Superintelligence, roughly defined as an AI algorithm that can solve all problems better than people, will be a watershed for humanity and tech.

Even the best human experts have trouble making predictions about highly probabilistic, wicked problems. And yet those wicked problems surround us. We are all living through immense change in complex systems that impact the climate, public health, geopolitics and basic needs served by the supply chain.

Just determining the best way to distribute COVID-19 vaccines without the help of an algorithm is practically impossible. We need to get smarter in how we solve these problems — fast.

Superintelligence, if achieved, would help us make better predictions about challenges like natural disasters, building resilient supply chains or geopolitical conflict, and come up with better strategies to solve them. The last decade has shown how much AI can improve the accuracy of our predictions. That’s why there is an international race among corporations and governments around superintelligence.

In the next year and a half, we’re going to see increasing adoption of technologies that will trigger a broader industry shift, much as Tesla triggered the transition to EVs.

Highly credible think tanks like Deepmind and OpenAI say that the path to superintelligence is visible. Last month, Deepmind said reinforcement learning (RL) could get us there, and RL is at the heart of embodied AI.

Embodied AI is AI that controls a physical “thing,” like a robot arm or an autonomous vehicle. It is able to move through the world and affect a physical environment with its actions, similar to the way a person does. In contrast, most predictive models live in the cloud doing things such as classifying text or images, steering flows of bits without ever moving a body through three-dimensional space.

For those who work in software, including AI researchers, it is too easy to forget the body. But any superintelligent algorithm needs to control a body because so many of the problems we confront as humans are physical. Firestorms, coronaviruses and supply chain breakdowns need solutions that aren’t just digital.

All the crazy Boston Dynamics videos of robots jumping, dancing, balancing and running are examples of embodied AI. They show how far we’ve come from early breakthroughs in dynamic robot balancing made by Trevor Blackwell and Anybots more than a decade ago. The field is moving fast and, in this revolution, you can dance.

Challenge 1: One of the challenges when controlling machines with AI is the high dimensionality of the world — the sheer range of things that can come at you.