Meta’s Tribe v2 AI predicts human brain response to visuals & audio – without needing new training for unseen languages

🧠 Meta* has developed Tribe v2, an artificial intelligence model that can reliably predict how the human brain reacts to visual and auditory content. According to Meta, the model is designed for scientific purposes, aimed at advancing neuroscience research.

📊 The system was trained on fMRI data from four individuals, plus brain‑activity records from over 700 volunteers. Participants were shown images, videos, text, and listened to podcasts while their neural signals were recorded.

🔮 Tribe v2 learned to “reliably” forecast brain activity – and can even make predictions for languages that were not included in the original dataset, with no extra training. Meta emphasizes that the model’s goal is to help neuroscientists test hypotheses without involving human subjects.

#AI #Neuroscience #BrainImaging #MachineLearning #Science #NeuroscienceResearch

☀️ AI Just Learned to Control Fusion Plasma Faster Than Humans Can React

Inside a fusion reactor, plasma can become unstable in just a few milliseconds.

That is a problem when even a highly focused human operator reacts on the scale of seconds.

Researchers from Princeton University and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory have now tested an AI control framework called PACMAN on the real DIII-D tokamak in California. The system continuously reads temperatures, densities and magnetic signals, runs multiple machine-learning models, resolves their commands and sends instructions back to the machine — with a complete control cycle typically taking about 20 milliseconds.

In one of five live experiments, PACMAN predicted a dangerous tearing-mode instability about 200 milliseconds before it appeared. Instead of trying to suppress the instability after it had already formed, the controller changed the plasma early enough to prevent it. In other tests, the system controlled plasma heating, density and rotation, detected energetic-particle waves, and simultaneously optimized all six of DIII-D’s microwave heating systems.

This does not mean AI has solved fusion. DIII-D is an experimental tokamak, not a commercial power plant, and researchers still set the goals and safety limits. The important step is that machine-learning models are now fast enough to participate directly in the millisecond-by-millisecond control of a real fusion plasma rather than merely analyzing experiments afterward.

Fusion has always had a control problem: the plasma changes faster than humans can think.

Apparently, that is exactly the sort of problem AI likes.

#Fusion #AI #Physics #Tokamak #Energy #MachineLearning #Science

https://www.pppl.gov/news/2026/pacman-ai-framework-controlling-fusion-systems-safely-makes-key-decisions-milliseconds