A self-supervised vision transformer model by Meta AI
Meta’s new AI glasses could have a ‘super-sensing’ mode with facial recognition
Facial recognition on your face.
Meta Sued Over Training Data for Its AI and Face-Recognition Systems
The proposed class action alleges Meta illegally harvested people’s Facebook and Instagram photos to train its AI image-generation models and to build its unreleased “NameTag” face recognition feature.
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli
Understanding how the human brain processes the world around us is one of the greatest open challenges in neuroscience. Breakthroughs here could transform how we understand and treat neurological conditions affecting hundreds of millions of people — and improve AI systems by directly guiding their development from neuroscientific principles.
Introducing TRIBE v2: AI Model Predicts Human Brain Responses | AI at Meta posted on the topic | LinkedIn
Today we're introducing TRIBE v2, a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity. It enables zero-shot predictions for new subjects, languages, and tasks, consistently outperforming standard modeling approaches. We’re releasing the model, codebase, paper, and an interactive demo to help researchers advance neuroscience, apply brain insights to build better AI, and use computational simulation to speed up breakthroughs in neurological disease diagnosis and treatment. Try the demo and learn more here: https://go.meta.me/tribe2 | 175 comments on LinkedIn
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🚨 We're very happy to introduce TRIBE v2: a foundation model of the human brain's responses to sight, sound, and language. Leveraging 1,000+ hours of fMRI across 720 subjects, it generalizes… | Stéphane d'Ascoli | 16 comments
🚨 We're very happy to introduce TRIBE v2: a foundation model of the human brain's responses to sight, sound, and language. Leveraging 1,000+ hours of fMRI across 720 subjects, it generalizes zero-shot to new stimuli, tasks and people, finetunes efficiently, and enables in-silico experiments. ❓How does it work? Stemming from our v1, which won the Algonauts 2025 challenge, TRIBE v2 combines video, audio, and language embeddings to predict brain activity for any brain, then adapts to each individual. Key results: 📊 High-quality predictions — TRIBE v2 predicts brain activity across cortical and subcortical regions, significantly better than standard linear models, with a log-linear scaling law and no plateau in sight. 🎯 Zero-shot generalization — Without retraining, the predictions of TRIBE v2 are more correlated with group-averaged brain responses than almost any individual fMRI scan! A short finetuning step vastly improves over linear models trained, from scratch, on each individual. 🧪 In-silico experiments — Can we do useful experiments with TRIBE v2? Yes: classic vision and language paradigms replicate in-silico. It zero-shot recovers the FFA, PPA, EBA, VWFA, Broca's lateralization, and syntactic responses in STG — all without training on these artificial tasks. 🔍 Interpretability & multimodality — ICA on the weights rediscovers known functional networks (auditory, language, motion, default mode, visual) from naturalistic data alone. Ablating modalities further maps how vision, audition, and language integrate, with the largest gains at the temporo-parietal-occipital junction. 🧠 This effort is a step toward a foundation model of the human brain. Much remains to be understood, but we hope this opens a path for neuroscience, AI, and medical research alike. All code, weights, and a live demo are open — find it useful or mistaken in some conditions? Let us know, new test cases can only help improving this effort. 📄 Paper: https://lnkd.in/e7cbunJp 💻 Code: https://lnkd.in/ebwBVuJp ▶️ Demo: https://lnkd.in/eEUVxP4S 🤗 Model: https://lnkd.in/e2T8nPJP Joint work with Jérémy RAPIN, Yohann Benchetrit, Teon Brooks, Katie Begany, Joséphine Raugel, Hubert Banville and Jean-Rémi King. 🙏 Special thanks to Elisa Cascardi, Diego Marcos, Dominic Giardini, AI at Meta, and the open-source and neuroscience communities (in particular Lune Bellec and Bertrand Thirion for the amazing Courtois NeuroMod and IBC datasets) | 16 comments on LinkedIn
Supercharge your Computer Vision models with the TensorFlow Object Detection API
Posted by Jonathan Huang, Research Scientist and Vivek Rathod, Software Engineer (Cross-posted on the Google Open Source Blog) At Google, we develo...
MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations
Humans have the capacity to question what we see and to recognize when our vision is unreliable (e.g., when we realize that we are experiencing a visual illusion). Inspired by this capacity, we present MetaCOG: a hierarchical probabilistic model that can be attached to a neural object detector to monitor its outputs and determine their reliability. MetaCOG achieves this by learning a probabilistic model of the object detector's performance via Bayesian inference -- i.e., a meta-cognitive representation of the network's propensity to hallucinate or miss different object categories. Given a set of video frames processed by an object detector, MetaCOG performs joint inference over the underlying 3D scene and the detector's performance, grounding inference on a basic assumption of object permanence. Paired with three neural object detectors, we show that MetaCOG accurately recovers each detector's performance parameters and improves the overall system's accuracy. We additionally show that MetaCOG is robust to varying levels of error in object detector outputs, showing proof-of-concept for a novel approach to the problem of detecting and correcting errors in vision systems when ground-truth is not available.
Mistral Large 4 - Mistral AI
Mistral Large 4 is a state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture. It features 52B active parameters and 1.05T total parameters, and a 1.6B vision encoder.
Meta plans to add facial recognition to its smart glasses, report claims | TechCrunch
The feature, internally known as “Name Tag,” would allow smart glasses wearers to identify people and get information about them via Meta's AI assistant.
Meta Tapped a Pentagon Supplier to Prototype Face Recognition for Its Glasses
Rank One, whose board includes a former CIA deputy director and a former FBI science chief, supplied face recognition to Meta for internal development of its smart glasses app.
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Import AI 455: AI systems are about to start building themselves.
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Muse: Meta's personal AI agent, features & capabilities
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transformers/src/transformers/models/auto/tokenization_auto.py at 1d5296f2de31ecee4fdd789dc2b2fe068b320284 · huggingface/transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - huggingface/transformers