There is general consensus within the AI research community that progress in the field is accelerating: it is believed that human-level AI will be reached within the next one or two decades. A key question is whether these advances will accelerate further after general human level AI is achieved, and, if so, how rapidly the next level of AI systems ('super-human') will be achieved.

Since the mid 1970s, Computer scientists have developed a rich theory about the computational resources that are needed to solve a wide range of problems. We will use these methods to make predictions about the feasibility of super-human level cognition.

There is general consensus within the AI research community that progress in the field is accelerating: it is believed that human-level AI will be reached within the next one or two decades on a range of cognitive tasks. A key question is whether these advances will accelerate further after general human level AI is achieved, and, if so, how rapidly the next level of AI systems (‘super-human’) will be achieved. Having a better understanding of how rapidly we may reach this next phase will be useful in preparing for the advent of such systems.

Computational complexity theory provides key insights into the scalability of computational systems. We will use methods from complexity theory to analyze the possibility of the scale-up to super-human intelligence and the speed of such scale-up for different categories of cognition.