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The problem with Montezuma’s Revenge is that it doesn’t provide regular rewards for the AI agent. It’s a puzzle-platformer where players have to explore an underground pyramid, dodging traps and enemies while collecting keys that unlock doors and special items. If you were training an AI agent to beat the game, you could reward it for staying alive and collecting keys, but how do you teach it to save certain keys for certain items, and use those items to overcome traps and complete the level? The answer: curiosity.

Automation and artificial intelligence (AI) are transforming businesses and will contribute to economic growth via contributions to productivity. They will also help address “moonshot” societal challenges in areas from health to climate change.

At the same time, these technologies will transform the nature of work and the workplace itself. Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will change.

While we believe there will be enough work to go around (barring extreme scenarios), society will need to grapple with significant workforce transitions and dislocation. Workers will need to acquire new skills and adapt to the increasingly capable machines alongside them in the workplace. They may have to move from declining occupations to growing and, in some cases, new occupations.

Lots of high-minded technological thinkers, particularly Elon Musk, have proposed a universal basic income, a form of wealth distribution that ensures every citizen receives a baseline income whether or not they are employed, as a likely solution to the problem of workforce automation. But the White House report takes a more somber approach, describing a basic income as “giving up on the possibility of workers’ remaining employed.” Instead, the report suggests a number of policy proposals (like Obama’s national free community college initiative, and expanded unemployment benefits) as ways of actively facilitating the transition into a more AI driven economy.

hanging people’s behaviour is key to tackling the world’s health, social and environmental problems, such as obesity, sustainable living and cybersecurity. To change behaviour, though, we need to know what works, for whom, where and how. But research is generated far faster than humans can access and use it. A search on Google Scholar for “behaviour change” produced over 60,000 hits for the first six months of 2018. Evidence on behaviour change interventions is also messy. Different terms are often used to describe similar things – “physical activity” and “exercise”, for example. This means different people can often mean different things when discussing or researching behaviour change. This lack of shared vocabulary limits our progress in discovering what interventions work best.