Researchers from OpenAI and DeepMind are hoping to make artificial intelligence safer using a new algorithm that learns from human feedback. Both companies are experts in reinforcement learning – an area of machine learning that rewards agents if they take the right actions to complete a task under a given environment. The goal is specified through an algorithm, and the agent is programmed to chase the reward, like winning points in a game. Reinforcement learning has been successful in teaching machines how to play games like Doom or Pong or drive autonomous cars via simulation. It's a powerful method to explore an agent's behavior, but it can be dangerous if the hard-coded algorithm is wrong or produces undesirable effects.

Google's latest effort to thaw relations with China involves an artificial intelligence pow-wow -- and a few games of Go. Google's latest effort to thaw relations with China involves an artificial intelligence pow-wow -- and a few games of Go. Years after Beijing locked out virtually every Alphabet Inc. service, executive chairman Eric Schmidt and a cadre of mid-level Chinese government officials kicked off a summit in the historic canal-laced town of Wuzhen Tuesday: a rare instance of the search leader working in tandem with the country's bureaucrats at a high-profile public event. Google experts and prominent local academics will exchange notes and host discussions but the centerpiece will be a contest between DeepMind's so-far undefeated AlphaGo system and Ke Jie, local champion of the 2,500-year-old strategy board game Go.

Google taught DeepMind to play Atari games all on its own, but letting humans help may be faster, according to researchers from Microsoft and Germany. They invited folks of varying skills to play five Atari 2600 titles: Ms. Pac-Man, Space Invaders, Video Pinball, Q*Bert and Montezuma's Revenge. After watching 45 hours of human gameplay, the algorithm could beat its mentors at pinball, though it struggled at Montezuma's revenge -- just as Deepmind did. Unlike with DeepMind's trial-and-error methods (below), however, the human-aided AI learned to play the games in less time than other AI systems. "Current state-of-the-art approaches require millions of training samples," the paper states.

Elon Musk keeps surprising the world with his technological breakthroughs. OpenAI, a non-profit company focused on AI research, recently made an announcement regarding its groundbreaking AI invention. It has developed an AI system that can complete an actual physical task after watching just one demonstration of the task. At the core of the AI system are two different neural networks -- a vision network and an imitation network. These two work behind the scenes to provide the remarkable capability to imitate human actions, a giant step closer to building true AI systems.

Silvia Chiappa is a Senior Research Scientist at DeepMind, working at the intersection of probabilistic modeling and deep learning. Prior to DeepMind, she worked at Microsoft Research Cambridge, at the Statistical Laboratory University of Cambridge and the Max-Planck Institute for Biological Cybernetics. I spoke with Silvia to learn about her career in science, how we can overcome barriers for women in tech, and more. How did you begin your work in science and technology? At the age of 12 I started to appreciate the elegance of maths when learning about trigonometry.

How does DeepMind choose research topics to focus on? Going through DeepMind's publication lists, their focus seems a bit more narrow than other labs. Yet they are probably one of the largest (if not the largest) research group with about 400 people (https://en.wikipedia.org/wiki/DeepMind) . For example, the have a great deal of work on generative models but no work on GANs. They also have a great deal of focus on Reinforcement Learning but not robotics.

The AI company is in the early stages of partnering with the UK's National Grid DeepMind is in talks with the UK's National Grid to boost energy efficiency using artificial intelligence. The AI company, acquired by Google for £400 million in 2014, has developed algorithms that can anticipate energy demand and supply. These algorithms are already being used within Google's own data centres, allowing the tech giant to cut energy by 40%, but they are now in talks with the National Grid, which owns and operates energy infrastructure across the UK. DeepMind is offering AI-powered solutions that could help balance energy supply and demand across the nation. "We're early stages talking to National Grid and other big providers about how we could look at the sorts of problems they have," Demis Hassabis, DeepMind's co-founder and CEO, told the Financial Times.

DeepMind now has almost two dozen staff working out of an office at Google's headquarters in Mountain View, California. The London-headquartered artificial intelligence (AI) lab, acquired by Google in 2014 for a reported £400 million, has formed the team in less than six months. "I can confirm there are currently just over 20 DeepMind staff in Mountain View," a DeepMind spokesperson told Business Insider on Wednesday. "The team there continues to hire and will grow." The team has been established so that DeepMind can collaborate more closely with Google. DeepMind's researchers, for example, are helping engineers at Google to embed AI into Google Play, Ads, and Shopping.

When future robots enter the world, they won't have a learning curve. Artificial intelligence researchers are creating tools to help teach the robots that will assemble our gadgets in factories, or do chores around our home, before they ever step (or roll) into the real world. These simulators, most recently announced by Nvidia as a project called Isaac's Lab but also pioneered by Alphabet's DeepMind and Elon Musk's OpenAI, are 3D spaces that have physics just like reality, with virtual objects that act the same way as their physical counterparts. Virtual spaces are required because one way of teaching robots is a method called reinforcement learning, or the chore of doing one task over and over again until it's done correctly. In a simulation, training the bots can be done more quickly and cheaply than in real life because lots of simulated robots can learn at once.

The day Thore Graepel joined Google's DeepMind artificial intelligence lab in the spring of 2015, his new colleagues sat him down for a game of Go. Over the previous year, they'd trained a neural network to play the ancient game. Graepel happened to be a player himself, holding a one dan rank, the Go equivalent of a black belt. As the game began with DeepMind researchers circled around him, Graepel was confident he would win. After all, he never had trouble playing other Go programs.