Something huge just landed at @ResearchHub. Introducing: Endowments. Starting today, everyone holding ResearchCoin (RSC) in their ResearchHub account will automatically earn high-yield Funding Credits, distributed daily. Funding Credits must be used to support projects on ResearchHub, turning your one-time investment into a continuous source of research funding.

GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. openai.com/index/scaling-ai-…

The results are in! Peer review relies on people giving their time. How often do we ask reviewers how the system should work for them? We’ve published the findings of the Pirevios reviewer survey, based on 200+ completed responses. Among those who responded: • 71% had considered stopping reviewing in the past year. • 58% named time demands as their main reason for declining a review. • 73% selected payment or compensation as one possible improvement. • 73% said they had never been asked how peer review should work. This was a self-selected survey, so these figures do not represent all reviewers. The full report includes detailed charts, written-response themes and methods: pirevios.com/survey-report The findings are helping shape what we’re building at Pirevios: a directory where reviewers can describe their specialities and availability, and say whether they are open to unpaid, paid or hybrid reviewing. Our aim is to help journals find suitable reviewers sooner, reduce invitations that do not fit, and give more reviewers a chance to contribute without repeatedly approaching the same people. Thank you to everyone who shared their experience. I’d welcome your thoughts on how we can make this genuinely useful for reviewers and journals. More about Pirevios: pirevios.com/ @mirvatalasnag @sbrugaletta @ncurzen @mmamas1973 @gbiondizoccai @AnilMakam @dr_yakupergun @EslamAbady33 @francisdeng @PTarantinoMD @ross_prager @LocasaleLab @DrNeilStone @drjohnm @DrDamluji @wisit661 @Hemida_MD @BrettSperryMD @docmartin22 @JadAbdelsattar @Azeem_Majeed @Dr_AmerZeidan @DrToddLee @doctorinigo @AmitGoyalMD @acorderofort @Elsayed_saidx @jtsaxon

Yes, AI is rapidly advancing mathematics (and theoretical physics problems that are essentially mathematical). Like Ramanujan’s notebooks, human mathematicians will spend months (years?) poring over the stream of papers that AI effortlessly generates, hoping to verify and understand them, probably until math itself (via Lean) becomes a branch of computer science like almost everything else. However, after math and physics, there is still a next scientific frontier: chemistry, and especially biology and medicine. I mean, it was already the frontier, but it’s the next one too. AI can crush deterministic math problems on the computer, but biomedicine is a highly empirical, noisy, multivariate, and yet often predictable domain. Thus, AI radically increases our ambitions in biomedicine. All the omics data, all the messy literature, it can all be synthesized and analyzed in a flash. This is what the labs mean when they talk about curing all diseases. The tooling is available to take cracks at biomedical problems previously thought impossible. Midjourney is doing this, Chan Zuckerberg is doing this, Anthropic is doing this, but there will be so much more.