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TL;DR:OmniXAI (short for Omni eXplainable AI) is designed to address many of the pain points in explaining decisions made by AI models. This open-source library aims to provide data scientists, machine learning engineers, and researchers with a one-stop Explainable AI (XAI) solution to analyze, debug, and interpret their

In our study, we show how a language model, trained simply to predict a masked (hidden) amino acid in a protein sequence, recovers high-level structural and functional properties of proteins.

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.

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.

Many NLP applications today deploy state-of-the-art deep neural networks that are essentially black-boxes. One of the goals of Explainable AI (XAI) is to have AI models reveal why and how they make their predictions so that these predictions are interpretable by a human. But work in this direction has been