Research - Meta AI

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Research

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 AI 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.

Meta AI engages in cutting-edge applied research that can improve and power new product experiences at huge scale for our community. Building on Meta AI'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.

Building new technology for the future of AR/VR

What remote and flexible work will look like at Meta

Generative AI

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Natural Language Processing

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Computer Vision

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Generative AI

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Natural Language Processing

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Computer Vision

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Explore our latest publications by research area.

Computer Vision

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

Generative AI

Creating AI systems that empower anyone to bring their imagination to life

Natural Language Processing

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

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Infrastructure

We’re building cutting-edge platforms to support and accelerate the growing demand of AI.

Highlighted projects

At Meta AI, 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

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Protein folding

ESM Metagenomic Atlas: The first view of the ‘dark matter’ of the protein universe

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No Language Left Behind

Driving inclusion through the power of translation

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CICERO

The first AI to play Diplomacy at a human level

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Research

Generating Biographies

Making Wikipedia more representational

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Research

FastMRI

Accelerating MRI scans without degrading the quality of the images

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Research

Open Catalyst Project

Finding more efficient and scalable ways to store and use renewable energy

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Research

wav2vec

Building breakthrough speech recognition with AI

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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

arXiv - 2022

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

arXiv - 2023

The Casual Conversations v2 Dataset

Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas

arXiv - 2023

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

ACM Journal on Transactions on Software Engineering and Methodology (TOSEM) - 2023

Emergence of Maps in the Memories of Blind Navigation Agents

Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari Morcos, Dhruv Batra

ICLR - 2023

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

arXiv - 2023

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Resources

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.