Penn researchers demonstrated optical signal switching using exciton-polaritons — hybrid light-matter particles — consuming roughly 4 femtojoules per operation. The breakthrough removes a key obstacle to fully optical AI inference hardware.
Google DeepMind unveiled an AI Co-Mathematician system — a multi-agent Gemini-based framework scoring 48% on FrontierMath Tier 4, the highest ever for any AI. AlphaEvolve improved lower bounds on five Ramsey numbers, including R(3,13) whose previous record had stood for 11 years.
The FDA has launched a first-of-its-kind pilot using cloud-based causal AI to shorten drug and medical device clinical trial timelines by 20–40%. FDA Chief AI Officer Jeremy Walsh says the approach could also accelerate regulatory approval timelines.
Isomorphic Labs, the AI drug discovery company founded by DeepMind's Demis Hassabis, has raised $2.1 billion in Series B funding led by Thrive Capital. The round includes Alphabet, GV, MGX, Temasek, CapitalG, and the UK Sovereign AI Fund.
UCLA researchers have identified DDL-920, the first drug to fully reproduce the effects of physical stroke rehabilitation in model mice. The findings, published in Nature Communications, could transform stroke recovery into a pharmacological option.
A new Linux kernel vulnerability called Dirtyfrag was publicly disclosed without patches or CVEs, allowing unprivileged users to gain root on all major distributions via chained kernel flaws.
ShinyHunters claims a second breach of Instructure Canvas LMS, allegedly stealing data on 231 million people across 9,000 schools and threatening to publish it by May 12.
A developer reflects on the unsettling implications of biocomputers using cultured human neurons to play DOOM — and why the usual dismissals about AI consciousness don't apply here.
IBM Research has published MAMMAL, a multi-modal model that unifies proteins, molecules, and gene data. It achieves state-of-the-art results on 9 of 11 biological benchmarks and outperforms AlphaFold 3 on several drug-discovery tasks.
A peer-reviewed study published in Science tested OpenAI's o1 on 76 real ER triage cases and found it achieved exact or near-exact diagnoses 67% of the time, versus 55% and 50% for two attending physicians who received identical patient data.
A new study published in Science found that a state-of-the-art LLM matched or exceeded human emergency physicians in diagnostic choices, emergency triage, and next-step management decisions using real ER data and hundreds of physician comparisons. Researchers say the results call for collaborative care models, not AI replacement of doctors.
Reddit was more interested in how this map holds 10 million papers than in the screenshot itself. By combining OpenAlex, SPECTER 2, UMAP, and Voronoi partitioning, the project pushes research discovery toward spatial exploration instead of a plain search box.