Our lives are already enhanced by AI – or at least an AI in its infancy – with technologies using algorithms that help them to learn from our behaviour. As AI grows up and starts to think, not just to learn, we ask how human-like do we want their intelligence to be and what impact will machines have on our jobs? We are well on the way to a world in which many aspects of our daily lives will depend on AI systems. Within a decade, machines might diagnose patients with the learned expertise of not just one doctor but thousands. They might make judiciary recommendations based on vast datasets of legal decisions and complex regulations.

Our lives are already enhanced by AI – or at least an AI in its infancy – with technologies using algorithms that help them to learn from our behaviour. As AI grows up and starts to think, not just to learn, we ask how human-like do we want their intelligence to be and what impact will machines have on our jobs? We are well on the way to a world in which many aspects of our daily lives will depend on AI systems. Within a decade, machines might diagnose patients with the learned expertise of not just one doctor but thousands. They might make judiciary recommendations based on vast datasets of legal decisions and complex regulations.

We are well on the way to a world in which many aspects of our daily lives will depend on AI systems. Within a decade, machines might diagnose patients with the learned expertise of not just one doctor but thousands. They might make judiciary recommendations based on vast datasets of legal decisions and complex regulations. And they will almost certainly know exactly what's around the corner in autonomous vehicles. "Machine capabilities are growing," says Dr Stephen Cave, Executive Director of the Leverhulme Centre for the Future of Intelligence (CFI).

Microsoft has made Project Malmo available for novice to experienced programmers on GitHub via an open-source license. It's a platform that uses the world of Minecraft as a testing ground for advanced artificial intelligence research. The system, which had until now only been open to a small group of computer scientists in private preview, is primarily designed to help researchers develop sophisticated, more general artificial intelligence, or AI, that can do things like learn, hold conversations, make decisions and complete complex tasks. That's key to creating systems that can augment human intelligence--and eventually help us with everything from cooking and doing laundry to driving and performing lifesaving tasks in an operating room. Katja Hofmann, a researcher in Microsoft's Cambridge, UK, research lab, who leads the development of Project Malmo, said the system will help researchers develop new techniques and approaches to reinforcement learning.

Microsoft has made Project Malmo, a platform that uses the world of Minecraft as a testing ground for advanced artificial intelligence research, available for novice to experienced programmers on GitHub via an open-source license. The system, which had until now only been open to a small group of computer scientists in private preview, is primarily designed to help researchers develop sophisticated, more general artificial intelligence, or AI, that can do things like learn, hold conversations, make decisions and complete complex tasks. That's key to creating systems that can augment human intelligence -- and eventually help us with everything from cooking and doing laundry to driving and performing lifesaving tasks in an operating room. Katja Hofmann, a researcher in Microsoft's Cambridge, UK, research lab, who leads the development of Project Malmo, said the system will help researchers develop new techniques and approaches to reinforcement learning. That's an area of AI in which agents learn how to complete a task by being given a lot of room for trial and error and then being rewarded when they make the right decision.

Participants in recent discussions of AI-related issues ranging from intelligence explosion to technological unemployment have made diverse claims about the nature, pace, and drivers of progress in AI. However, these theories are rarely specified in enough detail to enable systematic evaluation of their assumptions or to extrapolate progress quantitatively, as is often done with some success in other technological domains. After reviewing relevant literatures and justifying the need for more rigorous modeling of AI progress, this paper contributes to that research program by suggesting ways to account for the relationship between hardware speed increases and algorithmic improvements in AI, the role of human inputs in enabling AI capabilities, and the relationships between different subfields of AI. It then outlines ways of tailoring AI progress models to generate insights on the specific issue of technological unemployment, and outlines future directions for research on AI progress.