Google decided to set up a new AI research lab in France, and Facebook announced a €10M investment in FAIR-Paris, Facebook’s European AI research lab. Is France catching up to Canada?

Yann LeCun, the head of Facebook’s internal artificial intelligence research division, is stepping down from his role to take on a more dedicated research position as chief AI scientist. He will be handing his position over to Jérôme Pesenti, who ran British AI startup Benevolent and prior to that was the chief technology officer of IBM’s Big Data group.

Researchers in the emerging field of “neuromorphic computing” have attempted to design computer chips that work like the human brain. Instead of carrying out computations based on binary, on/off signaling, like digital chips do today.

Most notably, “Eager Execution” for TensorFlow is now available as a preview. This allows you can execute TensorFlow operations immediately as they are called from Python.

An in-depth walkthrough of Faster R-CNN, starting with a higher level overview, and then going over the details for each of the components.

The codebase contains a replica of the AlphaZero methodology, built in Python and Keras. Gain a deeper understanding of how AlphaZero works and adapt the code to plug in new games.

Focused on, but not limited to, medical AI, this post answers the question of what performance testing is for, and what the features of a good performance metric are.

Detectron is Facebook AI Research’s software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

Psychlab allows you to apply methods from fields like cognitive psychology to study behaviors of artificial agents in a controlled environment. You can read the research paper here.

A PyTorch implementation of Trust Region Policy Optimization (TRPO). In contrast to another implementation of TRPO in PyTorch, this implementation uses exact Hessian-vector product instead of finite differences approximation.

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab. Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial agents. It has a simple and flexible API that enables users to easily create their own tasks.

The authors formulate algorithmic fairness as constrained optimization: the objective is to maximize public safety while satisfying formal fairness constraints designed to reduce racial disparities. The authors focus on algorithms for pretrial release decisions, but the principles apply to other domains, and also to human decision makers carrying out structured decision rules.

Opponent-Learning Awareness (LOLA) is a method that reasons about the anticipated learning of the other agents. The LOLA learning rule includes an additional term that accounts for the impact of the agent’s policy on the anticipated parameter update of the other agents. Preliminary results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners’ dilemma (IPD), while independent learning does not.