Innovating with the freedom to explore, discover and apply AI at scale.
Our approach
We’re advancing AI through fundamental and applied research.
The Fundamental AI Research (FAIR) team at Meta seeks to further our fundamental understanding in both new and existing domains, covering the full spectrum of topics related to AI, with the mission of advancing the state-of-the-art of AI through open research for the benefit of all.
AI at Meta engages in cutting-edge applied research that can improve and power new product experiences at huge scale for our community. Building on AI at Meta's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on pushing the boundaries of AI to create a more connected world.
Featured research
Driven to innovate through research and community collaboration.
Generative AI
Natural language processing, computer vision.
Explore our latest publications by research area.
Creating new ways for computers to gain a higher level of understanding of the visual world around us
Core Machine Learning
Building algorithms inspired by, and compatible with human cognition
Embodied AI/Robotics
Developing embodied agents that assist and collaborate with people in virtual and physical spaces
Creating AI systems that empower anyone to bring their imagination to life
Advancing the state-of-the-art in natural language understanding and generation
Society & Responsible AI
Delivering AI research innovations and guidelines designed to help everyone benefit from AI
Speech & Audio
Creating spoken language technology to help people build community and connect with others
Infrastructure
We’re building cutting-edge platforms to support and accelerate the growing demand of AI.
Highlighted projects
At Meta, we conduct research to responsibly advance AI while understanding the impact our innovations have on the world.
Our aim is to shape and enhance an AI-driven future through our many, diverse fundamental and applied research projects.
Make-A-Video
An ai system that generates videos from text, protein folding, esm metagenomic atlas: the first view of the ‘dark matter’ of the protein universe, no language left behind, driving inclusion through the power of translation, the first ai to play diplomacy at a human level.
Generating Biographies
Making Wikipedia more representational
Accelerating MRI scans without degrading the quality of the images
Open Catalyst Project
Finding more efficient and scalable ways to store and use renewable energy
Building breakthrough speech recognition with AI
Curated publications
Learn about our latest AI breakthroughs and innovations from our library of published papers.
No Language Left Behind: Scaling Human-Centered Machine Translation
NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco (Paco) Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang
LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Guillaume Lample
The Casual Conversations v2 Dataset
Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas
ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks
Hua Qi, Zhijie Wang, Qing Guo, Jianlang Chen, Felix Juefei-Xu, Fuyuan Zhang, Lei Ma, Jianjun Zhao
Emergence of Maps in the Memories of Blind Navigation Agents
Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari Morcos, Dhruv Batra
Online Backfilling with No Regret for Large-Scale Image Retrieval
Seonguk Seo, Mustafa Gokhan Uzunbas, Bohyung Han, Sara Cao, Joena Zhang, Taipeng Tian, Ser Nam Lim
The advancement of AI should be a shared community initiative.
From research exploration to large-scale production deployments, we share our frameworks, libraries, demos, system cards, models, tools, such as PyTorch, ONNX, Glow and more.
Latest Work
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Meta Research
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Popular repositories
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Jupyter Notebook 43.6k 5.1k
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Python 29.1k 6.2k
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Python 28.5k 7.2k
A library for efficient similarity search and clustering of dense vectors.
C++ 27.7k 3.4k
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Python 26.1k 5.5k
Library for fast text representation and classification.
HTML 25.5k 4.7k
Repositories
Spot Sim2Real Infrastructure
PyTorch extensions for high performance and large scale training.
Hydra is a framework for elegantly configuring complex applications
FBPCS (Facebook Private Computation Solutions) leverages secure multi-party computation (MPC) to output aggregated data without making unencrypted, readable data available to the other party or any third parties. Facebook provides impression & opportunity data, and the advertiser provides conversion / outcome data.
Code release for "Stochastic Optimal Control Matching"
A modular high-level library to train embodied AI agents across a variety of tasks and environments.
FAIR Sequence Modeling Toolkit 2
Hackable and optimized Transformers building blocks, supporting a composable construction.
Unified Communication Collectives Library
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AI at Meta engages in cutting-edge applied research that can improve and power new product experiences at huge scale for our community. Building on AI at Meta's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on pushing the boundaries of AI to create a more connected world.
For the last decade, FAIR has been the source of many AI breakthroughs and a beacon for doing research in an open and responsible way. We are committed to open science and sharing our work, whether it be papers, code, models, demos or responsible use guides. We’ve made impressive strides in the past 10 years in object detection with Segment ...
FAIR is an open research organization that works on all aspects of AI R&D, from fundamental to applied, and publishes its results early and often. Learn about the history, key projects, and impact of FAIR on the state of the art of AI, such as face verification, video description, generative models, and more.
Meta AI. We’re advancing the state-of-the-art in Generative AI, Computer Vision, NLP, Infrastructure and other areas of AI. At Meta, research permeates everything we do. We believe the most interesting research questions are derived from real world problems.
Learn about Facebook Artificial Intelligence Research (FAIR) and its projects on deep learning, computer vision, natural language processing, and more. Explore articles, videos, publications, and events related to AI research at Meta.
Facebook AI Research
Meta introduces the AI Research SuperCluster (RSC), a powerful supercomputer that will accelerate AI research and help build for the metaverse. RSC will enable Meta's AI researchers to develop better AI models, work across languages, analyze multimodal signals and more.
Meta Research
Explore the open source repositories of Meta Research, the AI research arm of Facebook. Find code, models, and notebooks for various AI tasks, such as vision, language, and reinforcement learning.