“Many discoveries that have revolutionized biology and medicine started with a scientist noticing something odd in the staggering diversity of molecular machines found in nature. Restriction enzymes, proteins that cut DNA at specific short sequences, were found in bacterial immune systems, where they destroy the DNA of invading viruses. Researchers realized they could use these enzymes to cut DNA at chosen places and splice genes from one organism into another, which launched the biotechnology industry. Taq polymerase, an enzyme that copies DNA at high temperatures, was identified in a bacterium in a Yellowstone hot spring. It became the basis for PCR, the DNA-copying method used in much of modern diagnostics. CRISPR was first noticed as an unusual repeat sequence in the DNA of certain bacteria, and is now the foundation of gene editing-based medicines.

In the spring of 2026, we formed a research group to see whether general AI models can systematize and accelerate such discoveries. We believe that this acceleration will come from establishing a new way of doing biology research, in which agents collaborate with humans in every step of the process. Developing this new way of working required that we build our own lab and a single team working on everything from training Claude in biology to running experiments in the lab.

Today, we’re sharing early results from one of our first research programs, in which Claude autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR. Although we don’t yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems, all of which are programmable and perform operations like cutting, copying, and pasting DNA. Beyond CRISPR, which has already transformed science and medicine, several other such systems are now in development as promising tools.

The system that Claude found is based on a reverse transcriptase (RT), enzymes that copy RNA into DNA. While this underlying RT, found in a jumbo phage, had been identified in previous studies, Claude appears to be the first to notice the system’s defining features—an associated array of non-coding DNA sequences and an additional accessory protein of unknown function.”

From Anthropic.

“As AI tools become better and easier to use, having clear context about the content you encounter online helps you make informed decisions about what to trust. Since launching Google SynthID in 2023, we've helped people identify AI-generated media using imperceptible watermarks across images, video, and audio. To date, we've watermarked over 180 billion images and videos, along with 240,000 years of audio content.

Last year, we introduced an early version of the SynthID Detector to help media professionals verify AI-generated content. Now, we’re expanding access to everyone – with the tool available globally in English starting today.

With the new SynthID Detector, anyone can easily check if an image, video, or audio file was made with AI from Google or our partners, including OpenAI, NVIDIA, Kakao, and soon, Apple.”

From The Keyword.

“On Tuesday night, OpenAI published hundreds of proofs for mathematicians to wrap their minds around.

An unprecedented number of answers that had eluded mathematicians for decades arrived in a single night. The deluge included AI solutions to more than 300 problems across number theory, algebraic geometry and other fields with names that could give anyone haunted by high-school math a migraine. Among the major findings in a collection of 722 papers were advances related to three of the five remaining Millennium Prize Problems, including the Riemann hypothesis.

'If a human did this, it would be an instant Fields Medal, no questions asked,' Alex Kontorovich, a Rutgers University distinguished math professor, wrote on X about the 'quasi-Riemann hypothesis' proof.

The understated rollout of such monumental results marked a shift in OpenAI’s approach to publishing scientific discoveries, part of an effort to win back mathematicians who have become allergic to AI hype in recent months. On late Tuesday and early Wednesday, many of them reported feeling a sense of awe.

As stunned math experts scrolled through the results and began to absorb them, even a researcher inside Anthropic called the release 'obviously the most significant moment in mathematical history.'”

From Wall Street Journal.

“Using advances in machine-learning, researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI that defeated top-ranked human players of the board wargame Stratego by a large margin — something no AI system had been able to achieve.

Stratego, a two-player game of imperfect information, in which the opponent’s piece identities remain hidden, is often used as a benchmark to test the strategic thinking abilities of powerful AI models.

To build their model, the researchers combined efficient training algorithms with new techniques tailored for calculated decision-making in hidden information settings.

The AI system achieved greater performance at Stratego than the next best models, while being far cheaper and less computationally demanding to train. The system also outperformed top human players in other strategic games with different rules and designs, demonstrating how it can be generalized for a variety of use-cases…

Past efforts, such as Google’s DeepMind, relied on sophisticated operations that were computationally demanding and costly. But even with millions of dollars in training costs, these models were still not strong enough to beat top human Stratego players.”

From MIT News.