4 ms·

If we truly need LLMs to be research assistants, then I question, are we really still doing useful things or just "playing the game" of research? I mean, if we

If we truly need LLMs to be research assistants, then I question, are we really still doing useful things or just "playing the game" of research? I mean, if we need datacenters and models that cost millions to train and megawatts to run, is what comes out of it any use for us? Scientific research has come to resemble gambling more and more these days, where there is an extremely obsessive quest to accumulate more data, theories, and information, rather than trying to figure out improvements to life.

falcor84 3y ago
I'm not following your argument at all. Yes, there are diminishing returns in science as in everything, but generally speaking, all other things being equal, the more resources you put into an endeavor, the more you get out of it. One big example from recent years is AlphaFold, which required massive computation resources, and has since its release been an ongoing fountain of innovation for biomedical (and particularly pharmacological) application.
- hesiintle 3y ago
  > has since its release been an ongoing fountain of innovation for biomedical (and particularly pharmacological) application. [Citation Needed] Last time I looked into it, as is often the case, the ‘actual’ results were much much more sobering than the headlines seemed to suggest.
  - falcor84 3y ago
    Yes, the actual results are definitely not as impressive as the overly hyped headlines, but there's still a lot. First off, in terms of research building up on top of it, as of today, Pubmed shows 9,364 articles citing their 2021 paper, and Google Scholar shows 21,719 results as a whole[1], but these include non-biomedical papers (e.g. applications of similar ML models to other disciplines). As for actual applications, it's a bit early, but here's a relatively extensive review from 2023[2], and there's a particularly notable example showing progress on liver cancer [3]. Researchers are still figuring out how to utilize it, e.g. with search mechanisms like Foldseek[4], and I'm sure we'll see a lot over the coming years. But you're definitely right it's not a silver bullet on its own, and there's still need for actual scientific research. On this note there's this interesting paper on the "illusion of understanding" we get from working with AI. [0] https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_citedin&from_uid=34265844 https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_cite... [1] https://scholar.google.com/scholar?cites=6286436358625670901&as_sdt=2005&sciodt=0,5&hl=en https://scholar.google.com/scholar?cites=6286436358625670901... [2] https://www.nature.com/articles/s41392-023-01381-z https://www.nature.com/articles/s41392-023-01381-z [3] https://www.genengnews.com/insights/first-application-of-alphafold-in-identifying-potential-liver-cancer-drug/ https://www.genengnews.com/insights/first-application-of-alp... (paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/) [4] https://www.nature.com/articles/s41587-023-01773-0 https://www.nature.com/articles/s41587-023-01773-0 [5] https://www.nature.com/articles/s41586-024-07146-0 https://www.nature.com/articles/s41586-024-07146-0
canadianfella 3y ago
[dead]
hesiintle 3y ago
I agree whole heartedly, except: > quest to accumulate more data, theories, and information, rather than You forgot “more money”.

falcor84 3y ago

I'm not following your argument at all. Yes, there are diminishing returns in science as in everything, but generally speaking, all other things being equal, the more resources you put into an endeavor, the more you get out of it. One big example from recent years is AlphaFold, which required massive computation resources, and has since its release been an ongoing fountain of innovation for biomedical (and particularly pharmacological) application.

hesiintle 3y ago
> has since its release been an ongoing fountain of innovation for biomedical (and particularly pharmacological) application. [Citation Needed] Last time I looked into it, as is often the case, the ‘actual’ results were much much more sobering than the headlines seemed to suggest.
- falcor84 3y ago
  Yes, the actual results are definitely not as impressive as the overly hyped headlines, but there's still a lot. First off, in terms of research building up on top of it, as of today, Pubmed shows 9,364 articles citing their 2021 paper, and Google Scholar shows 21,719 results as a whole[1], but these include non-biomedical papers (e.g. applications of similar ML models to other disciplines). As for actual applications, it's a bit early, but here's a relatively extensive review from 2023[2], and there's a particularly notable example showing progress on liver cancer [3]. Researchers are still figuring out how to utilize it, e.g. with search mechanisms like Foldseek[4], and I'm sure we'll see a lot over the coming years. But you're definitely right it's not a silver bullet on its own, and there's still need for actual scientific research. On this note there's this interesting paper on the "illusion of understanding" we get from working with AI. [0] https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_citedin&from_uid=34265844 https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_cite... [1] https://scholar.google.com/scholar?cites=6286436358625670901&as_sdt=2005&sciodt=0,5&hl=en https://scholar.google.com/scholar?cites=6286436358625670901... [2] https://www.nature.com/articles/s41392-023-01381-z https://www.nature.com/articles/s41392-023-01381-z [3] https://www.genengnews.com/insights/first-application-of-alphafold-in-identifying-potential-liver-cancer-drug/ https://www.genengnews.com/insights/first-application-of-alp... (paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/) [4] https://www.nature.com/articles/s41587-023-01773-0 https://www.nature.com/articles/s41587-023-01773-0 [5] https://www.nature.com/articles/s41586-024-07146-0 https://www.nature.com/articles/s41586-024-07146-0

canadianfella 3y ago

[dead]

hesiintle 3y ago

I agree whole heartedly, except: > quest to accumulate more data, theories, and information, rather than You forgot “more money”.