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November 30, 2023
Today we’re celebrating the 10-year anniversary of Meta’s Fundamental AI Research (FAIR) team. During the past 10 years, the field of AI has undergone a profound transformation, and through it all, FAIR has been a source of many AI research breakthroughs, as well as a beacon for doing research in an open and responsible way.
December 04, 2023
AI Alliance Launches as an International Community of Leading Technology Developers, Researchers, and Adopters Collaborating Together to Advance Open, Safe, Responsible AI
December 07, 2023
Meta AI announces Purple Llama, a project for open trust and safety in generative AI, with tools for cybersecurity and input/output filtering.
February 15, 2024
We’re releasing the Video Joint Embedding Predictive Architecture (V-JEPA) model, a crucial step in advancing machine intelligence with a more grounded understanding of the world.
March 20, 2024
Today, we’re introducing SceneScript, a novel method for reconstructing environments and representing the layout of physical spaces.
April 11, 2024
Today, we’re introducing the Open-Vocabulary Embodied Question Answering (OpenEQA) framework—a new benchmark to measure an AI agent’s understanding of its environment.
April 15, 2024
When Seoul-based company Mathpresso set out to create a highly accurate math-specific LLM with Korean AI startup Upstage, Meta’s open-source model Llama 2 was a fitting choice.
April 18, 2024
Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model. In the coming months, we expect to share new capabilities, additional model sizes, and more.
We’ve taken responsible steps before launching Meta AI and Meta Llama 3 so people can have safer and more enjoyable experiences.
April 25, 2024
Meditron, a suite of open-source large multimodal foundation models tailored to the medical field, was built on Meta Llama 2.
May 02, 2024
The project aims to accelerate direct air capture development while significantly reducing costs.
May 07, 2024
Retrieval-Augmented Fine-Tuning (RAFT) combines the benefits of Retrieval-Augmented Generation and Supervised Fine-Tuning for better domain adaptation.
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