As a bioinformatics person that's spent time in and out of industry/academia, I agree with some of the article's thesis. While I don't think LLMs or AI are going away, I do think it will allow people in academia to pump out a bunch of inane papers and continue to prop up predatory scientific journal publishing via tenure and promotion. In fact outside of how utterly useless Fable 5 is via their aggressive guard rails for my work, I quite like using statically typed and/or functional languages with other LLMs since there are some baked in guardrails via compiler + type system.
I think the flattening of progress is the most interesting dimension to the article. For an example a useful biological product discovery with a nonlinear path to get to there, look at the Taq polymerase (https://en.wikipedia.org/wiki/Taq_polymerase). Without some NSF funded exploratory ecological research by Tom Brock in Yellowstone Hot Springs to test the theoretical limit of life at high temperatures (https://en.wikipedia.org/wiki/Thermus_aquaticus) we never get to the Taq polymerase, we never get reliable/robust PCR (https://en.wikipedia.org/wiki/Polymerase_chain_reaction), which is now a gold standard method in both clinical and environmental testing! It is rather improbable to think that large language models would associate those domain connections across the topic (molecular biotechnology + ecology + microbial physiology). I also did some exploratory work with text embedding models people might use for RAG and challenged them with an open source scientific MCA question dataset, generalist embedders performed worse vs. domain specific embedders trained on scientific corpora (doesn't surprise me at all). However, if everything regresses to the median of the universe of possible knowledge, it seems like scientific leaning frontier models would get locked into this asymptotic flattening before turning cashflow positive for model vendors OR they become so locked down that only big pharma, state actors, or big ag can afford the API rates and vetting process.
Yeah, I definitely do something similar with my personal projects.
I come from more of a hardware & environmental engineering background and we were always taught that projects were iteratively built via Design, Build, Test, Learn cycles.
I drive the Design and basic skeleton of the build (pseudocode or boilerplate), then pass off the rest of the Build and Test to the agent. I pick up after the test and read the agent commits/notes, then write up next steps. Repeat DBTL. Maybe spin a few features out at a time in parallel depending on how much time I want to devote to reviewing new project features later in the day.
I wonder if the same thing happened with--or is happening at--NSF? I know researchers that did not get funding for quantitative ecology fellowships or grants. After back channeling with program managers, it seems that using "diversity"--as in the quantitative ecological measures, metrics, or derived functional values--may have flagged proposals to be rejected.
The "lists" that were public of science grants identified as DEI absolutely indicated they were just doing a ctrl+f on diversity, women, race at times. Same went for removing people from government websites and so on.
Pretty cool bioinformatics algorithm to speed up what was traditionally a dynamic Burrows-Wheeler Transform. Interested to see where this gets implemented outside of benchmarking in the next few years!
It's somewhat interesting, but the authors' conclusions are a bit odd given their data.
They acknowledge that fame is potentially confounding:
Risk factors (impulsivity, substance use, etc.) -> Fame achievement |
Risk factors -> Early mortality
The authors also appear to conclude that fame is semi-causal of the mortality risk. If, taking a causal statistical approach, the authors conditioned on the collider:
Risk factors (substance use, personality traits, mental health vulnerabilities) -> Becoming/staying a professional singer <- Talent/drive toward fame
I do applaud them for preregistering the study, but I think this paper needed a little more rigor in peer review.
This particular design more or less can't tell us whether fame in and of itself is a risk factor. We'd need to look at a cross-section of professions, not just musicians. Do marquee leading actors die younger than character actors? Do national politicians die younger than local? Do best-selling authors die younger than struggling authors who publish but never sell anything? Do professional athletes in popular sports die younger than athletes in less popular sports?
Mechanistically, it seems pretty obvious that fame can't cause a physical health outcome. I think the authors know this and they mention that it isn't really fame per se; it's the anxiety caused by public scrutiny and high expectations, often coped with by using illegal drugs to self-medicate.
That isn't a worthless finding, but what are we supposed to take from this? I would imagine drug-using hard-partying rock stars know their lifestyle in unhealthy and dangerous, just as I am fairly certain you'd be able to produce a retrospective study showing wingsuit divers die younger than big wall rock climbers, and big wall rock climbers die younger than trail runners. Anyone doing these things knows the risk and does it anyway. It seems the effect they found is famous musicians die 4.6 years younger on average than comparable unknown musicians. If you told me I could be a rock star but I'd die at 81 instead of 85, I think I'm probably taking that. Of course, we know it doesn't actually work that way, more that a few die in their 20s, far more in their 40s and 50s, and anyone making it past that is probably dying about the same time as anyone else, but whatever the risk is, if that's the life you want, so be it.
This is a non-issue with Polars dataframes to_pandas() method. You get all the performance of Polars for cleaning large datasets, and to_pandas() gives you backwards compatibility with other libraries. However, plotnine is completely compatible with Polars dataframe objects.
> Life's two most fundamental properties are homeostasis and reproduction.
> The loss of these two combined with its parasitic nature makes this cell a form on non-life.
This is a decidedly Eukaryote-centric take. Homeostasis in higher mammals is a complex network of genes -> RNA -> proteins -> metabolic pathways
Reproduction is also far more simple in organisms with binary fission cellular division.
A more appropriate scientific term would be obligate commensalism vs. "parasitic". That actually encapsulates their need for metabolic precursors from the host, but allows for tRNA, rRNA, origin of replication, etc...present in the organism's genome.
For all the folks saying, "Isn't this just a virus?"
The actual paper states that the genome encodes transfer RNA's and ribosomal RNA's. I think that's a really important biological distinction missing from the popular press junket. The primary source material is well written and elucidates a lot more than the Quanta article. https://www.biorxiv.org/content/10.1101/2025.05.02.651781v1
I think the flattening of progress is the most interesting dimension to the article. For an example a useful biological product discovery with a nonlinear path to get to there, look at the Taq polymerase (https://en.wikipedia.org/wiki/Taq_polymerase). Without some NSF funded exploratory ecological research by Tom Brock in Yellowstone Hot Springs to test the theoretical limit of life at high temperatures (https://en.wikipedia.org/wiki/Thermus_aquaticus) we never get to the Taq polymerase, we never get reliable/robust PCR (https://en.wikipedia.org/wiki/Polymerase_chain_reaction), which is now a gold standard method in both clinical and environmental testing! It is rather improbable to think that large language models would associate those domain connections across the topic (molecular biotechnology + ecology + microbial physiology). I also did some exploratory work with text embedding models people might use for RAG and challenged them with an open source scientific MCA question dataset, generalist embedders performed worse vs. domain specific embedders trained on scientific corpora (doesn't surprise me at all). However, if everything regresses to the median of the universe of possible knowledge, it seems like scientific leaning frontier models would get locked into this asymptotic flattening before turning cashflow positive for model vendors OR they become so locked down that only big pharma, state actors, or big ag can afford the API rates and vetting process.