Artificial Intelligence

Advancing the field of machine intelligence

We are committed to advancing the field of machine intelligence and are creating new technologies to give people better ways to communicate. In short, to solve AI.

Facebook Artificial Intelligence researchers seek to understand and develop systems with human-level intelligence by advancing the longer-term academic problems surrounding AI. Our research covers the full spectrum of topics related to AI, and to deriving knowledge from data: theory, algorithms, applications, software infrastructure and hardware infrastructure. Long-term objectives of understanding intelligence and building intelligent machines are bold and ambitious, and we know that making significant progress towards AI can’t be done in isolation. That’s why we actively engage with the research community through publications, open source software, participation in technical conferences and workshops, and collaborations with colleagues in academia.

Facebook AI researchers work from our offices around the globe: Menlo Park, New York City, Seattle, Pittsburgh, Montreal, Paris, Tel Aviv and London.

“We have incredible people in FAIR who are making significant progress in AI, but to really move the bar it’s equally as important to be outward focused. To push the envelope, push the science and technology forward, we must be actively engaged with the research community. We publish a lot of things we do, distribute a lot of code on open-source, and engage deeply with academia to drive the progress.” Yann LeCun, VP & Chief AI Scientist

Latest Publications

All Publications

IJCAI - January 5, 2021

IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL

Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam

IEEE Transactions on Automatic Control - January 1, 2021

Asynchronous Gradient-Push

Mahmoud Assran, Michael Rabbat

COLING - December 8, 2020

Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming

Zhenpeng Zhou, Ahmad Beirami, Paul A. Crook, Pararth Shah, Rajen Subba, Alborz Geramifard

COLING - December 8, 2020

Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data

Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White

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