In recent months, researchers at OpenAI have been focusing on developing artificial intelligence (AI) that learns better. Their machine learning algorithms are now capable of training themselves, so to speak, thanks to the reinforcement learning methods of their OpenAI Baselines. Now, a new algorithm lets their AI learn from its own mistakes, almost as human beings do. The development comes from a new open-source algorithm called Hindsight Experience Replay (HER), which OpenAI researchers released earlier this week. As its name suggests, HER helps an AI agent "look back" in hindsight, so to speak, as it completes a task.

The Shadow Robot Company, that manufactures robotic hands for grasping and manipulation for real world challenges from fruit picking to bomb disposal, is supplying its Shadow Dexterous Hands to OpenAI, a non-profit company focusing on the path to safe artificial intelligence. The research is claimed to have created eight newly released environments, four of which using the Shadow Hand robot to solve realistic manipulation tasks. The Shadow Hand is tactile enough to rotate a block and a solid egg and flexible enough to move a pen between its fingers. Each task has a'goal', such as achieving the desired orientation of a block in the Shadow hand block manipulation task. Along with releasing these new robotics environments, OpenAI is releasing code for Hindsight Experience Replay, a reinforcement learning algorithm that can teach and improve robotic technology to learn from failure.

The minute hand on the robot apocalypse clock just inched a little closer to midnight. DeepMind, the Google sister-company responsible for the smartest AI on the planet, just taught machines how to figure things out for themselves. AI that only exists to parse data, such as neural networks that decide whether something is a hotdog or not, have relatively little to concentrate on compared to the near-infinite number of things a physical robot has to figure out. To solve this problem DeepMind built a new learning paradigm for AI-powered robots called'Scheduled Auxiliary Control (SAC-X).' This new paradigm gives robots a simple goal like'clean up this playground' and rewards it for completion. The auxiliary tasks we define follow a general principle: they encourage the agent to explore its sensor space.

In recent months, researchers at OpenAI have been focusing on developing artificial intelligence (AI) that learns better. Their machine learning algorithms are now capable of training themselves, so to speak, thanks to the reinforcement learning methods of their OpenAI Baselines. Now, a new algorithm lets their AI learn from its own mistakes, almost as human beings do. The development comes from a new open-source algorithm called Hindsight Experience Replay (HER), which OpenAI researchers released earlier this week. As its name suggests, HER helps an AI agent "look back" in hindsight, so to speak, as it completes a task.

Subscribe and review: Apple, Spotify, Soundcloud, Audioboom, Mixcloud & Acast and join the discussion on Facebook, Twitter & email us as podcast@theguardian.com Technology companies seem to have a bad reputation at the moment. Whether through honest mistakes or more intentional oversights, the likes of Apple, Facebook, Google and Twitter have created distrust among consumers. But as technology develops, and as we hand over more control to artificial intelligence and machines, it becomes difficult for developers to foresee the negative consequences or side-effects that might arise. In October 2017, the AI company DeepMind, a subsidiary of Google, created an ethics group made up of employees and external experts called DeepMind Ethics & Society. But are these groups any more than a PR strategy? And how can we train technology students to preempt an ethical disaster before they enter the workforce?

A new artificial intelligence that is learning to understand the'thoughts' of others has been built by Google-owned research firm DeepMind. The software is capable of predicting what other AIs will do, and can even understand whether they hold'false beliefs' about the world around them. DeepMind reports its bot can now pass a key psychological test that most children only develop the skills for at around age four. Its proficiency in this'theory of mind' test may lead to robots that can think more like humans. DeepMind reports its bot can now pass a key psychological test that most children only develop the skills for around age four.

MACHINES are getting to know each other better. An artificial intelligence, developed by Google-owned research firm DeepMind, can now pass an important psychological assessment that most children only develop the skills to pass at around age 4. Its aptitude in this key theory of mind test may lead to AIs that are more human-like. Most humans regularly think about other people's desires, beliefs or intentions.

Google's artificial intelligence (AI) division DeepMind is developing a system that could one day predict when a hospital patient is at risk of dying, even if serious signs of illness are not immediately apparent. With the assistance of the US Veterans Administration, the partnership is seeking to understand the changes in a hospital patient's condition that could result in death if left unchecked by a doctor or nurse, Alphr reports. To do this, the website says, the partnership has fed 700,000 medical records to an AI programme to identify signs of "human error" in treatment. The records are from US army and police veterans. The partnership's first priority is to use AI to understand acute kidney injury, says MedCityNews, which is "a complication related to patient deterioration".

Mujoco is mostly a physics engine, and I'm willing to bet that whatever parts you're thinking of when you say it's "more" than a physics engine either exist in some form in Bullet and the rest, or aren't relevant for RL. The things you listed are engines that delegate to other projects for their physics simulation, and come with a ton of heavyweight baggage that you don't need to do RL.

This release includes four environments using the Fetch research platform and four environments using the ShadowHand robot. The manipulation tasks contained in these environments are significantly more difficult than the MuJoCo continuous control environments currently available in Gym, all of which are now easily solvable using recently released algorithms like PPO. Furthermore, our newly released environments use models of real robots and require the agent to solve realistic tasks. FetchReach-v0: Fetch has to move its end-effector to the desired goal position. FetchSlide-v0: Fetch has to hit a puck across a long table such that it slides and comes to rest on the desired goal.