AI Research

Prototypical Contrastive Learning unifies clustering and contrastive self-supervised learning to push the frontiers of unsupervised learning.

We show that deep neural models can describe common sense physics in a valid and sufficient way that is also generalizable. Our ESPRIT framework is trained on a new dataset with physics simulations and descriptions that we collected and have open-sourced.

In this work, we focus on the opportunity to use AI to promote social welfare through the design of optimal tax policies in dynamic economies.

In our study, we demonstrate that an artificial intelligence (AI) model can learn the language of biology in order to generate proteins in a controllable fashion.

We are proud and excited to announce this year’s winners of the Salesforce Research Deep Learning Grant.

Our graph-based trainable retriever-reader framework retrieves evidence paragraphs from Wikipedia to answer open-domain questions. We show state-of-the-art performance on HotpotQA, SQuAD Open, and Natural Questions Open without any architectural changes.

Learn about the ethical implications of voice for business and how to make them an operational and strategic priority now—before you’re too far down the path.

Large-scale language models show promising text generation capabilities, but users cannot control their generated content, style or train them for multiple supervised language generation tasks.