I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Safety is one of the emerging concerns in deep learning systems. In the context of deep learning systems, safety is related to building agents that respect safety dynamics in a given environment.
Safety is one of the emerging concerns in deep learning systems. In the context of deep learning systems, safety is related to building agents that respect safety dynamics in a given environment. In many cases such as supervised learning, safety is modeled as part of the training datasets. However, other methods such as reinforcement learning require agents to master the dynamics of the environments by experimenting with it which introduces its own set of safety concerns. To address some of these challenges, OpenAI has recently open sourced Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.
One of the most important innovations in the present era for the development of highly-advanced AI systems has been the introduction of Reinforcement Learning (RL). It has the potential to solve complex decision-making problems. It generally follows a "trial and error" method to learn optimal policies of a given problem. It has been used to achieve superhuman performance in competitive strategy games, including Go, Starcraft, Dota, among others. Despite the promise shown by reinforcement algorithms in many decision-making problems, there are few glitches and challenges, which still need to be addressed.
Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.
Where the training of machine learning models is concerned, there's always a risk of overfitting -- or corresponding to closely -- to a particular set of data. In point of fact, it's not infeasible that popular machine learning benchmarks like the Arcade Learning Environment encourage overfitting, in that they have a low emphasis on generalization. That's why OpenAI -- the San Francisco-based research firm cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, and others -- today released the Procgen Benchmark, a set of 16 procedurally-generated environments (CoinRun, StarPilot, CaveFlyer, Dodgeball, FruitBot, Chaser, Miner, Jumper, Leaper, Maze, BigFish, Heist, Climber, Plunder, Ninja, and BossFight) that measure how quickly a model learns generalizable skills. It builds atop the startup's CoinRun toolset, which used procedural generation to construct sets of training and test levels. "We want the best of both worlds: a benchmark comprised of many diverse environments, each of which fundamentally requires generalization," wrote OpenAI in a blog post.