Artificial Intelligence

Making sure Artificial Intelligence (AI) does what we want and behaves in predictable ways will be crucial as the technology becomes increasingly ubiquitous. It is an area frequently neglected in the race to develop products, but DeepMind has now outlined its research agenda to tackle the problem.
AI safety, as the field is known, has been gaining prominence in recent years. That is probably at least partly down to the overzealous warnings of a coming AI apocalypse from Elon Musk and Stephen Hawking. It is also recognition of the fact that AI technology is quickly pervading all aspects of our lives, making decisions on everything from what movies we watch to whether we get a mortgage.
That is why DeepMind hired researchers who specialize in foreseeing the unforeseen consequences of the way we built AI back in 2016. The team has spelled out the three key domains they think require research if we are going to build autonomous machines that do what we want.

In a new blog designed to provide updates on the team’s work, they introduce the ideas of specification, robustness, and assurance, which they say will act as the cornerstones of future research. Specification involves making sure AI systems do what their operator intends; robustness means a system can cope with changes to its environment and attempts to throw it off course; and assurance involves our ability to understand what systems are doing and how to control them.