Do Human Gamers Stand a Chance Against Trash-Talking AI Bots?

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Discouraging Words from Machines Impair Human Game Play
A new CMU study shows that people who played a game with a humanoid robot known as Pepper performed worse when the robot discouraged them and better when it encouraged them. “This is one of the first studies of human-robot interaction in an environment where they are not cooperating,” said co-author Fei Fang, an assistant professor in the Institute for Software Research.
(Carnegie Mellon University)

Bot Can Beat Humans in Multiplayer Hidden-Role Games
MIT researchers have developed a bot, DeepRole, equipped with artificial intelligence that can beat human players in tricky online multiplayer games where player roles and motives are kept secret. At the Conference on Neural Information Processing Systems next month, the researchers will present DeepRole.
(MIT)

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
In this work, researchers present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics.
(DeepMind & University College London)

Technology

The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era.
(Google Research)

Benchmarking Safe Exploration in Deep Reinforcement Learning
OpenAI has released Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. They also provide a standardized method of comparing algorithms and how well they avoid costly mistakes while learning.
(OpenAI) / (OpenAI Blog)

RandAugment: Practical Automated Data Augmentation with A Reduced Search Space
RandAugment has a significantly reduced search space which allows it to be trained on the target task with no need for a separate proxy task. Furthermore, due to the parameterization, the regularization strength may be tailored to different model and dataset sizes.
(Google Brain)