Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of.

From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world's most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centres consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world's increasing need for computing power. Reducing energy usage has been a major focus for us over the past 10 years: we have built our own super-efficient servers at Google, invented more efficient ways to cool our data centres and invested heavily in green energy sources, with the goal of being powered 100 percent by renewable energy. Compared to five years ago, we now get around 3.5 times the computing power out of the same amount of energy, and we continue to make many improvements each year.

Google-stablemate DeepMind is creating a blockchain-like system to show how sensitive medical data passing through its processors will be used, allowing healthcare professionals to check if data has been tampered with. Its healthcare arm, DeepMind Health, is working to improve medical diagnoses with machine learning tools. Large amounts of confidential data are required to develop these tools – something DeepMind hasn't always been trusted to handle. Last year, the company was criticized for gaining access to current and historic patient records for 1.6 million individuals across three London Royal Free NHS Trust hospitals, which extended beyond the scope of their research. The announcement of Verifiable Data Audit is an attempt to gain back some of the lost trust.

In January 2014, Google splashed £400 million on buying the London-based artificial intelligence firm DeepMind. At the time, it wasn't clear what Google, and now parent company Alphabet, would get for its money. Four years later, DeepMind's team that focuses on developing AI for Google is starting to pay off. Google's launch of its latest mobile operating system, Android Pie, involves DeepMind's largest real-world machine learning roll-out to date. It's looking to solve one of the modern smartphone's most frustrating features: poor battery life.

Do computers know what we are thinking? To find out, Google's artificial intelligence lab DeepMind has created a set of gruelling tests that probe AI's progress in understanding the world. No AIs have passed the tests yet, but one got extremely close. The tests examine theory of mind – the ability to reason about another's beliefs – and are inspired by classic experiments in psychology. Each test consists of a short paragraph describing a scenario involving people and a few questions for AI to answer about it.

From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world's most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centers consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world's increasing need for computing power. Google is taking many steps to reduce energy consumptions . Compared to five years ago, Google now get around 3.5 times the computing power out of the same amount of energy.

Last week, humanity struck back against the machines -- sort of. Actually, we beat them at a video game. In a best-of-three match, two teams of pro gamers overcame a squad of AI bots that were created by the Elon Musk-founded research lab OpenAI. The competitors were playing Dota 2, a phenomenally popular and complex battle arena game. But the match was also something of a litmus test for artificial intelligence: the latest high-profile measure of our ambition to create machines that can out-think us. In the human-AI scorecard, artificial intelligence has racked up some big wins recently.

DeepMind Technologies, an artificial intelligence (AI) company based in London (U.K.) that was purchased by Google in 2014, recently announced that it is applying its machine learning algorithms to Google data centers to reduce the amount of energy dedicated to cooling by as much as 40 percent.

AI may have beaten the world's best Go player, but Dota 2 pros have shown that in their game, humans are still top of the food chain -- for now, at least. Last week, Dota 2 players from around the world clashed at the biggest tournament of the year, The International, with team OG taking the title and over $11 million in prize money. Arguably more important, though, was the contest of man versus machine(-learning) in a best-of-three exhibition series. OpenAI, the research group co-founded by Elon Musk, took its team of five bots to The International to square up against professional players in their toughest test yet. Earlier in August, OpenAI wiped the floor with a squad of Dota 2 casters and ex-pro players in a warm-up match.

Update: Dota 2 does provide a scripting interface, enabling bots to be written in Lua. However, this limited interface does not enable bots to communicate with remote processes and save data about games played. OpenAI Five is a huge step forward for AI, but it's also really intimidating for AI researchers. Never before has there been so many open tools for building AI systems, but it also feels like the barrier to entry for academics has actually increased over recent years. I posted an open call for any interested parties to build the best StarCraft AI possible back in 2009, and it was open to anyone interested in AI. Now, it seems like you need to have access to closed APIs, massive compute power, and historic training data to make advances in AI.