[HN Gopher] John Jumper: AI is revolutionizing scientific discov...
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John Jumper: AI is revolutionizing scientific discovery [video]
Author : sandslash
Score : 42 points
Date : 2025-09-29 15:20 UTC (7 hours ago)
(HTM) web link (www.youtube.com)
(TXT) w3m dump (www.youtube.com)
| malux85 wrote:
| First jump that computers gave us : speed. With excess of speed
| came the ability to brute force many problems.
|
| Next jump given by AI (not LLMs specifically, I mean "machine
| learned systems" in general) is navigation. Even with large
| amounts of speed some problems are still impractically large, we
| are using AI to better explore that space, by navigating it
| smarter, rather than just speeding through it combinatorially.
| whatever1 wrote:
| No evidence so far that "AI" has improved our general
| optimization capabilities. At all.
|
| Still at the top of the benchmarks of integer optimization by
| huge margin are the traditional usual suspects. Same in
| constraint programming and SAT.
| lomase wrote:
| If you only know how to use a hammer, everything looks like a
| nail.
| hodgehog11 wrote:
| Here is some evidence for you then:
| https://arxiv.org/abs/2411.00566
|
| Not published just yet are experiments for finding solutions
| to mathematical problems traditionally found with SAT
| solvers, at much larger scale than was previously possible.
| bgwalter wrote:
| Google DeepMind Director John Jumper. Literally no one who is not
| connected to the "AI" industrial complex praises "AI". In any
| video or blog post there is a link.
| baxtr wrote:
| Follow the money...
| layoric wrote:
| Thank you for this context, should be the title IMO..
| emil-lp wrote:
| I'm a researcher in AI and I haven't met anyone who has gotten
| substantial help from AI.
|
| Many people have tried, many people have been let down.
| some_guy_nobel wrote:
| NVIDIA published the Illustrated Evo2 a few days ago, walking
| through the architecture of their genetics foundation model:
|
| https://research.nvidia.com/labs/dbr/blog/illustrated-evo2/
|
| It's nice to see more and more labs using ai for drug discovery,
| something truly net positive for society.
| the__alchemist wrote:
| I am reposting something along the lines of a flagged and dead
| comment: This would be lend more credibility to the premise AI is
| revolutionizing scientific discovery if it came from someone
| who's Nobel (or work in general) were in a non-AI-centered
| domain. This is not a critique of his speech or points, but I
| think the lead implied by the (especially Youtube) title would
| hit harder if it came from someone whose work wasn't AI-centered.
|
| Jumper's work is the poster child of AI success in science; this
| isn't about a new domain being revolutionized by it.
|
| I will throw out an idea I've been thinking about recently about
| a far less ambitious idea, but related: Amber (MD package)
| provides Force Field names and partial charges for a number of
| small organic molecules in their GeoStd set. I believe these come
| from its Antechamber program. Would it be possible to infer
| useful FF name and Partial charge for arbitrary organic molecules
| using AI instead, trained on the GeoStd set data?
| jgalt212 wrote:
| I see this sort of work as a natural extension of Combinatorial
| Chemistry or bootstrapping and Monte Carlo methods in stats.
|
| https://en.wikipedia.org/wiki/Combinatorial_chemistry
| epolanski wrote:
| I'll share something as a former solar researcher.
|
| Scientific progress is heavily influenced by how many bodies you
| can throw at a problem.
|
| The more experiments you can run, with more variety and angles
| the more data you can get, the higher the likelihood of a
| breakthrough.
|
| Several huge scientist are famous not because they are geniuses,
| but because they are great fundraisers and can have 20/30/50
| bodies to throw at problems every year.
|
| This is true in virtually any experimental field.
|
| If LLMs can be de facto another body then scientific progress is
| going to sky rocket.
|
| Robots also tend to be more precise than humans and could
| possibly lead to better replication.
|
| But given that LLMs cannot interact with the real world I don't
| see that happening anytime soon.
| bonoboTP wrote:
| > But given that LLMs cannot interact with the real world
|
| What type of interaction do you envision? Could a non-domain-
| expert, but somewhat trained person provide a bridge? If the
| LLM comes up with the big ideas and tells a human technical
| assistant to execute (put the vial here, run the 3D printer
| with this file, put the object there, drive in a screw), would
| that help? But dexterous robots are getting more and more
| advanced, see CoRL demos right now.
| NedF wrote:
| Awful title, great video.
|
| Three points jumped out
|
| 1) "really when you look at these machine learning breakthroughs
| they're probably fewer people than you imagine"
|
| In a world of idiots, few people can do great things.
|
| 2) External benchmarks forced people upstream to improve
|
| We need more of these.
|
| 3) "the third of these ingredients research was worth a
| hundredfold of the first of these ingredients data."
|
| Available data is 0 for most things.
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