 3 years or even today are doing your role as a scientist dramatically different because of artificial intelligence.
open models are still incredibly useful, but fill a long-tail ecosystem relative to the closed counterparts that have monopoly ownership stakes in the most valuable areas like knowledge work collaboration, drug discovery, SWE, etc.
I think we're we're ready for complete re rewriting of how scientific discovery can be done because for ages, I don't even know how long, it relies on smart humans retaining what they have learned from other smart humans
the the breakthrough for us was realizing that AlexNet was not AlexNet. That AlexNet was an approach with deep deep learning that allows you to learn any function.
That is mostly in the realm of chemistry and is intrinsically far more tractable and predictable; given a validated target, good teams usually reach a decent molecule.
My one little bit, the one little bit of of fundamental uncertainty even on long time scales is this thing about tasks that aren't verifiable. Like, planning a mission to Mars, like, uh you know, doing some fundamental scientific discovery like like CRISPR, like, you know, writing a writing a novel. Hard to hard to verify those tasks.
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take prompting and as an engineer, there's a range of prompting abilities. uh the way you discretize and split up your task matters. And if you assume that uh a model can do more than it can do, then you're going to have a bad time.
so it's not a simple matter of programming it's not you know typing into a keyboard until or or I guess asking cursor to do something until a piece of software emerges, it's exploration, it's discovery, it's posing hypotheses and validating or invalidating those again and again and again.
I would find it impossible to not pursue a discovery that I could imagine my way through, if I can really see how to get there. I cannot imagine abandoning it for some other reason, fear that it would be misused, which is a real fear.
So, I think she comes back in to relevancy in a different way than Friedman does, because I think in some ways she's tapped into a more universal human longing for independence and autonomy and self-creation and self-discovery.
Luis and Walter Alvarez, who made that incredible discovery, initially their discovery was based entirely on impact proxies, just as the Younger Dryas is. There was no crater. And for a long time they were disbelieved because they couldn’t produce a crater.
Another challenge for Class II biotechs is that even if you have a nifty computational drug discovery platform, you are still often bottlenecked by the rest of the drug development process. It takes 1,000 (or more!) small steps to advance a drug through discovery, development, and past the regulators.
Whatever founder mode consists of, it's pretty clear that it's going to break the principle that the CEO should engage with the company only via his or her direct reports.
For now, I am not convinced that issuing press releases about your compounds that talk about their discovery through AI techniques is sufficient to expect greater things from them.
And the punchline is the equation is much smarter than Albert Einstein because Albert Einstein did not know about the Big Bang, he didn’t know about gravitational waves, he didn’t know about black holes but his equation did.
The only way to learn is by doing things. That we can read about how to swim and we can read about how to make uh uh uh a vegetarian maki roll, but you will not learn how to do it until you do it. And the reading, the listening is important preparation, but learning is the act of failing on our way to mastery.
I think when a system can significantly increase the rate of scientific discovery in the world, that’s a huge deal. I believe that most real economic growth comes from scientific and technological progress.
Such prolificacy is unlikely to be repeated any time soon; if current trends hold, a drug discovery scientist starting their career today is likely to retire without ever having worked on a single drug that makes it to market.
The most significant factor preventing us from taking better molecules into clinical trials is not our ability to design arbitrary molecules, but our (lack of) understanding of underlying disease biology. In other words, we often know how to make something, but not what to make.
In reality, the productivity crisis in the pharmaceutical industry is the culmination of decades of just about every aspect of drug discovery and development getting gradually harder and more expensive.
It's interesting to me that large language models in their current form are not inventions, they're discoveries. The telescope was an invention, but looking through it at Jupiter, knowing that it had moons, was a discovery.
I’d say for humanity, it’s both a tribute to the ability of discovery and the ability of really believing in things so that you have the confidence to go look for them, but it’s also a cautionary tale that you don’t want to assume things before they’ve been actually found.
This is incredibly interesting because from our perspective, we have forgotten (if we ever knew) what went into the process of taking the thousands of villages and regions differing in all sorts of ways
The specificity matters here because each innovation in the chain required not merely the discovery of the principle, but also the design and an economically viable use-case to all line up in order to have impact.
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