I'm not at all anti-AI, but I'm still holding my breath to see real scientific contributions coming from modern (LLM/CoT/agent) AI. Much of the premise behind "AI will make superabundance a reality" hinges on getting AI-driven science that affects everyday lives Anthropic's quasi-CRISPR-like discovery did not seem super compelling, and that is still the best I have seen. If anyone has more heartening stories that could convince me to be an optimist, please do share ;)
criemen 17 minutes ago [-]
Is math or theoretical Computer Science not "real science"? Whatever one may think about the process, the results are real and are coming from LLMs.
Lichtso 12 minutes ago [-]
It is.
What I think the parent post is missing is that breakthroughs in science don't immediately translate into breakthroughs in engineering and even less so into economically viable products.
parineum 6 minutes ago [-]
I'd expect that process to be accelerated by AI more than the process of novel discovery.
ajkjk 10 minutes ago [-]
well they don't affect anyone's day-to-day lives, which is what the person you're replying to is talking about..
fallingbananna 9 minutes ago [-]
I have a feeling that many more fields are utilizing AI and having some success with it and we just don't know about it in our STEM bubble.
On the other hand, you could downplay the math findings a bit, since it didn't further our understanding of mathematics just yet, only provided proves to things we didn't have proves for.
spwa4 1 minutes ago [-]
AI has an incredibly long history of contributing to science and engineering. Some highlights:
Gerard t' Hoofd (under Martinus Veldman) proved in 1971 that Yang-Mills gauge theories are renormalizable, using a rule-based AI (LISP-styled but manually written in assembly) called "Schoonschip" (I'm guessing called that because there were several failed attempts that went before and failed). This program was an AI in the sense of a search algorithm searching symbolic proofs. It got the 1999 Nobel prize in Physics.
This was hugely important because first, it showed that it was possible to make the Standard Model of physics and it also helped beyond that by showing for a lot of (at that point considered) impossible theories exactly how to make them work.
Hao Wang proved that the logic of "Principia Mathematica" was actually correct using another symbolic proof generator. His efforts were also the start of many other such programs that contributed lots of stuff to mathematics that nobody sane was ever going to do, such as finding all the higher-order versions of the quadratic formula (there's a lot of them).
At Stanford university, tens of thousands of molecules were found in 1965 by having an AI, this one was what's called an "expert system", evaluate mass spectroscopy data and combine it with chemistry knowledge in the form of rules about what was possible or not possible and if only one option remained, spit it out. It was later adapted to make mass spectroscopy useful in finding the composition of samples.
Then there's Alphafold, first a ConvNet (Resnet+Convnet to be exact), then switched to the transformer architecture, which gave use the folding of just about every protein we have ever found, and even more proteins that we've never even found. Nobel prize in Chemistry 2024.
nzeid 18 minutes ago [-]
This post so perfectly captures the tragedy of how the term AI has lost its meaning to LLMs.
bananaflag 3 minutes ago [-]
Because LLMs are the only thing that can be said to pass in some sense the Turing test, which is like the benchmark for AI.
andy99 12 minutes ago [-]
There’s been a tendency since well before LLMs for AI demos to focus on “here’s a new problem we made up that it solves that you could imagine will soon transfer to an existing real problem” as opposed to “AI solves X”. This has been an ongoing challenge, it does cool stuff and looks so promising but has been very hard to harness for useful work. It’s still going to take time before the hype dies down and we see what’s left.
I don’t think it’s going away, LLMs and other ML stuff will no doubt remain part of people’s workflow, but I also see no evidence for superabundance or anything close to it.
havaianaslife 1 hours ago [-]
I hope European university networks and their underfunded labs will join forces as soon as possible with other European national research centers.
sandermvanvliet 37 minutes ago [-]
I’d be surprised if that isn’t already the case with CERN itself already being a multi-national collaboration project and with things like GÉANT https://geant.org/ existing
timbaboon 38 minutes ago [-]
Man, when I was doing my PhD at CERN the most far out thing that we were generally allowed to use was boosted decision trees :) getting anything other than that approved for any Higgs-related analysis was… not a thing.
blueplanet200 5 minutes ago [-]
Using neural nets for signal background separation was pretty standard in ~2012 when the Higgs discovery was published. Was also pretty standard for CDF + DZero papers that came out before the LHC turned on.
https://arxiv.org/abs/1207.7214 - The original ATLAS Higgs discovery paper uses neural nets to identify photons. It also uses a neural net b-tagger to improve the H->WW signal to noise ratio.
You needed approval to try different methods for data analysis?
27 minutes ago [-]
trilogic 1 hours ago [-]
Great news, let them cook.
