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AUTHORS: Sharvin Shah, Jin Qu, Donald Rose TL;DR: TaiChi is an open source library for few-shot NLP, designed for data scientists and software engineers who want to get some quick results or build proof-of-concept products but don’t have much experience with few-shot learning (FSL). The library abstracts complex

AUTHORS: Huan Wang, Aadyot Bhatnagar, Doyen Sahoo, Wenzhuo Yang, Steven Hoi, Caiming Xiong, Donald Rose TL;DR: Time series data is a critical source of insights for many applications, including IT Operations, Quality Management, Financial Analytics, and Inventory & Sales Management. While a variety of dedicated packages and software exist, engineers

TL;DR: The AI Economist, a reinforcement learning (RL) system, learns dynamic tax policies that optimize equality along with productivity in simulated economies, outperforming alternative tax systems. We have now expanded this research, which is being published in the interdisciplinary scientific journal Science Advances. Humans or AI: Which Can Design

Conference Overview This year marks the Tenth International Conference on Learning Representations ( ICLR [https://iclr.cc/Conferences/2022]), one of the premier academic conferences dedicated to advancing research in representation learning - a type of machine learning also referred to as feature learning or deep learning. ICLR features the latest

tldr: WarpDrive is an open-source framework to do multi-agent RL end-to-end on a GPU. It achieves orders of magnitude faster multi-agent RL training with 2000 environments and 1000 agents in a simple Tag environment. WarpDrive provides lightweight tools and workflow objects to build your own fast RL workflows. Check out

This is my obsessively curated list of research papers and articles on ethics in AI that I have been collecting over the years. Ones in bold are those that I refer back to and found particularly useful. Let me know if I am missing your favorites.

This post is for those responsible for implementing AI systems but do not have a background in data science, statistics, or probability. The intention is to create an approachable introduction to the key concepts to identify potential bias (error) in their training data.

What kind of world do we want for ourselves, those we love, and future society? How do we are organizations, employees, customers, and members of society ensure that technology is in service of society and not the other way around?

In this article, I will focus on mechanisms for removing exclusion from your data and algorithms. Of all the interventions or actions we can take, the advancements here have the highest rate of change.

This is part one of a two-part series about how to build ethics into AI. Part one focuses on cultivating an ethical culture in your company and team, as well as being transparent within your company and externally.