OpenAI has released a corpus of 719 mathematical manuscripts, grouped into 372 families of results and generated largely by an internal, undisclosed model. The scope of the publication goes beyond a one-off demonstration, but it…
MAGIC: How the Global Assessment Is Changing Generative AI in Healthcare
Published on October 6, 2026 in Nature Medicine, the founding paper of MAGIC announces an international network designed to develop a more representative evaluation of generative AI in healthcare. Its goal is to move beyond…
With Claude, a new milestone for biological research?
On September 23, 2026, Anthropic presented the initial findings from its AI-assisted biology lab. The company claims that a set of Claude agents identified, in genomic data, a system combining a reverse transcriptase to DNA…
Recursive Self-Improvement in AI: Measurable Progress or Still a Theoretical Scenario?
Recursive self-improvement refers to the possibility that an artificial intelligence system helps create a more capable version of itself, then repeats the process. In September 2026, several studies showed that agents can already optimize their…
THOR AI: AI 400 times faster at solving century-old physics problems
Some equations stand the test of time. For over a century, physicists have had theoretical models capable of describing the behavior of atomic systems, but solving them remains out of reach in many practical cases….
OpenAI is revolutionizing search with Prism, its free AI workspace
After revolutionizing writing, coding, and conversational assistance, OpenAI is now turning its attention to another strategic pillar: scientific research. With the launch of Prism, the company is offering a free AI workspace designed for researchers,…
DeepMind's AI manages to "heat up" tumors to fight them more effectively
Artificial intelligence continues to revolutionize medical research. Google DeepMind, already known for its major breakthroughs with AlphaFold (which can predict the structure of over 200 million human proteins), has just announced a breakthrough in the…
MLE-STAR: Google’s approach to effectively structuring machine learning engineering
Drawing on its extensive experience in deploying AI at scale, Google offers a methodological approach to this challenge through the MLE-STAR framework. Designed as a synthesis of best practices in software engineering tailored to machine learning, this framework aims to structure AI projects in a more reliable, modular, and sustainable way.