| Drugmaker CEOs opinions are irrelevant, last month the mathematicians saw it and understood it, what AI is for better and worse, and now scientists are just waiting for the other shoe to drop. It will be a mix of hype and revolution, dismissing the former does not mean being blind to the latter. update: I really love that within 2 minutes the response to a genuine comment is to downvote/deplatform it, how inimical to rational disagreement and fragile pseudointellectualism our (supposedly STEM) community has become. |
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| ▲ | twoodfin a day ago | parent | next [-] | | Probably in fantasy land here, but wondering if there’s a golden path wherein AI can usefully accelerate the development of quantum computers, which themselves give AI the fast feedback loop it needs to rapidly experiment in chemical and biological domains. | |
| ▲ | calf a day ago | parent | prev [-] | | This is so wrong I can't even begin to unpack this. Mathematics and proof is the basis of scientific reasoning. The proof of concept that AI can automate such a nontrivial part of human analysis is the further application of these computational capabilities to the scientific method, of which math, reason, and logic are the foundations. They are not two ball games. That is why actual scientists are seeing the cards. Go read any essay last month written by physicists. Let alone mathematicians. The entire tower of Babel is at stake and many serious scientists (academics, not the industry ones) are keenly aware. This is entirely separate from hype and slop which are also a very real problem--also, it is not the contradiction that lay arguments simplistically and incorrectly frame it as. Example: you assert data and simulations are needed. Who do you think designs and obtains those things? Human beings. Now imagine an AI with the power of solving Navier-Stokes, a generic AI able to iterate on the very framework where those things are obtained. That would be the scale where progress will happen. It is basically another argument for a type of RSI. I don't know about you but this ought to be terrifying and amazing at once. | | |
| ▲ | fabian2k a day ago | parent | next [-] | | You don't need experimental data to do math. But we cannot currently simulate the effect a specific drug molecule has on the entire human. We have models for individual aspects, but that's it. And the AI CEOs are promising these results very soon. I don't doubt that AI has the potential to fundamentally change the way we do science. But that will still require experiments, it won't be pure calculation for a few weeks and a working cancer drug falls out. | |
| ▲ | preg_match a day ago | parent | prev | next [-] | | The blocker is experimentation and trials. Mathematics and code lives in "virtual" space. Virtual space is nice because everything is infinitely faster than real life. It really doesn't matter much if AI can make the perfect drug instantly. We will still have to give it to humans, then wait, then observe. Perfect simulation doesn't fix this because the human body is not a mathematical model. It's not just that the human body is really complicated, it's that there are many bodies and how systems interact can't necessarily be predicted ahead of time. And, even if you could perfectly predict how a drug will function for any arbitrary person, we'd never be okay with just that. No, we'd require cold-hard experiments to show it is safe and effective. There are many, many potential drugs that are, on paper, perfect. But, in reality, worthless. | |
| ▲ | Bengalilol a day ago | parent | prev | next [-] | | You are right, but let's not forget the drug dev may be tougher because it needs modeling (both chemical and physical). Math is a formal system created by humans, but biology is an objective reality we don't control. | |
| ▲ | wuschel a day ago | parent | prev | next [-] | | Could you recommend/point out some of those essays? Cheers! There is the - mentioned in another comment in this branch - dinstinction of real world experiments and "tricky" data. To my understanding LLMs are good in areas they have in part covered in their training data. The biological, chemical and physicial world is full of effects we have not measured yet, or not measured to a sufficient precise degree. That would be my argument. The question is, however, not whether LLMs can develop drugs on their own, but what advances they will bring. | |
| ▲ | direwolf20 a day ago | parent | prev [-] | | They are different ball games. Math proofs can be brute forced by a computer, in principle, but that takes too long so we need good heuristics. Drugs can't be discovered on a computer, even in principle - you always need real-world feedback. Consider the difference between asking an LLM to translate a poem, and asking it whether my front door is open or closed. No amount of clever computation will make it reliably answer the latter. |
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