Fusion reactors promise cheap, abundant and relatively clean energy – if we can get them to work. Now, thanks to artificial intelligence firm DeepMind, fusion researchers are one step closer to extracting power from plasma hotter than the surface of the sun. DeepMind worked with scientists at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, to create a neural network capable of controlling the magnetic fields within EPFL's Variable Configuration Tokamak (TCV) fusion reactor. These magnetic fields are essential for keeping the plasma generated by the reactor safely contained. If the plasma touches the walls of the reactor it rapidly cools, stifling the reaction and potentially causing significant damage.

"We've been excited by the potential for this technology to help fill in some of the gaps in our understanding of biology and accelerate scientific research to enable new, effective treatments for diseases," DeepMind AI-for-science head Pushmeet Kohli said.

DeepMind has attracted mixed headlines since Google paid $50 million for the U.K.-based AI startup in 2014. The awe inspired by DeepMind's AlphaGo system defeating Go world champion Lee Sedol was soon tempered by criticisms of its controversial access to personal health records, which the ICO ruled had breached the Data Protection Act, and the concerns grew when Google announced it would be taking control of DeepMind Health. Trust has wavered ever since, but the AI developed in the DeepMind lab in King's Cross, London, continues to lead the world and is finding its way into some intriguing applications. DeepMind is collaborating with Google's AOI health research team and a group of research institutions, led by the Cancer Research U.K. Centre at Imperial College London to improve the detection of breast cancer. The disease kills 500,000 people around the world every year, partly due to the challenges of detection and diagnosis.

DeepMind has joined the ranks of those using AI to save fragile wildlife populations, and it's doing that on a grand scale. The company is partnering with conservationists and ecologists on a project that uses machine learning to speedily detect and count animals in "millions" of photos taken over the past nine years in Tanzania's Serengeti National Park. Where it normally takes up to a year for volunteers to return labeled photos, DeepMind has developed a model that can label most animals at least as well as humans while shortening the process by up to nine months That's no small challenge when animals seldom cooperate with motion-sensitive cameras -- the AI can recognize out-of-focus cheetahs or fast-moving ostriches. The technology should also be viable in the wild. DeepMind is developing a pre-trained version of its AI model that would need only "modest" hardware and little internet connectivity -- important when a powerful computer and fast internet access could be disruptive to wildlife and expensive to deploy.

In collaboration with its Britain-based Artificial Intelligence (AI) subsidiary DeepMind, Google has developed a system to predict wind power output 36 hours ahead of actual generation. Google said that these type of predictions can boost the value of wind energy and can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. "Over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged," Sims Witherspoon, Programme Manager at DeepMind and Will Fadrhonc, Carbon Free Energy Programme Lead at Google wrote in a blog post this week. "However, the variable nature of wind itself makes it an unpredictable energy source - less useful than one that can reliably deliver power at a set time," they said. In search of a solution to this problem, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central US.