4849599595 1 hours ago [-]
[flagged]
chews 58 minutes ago [-]
Wonderful news to not see the duopoly accelerating real science.
AndrewKemendo 46 minutes ago [-]
This is great progress in fundamental research and I’m thrilled to see Mistral leading it.
Makes perfect sense and I’m glad that CERN is focusing on EU providers
parineum 8 minutes ago [-]
It makes sense in a political way but I'm not sure if it's the best choice for scientific development seeing as Mistral has been pretty behind the curve it's entire existence.
It makes more sense if they're the only company who was willing to work on a more custom solution for the proposed LHC model but I don't that a bit as it would be great PR for the frontier labs.
This seems a lot more like an effort to bolster European AI by moving CERN money into Mistral.
swingandamiss 44 minutes ago [-]
Wait, so we like AI now?
setopt 27 minutes ago [-]
AI != GPT
zamadatix 30 minutes ago [-]
Some people do, some people don't. Some it depends on what for and how it's used as well, as well as what kind.
sheikhnbake 35 minutes ago [-]
AI has been beneficial in multiple research fields across maths and science. That is to say, it's actually benefited humanity in those particular use cases.
As opposed to enshitifying the lives of the working class in most other areas.
Legend2440 30 minutes ago [-]
I think there is a large group of commenters who actually just hate silicon valley tech companies, and they're fine with AI as long as it comes from someone else.
goolz 11 minutes ago [-]
Poetic considering the locale. But ya, modern tech is pretty gross, and I yearn for the open models.
kaffekaka 26 minutes ago [-]
1. Yes. 2. Maybe
x187463 35 minutes ago [-]
Using AI as a tool to investigate the mysteries of the universe is a huge win for humanity. Generating slop images to confuse people online is not. AI is tool, we like when tools are used for good.
malux85 39 minutes ago [-]
Maturity is understanding there's an enormous range between "like" and "dislike", between "love" and "hate", between "good" and "bad", and its also possible to be neither at once or both at once.
anupj 10 minutes ago [-]
[dead]
tonyhart7 46 minutes ago [-]
hope we get ethical use of AI unlike OpenAI shenanigan
Rendered at 17:44:32 GMT+0000 (Coordinated Universal Time) with Vercel.
What I think the parent post is missing is that breakthroughs in science don't immediately translate into breakthroughs in engineering and even less so into economically viable products.
On the other hand, you could downplay the math findings a bit, since it didn't further our understanding of mathematics just yet, only provided proves to things we didn't have proves for.
Gerard t' Hoofd (under Martinus Veldman) proved in 1971 that Yang-Mills gauge theories are renormalizable, using a rule-based AI (LISP-styled but manually written in assembly) called "Schoonschip" (I'm guessing called that because there were several failed attempts that went before and failed). This program was an AI in the sense of a search algorithm searching symbolic proofs. It got the 1999 Nobel prize in Physics.
This was hugely important because first, it showed that it was possible to make the Standard Model of physics and it also helped beyond that by showing for a lot of (at that point considered) impossible theories exactly how to make them work.
Hao Wang proved that the logic of "Principia Mathematica" was actually correct using another symbolic proof generator. His efforts were also the start of many other such programs that contributed lots of stuff to mathematics that nobody sane was ever going to do, such as finding all the higher-order versions of the quadratic formula (there's a lot of them).
At Stanford university, tens of thousands of molecules were found in 1965 by having an AI, this one was what's called an "expert system", evaluate mass spectroscopy data and combine it with chemistry knowledge in the form of rules about what was possible or not possible and if only one option remained, spit it out. It was later adapted to make mass spectroscopy useful in finding the composition of samples.
Then there's Alphafold, first a ConvNet (Resnet+Convnet to be exact), then switched to the transformer architecture, which gave use the folding of just about every protein we have ever found, and even more proteins that we've never even found. Nobel prize in Chemistry 2024.
I don’t think it’s going away, LLMs and other ML stuff will no doubt remain part of people’s workflow, but I also see no evidence for superabundance or anything close to it.
https://arxiv.org/abs/1207.7214 - The original ATLAS Higgs discovery paper uses neural nets to identify photons. It also uses a neural net b-tagger to improve the H->WW signal to noise ratio.
https://arxiv.org/abs/1303.0763 - used neural nets extra tt+jets noise https://arxiv.org/abs/1503.05066 - used neural nets for signal/background separation.
Makes perfect sense and I’m glad that CERN is focusing on EU providers
It makes more sense if they're the only company who was willing to work on a more custom solution for the proposed LHC model but I don't that a bit as it would be great PR for the frontier labs.
This seems a lot more like an effort to bolster European AI by moving CERN money into Mistral.
As opposed to enshitifying the lives of the working class in most other areas.