Check out our recent work of benchmarking modern LLMs on the end-to-end scientific discovery tasks!! 😎👇

I love single sign on because you only have to sign on once, 8 times a day

Lots of hype around “AI scientists” but they have all been focused on the fun parts, not the hard parts. The focus has been on hypothesis generation, rather than *validation*: the expensive and grueling part. It’s like having an AI write your poetry rather than do your

Big news today: we’ve raised $500M to grow cohere, and have added some incredible new leaders to our team! We’re fortunate to build Cohere to be the world’s best choice for enterprises alongside some of the best investors, partners, and customers in the business.

How can protein representations be improved by leveraging text annotations?Nature Machine Intelligence University of Toronto "Boosting the predictive power of protein representations with a corpus of text annotations" 1. The background introduces protein language models, which are trained on vast protein

Nobody will remember how high on AIME 26 your model scored, or how many papers you got in Neurips 2027. Being generous (or not) is literally the only thing people will remember years after the last interaction with you.

The rebuttal process NeurIPS Conference was too collegial and productive, so a bunch of papers whose process ended on a positive note will be rejected.

TMLR's (Transactions on Machine Learning Research) review process: 1. is structured around clear and well-defined acceptance criteria (Is the paper sound and interesting?) 2. allows authors to revise after reviews based around these criteria 3. has no capped acceptance rate. A lot of fine science gets accepted.