links 1/3/2025: https://roamresearch.com/#/app/srcpublic/page/01-03-2025

• https://thisgenomiclife.substack.com/p/this-weeks-finds-in-genomics-and-4bd "More than half of [a new dataset of 2.35 million candidate regulatory DNA] elements are not close to the transcription start sites of genes, meaning they act at a distance to control gene expression"
• https://www.ams.org/notices/202501/rnoti-p6.pdf Terence Tao on machine-assisted proof
• https://www.cato.org/blog/national-security-hoax Joe Biden blocked a Japanese company's acquisition of U.S. Steel on "national security" grounds...even though Japan is a US ally and this would be an investment into America-based steelmaking facilities
• https://marginalrevolution.com/marginalrevolution/2024/12/the-future-of-the-scientist-in-a-world-with-advanced-ai.html Tyler Cowen thinks that all the hypothesis generation in science will be done by AI and humans will only gather data (via lab work or confidentiality/data access negotiations.). This is...pretty backwards from what I expect the best uses of AI vs. creative humans are. I'm much more interested in AIs for lab automation and faster idea-generation/lit-review loops.
• https://kyunghyuncho.me/i-sensed-anxiety-and-frustration-at-neurips24/  interesting perspective. as we're entering the "productization" phase of AI, says the article, the days of PhDs getting fat industry salaries to do research are ending.
  • this...does not match my experience, though maybe I was seeing a different side of the elephant. In the mid-2010s, ML people knew that "deep learning" was the best at the benchmarks, but it was fiendishly hard to get business results from it in most contexts, so startups would typically aspire to use it and then...quietly not. in my corner of startupland, machine learning PhDs were definitely not being paid to do freeform research; these were the days of "feature engineering is king" and "data munging" and "let's just use a logistic regression, it works better"
  • article's probably right that it's not a great time to be in non-LLM fields of ML, though there are exceptions
• https://www.nature.com/articles/s41562-021-01286-3.epdf?sharing_token=FmzVoOJu5aSIdLKxYEmbs9RgN0jAjWel9jnR3ZoTv0Mqd2apSCbVNCoLnGZzPpZgrhSB6F3n6W7UifVpls202s_RwJ4kTaNjSgIQzVnS6hGeSq41cU3dWAYR23ygDu7de3fh4_6F6XJA2pD3xNO3c8hyjtnc_kr6GV5YDuwIdFQ%3D  it's very easy for grant funding schemes to be net negative in societal value.
  • "if the cumulative work that goes into an average grant application adds up to considerably more than a couple of days, these grant schemes draw more resources from the scientific community than they add." in reference to two (actually existing) grant programs awarding €50,000 and €30,000 with success rates of 5% and 2.5%, respectively.
  • in other words: if grants are small, selective, and time-consuming, they're using up more scientist-hours on grant applications than they are funding scientist-hours of research.
• https://poetryarchive.org/poem/fiddler-dooney/ one of my favorites
• https://www.feraleyes.xyz/p/god-written-by-a-girl personal essay, I don't totally understand but it's heartfelt
• https://viaseparations.com/wp-content/uploads/2024/12/Via-Separations-White-Paper-Dec.-2024.pdf  Via Separations produces membrane separations for industry that replaces (combustion-powered) evaporators, using 75% less energy without yield loss and with favorable economics without subsidies.

• https://mathstodon.xyz/@tao/113721192051328193 Terence Tao on getting his papers declined; it happens to him about once a year. rejections are not unusual in math journals and even good mathematicians get them.