this post was submitted on 11 Oct 2025
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[–] supersquirrel@sopuli.xyz 1 points 15 hours ago* (last edited 4 hours ago) (1 children)

I have not claimed that, I said that AI algorithms are likely to be part of our climate solutions and our ability to serve more people with less manual labour. They help to solve entirely new classes of problems and can do so far more efficiently than years of human labour.

hahaha like AI will be a part of climate solutions are you serious right now?

Y'all are incapable of understanding expertise in your domain does not make you an expert in everything else, there is no way anyone in the industry you are speaking about will listen to climatologists and environmental scientists long enough to even begin to be helpful.

You keep talking about technology, when this is really a discussion about the catastrophic myopia of the tech industry of which you are making yourself a perfect example of.

https://www.goldmansachs.com/insights/articles/how-ai-is-transforming-data-centers-and-ramping-up-power-demand

To some biologists, that approach leaves the protein folding problem incomplete. From the earliest days of structural biology, researchers hoped to learn the rules of how an amino acid string folds into a protein. With AlphaFold2, most biologists agree that the structure prediction problem is solved. However, the protein folding problem is not. “Right now, you just have this black box that can somehow tell you the folded states, but not actually how you get there,” Zhong said.

“It’s not solved the way a scientist would solve it,” said Littman, the Brown University computer scientist.

This might sound like “semantic quibbling,” said George Rose, the biophysics professor emeritus at Johns Hopkins. “But of course it isn’t.” AlphaFold2 can recognize patterns in how a given amino acid sequence might fold up based on its analysis of hundreds of thousands of protein structures. But it can’t tell scientists anything about the protein folding process.

...

AlphaFold2’s success was founded on the availability of training data — hundreds of thousands of protein structures meticulously determined by the hands of patient experimentalists. While AlphaFold3 and related algorithms have shown some success in determining the structures of molecular compounds, their accuracy lags behind that of their single-protein predecessors. That’s in part because there is significantly less training data available.

The protein folding problem was “almost a perfect example for an AI solution,” Thornton said, because the algorithm could train on hundreds of thousands of protein structures collected in a uniform way. However, the Protein Data Bank may be an unusual example of organized data sharing in biology. Without high-quality data to train algorithms, they won’t make accurate predictions.

“We got lucky,” Jumper said. “We met the problem at the time it was ready to be solved.”

https://www.quantamagazine.org/how-ai-revolutionized-protein-science-but-didnt-end-it-20240626/

However, it should be noted that due to the intrinsic nature of AI, its success is not due to conceptual advancement and has not hitherto provided new intellectual interpretive models for the scientific community. If these considerations are placed in Kuhn’s framework of scientific revolution [68], AF release is a revolution without any paradigm change. Instead of “providing model problems and solutions for a community of practitioners” [68], it is a rather effective tool for solving a fundamental scientific problem.

https://pmc.ncbi.nlm.nih.gov/articles/PMC12109453/

This is because scientists working on AI (myself included) often work backwards. Instead of identifying a problem and then trying to find a solution, we start by assuming that AI will be the solution and then looking for problems to solve. But because it’s difficult to identify open scientific challenges that can be solved using AI, this “hammer in search of a nail” style of science means that researchers will often tackle problems which are suitable for using AI but which either have already been solved or don't create new scientific knowledge.

^ this is NOT the scientific method and it undermines the scientific integrity of the entire process

https://www.understandingai.org/p/i-got-fooled-by-ai-for-science-hypeheres

https://www.scilifelab.se/news/alphafold3-early-pain-points-overshadow-potential-promise/

https://www.reddit.com/r/biotech/comments/1d1096g/ai_for_drug_discovery/

https://www.reddit.com/r/Biochemistry/comments/1gui8n8/what_can_alphafold_teach_us_about_the_impact_of/

https://www.reddit.com/r/Biochemistry/comments/1j47wqy/thoughts_on_the_recent_veritasium_video_about/

https://www.reddit.com/r/labrats/comments/1b1l68p/people_are_overestimating_alphafold_and_its_a/

[–] masterspace@lemmy.ca -1 points 13 hours ago* (last edited 13 hours ago)

You keep saying y'all and it's telling.

Learn how to communicate with people, not the simplified boxes you put them in.

When you're ready to have a conversation instead of just hearing yourself regurgitate mindless internet grandstanding I'm here.