Robots walking down the street, surrounded by astounded onlookers, is an increasingly common sight. But these machines aren't yet the do-it-all assistants you'd want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it's labor-intensive and time-consuming to physically teach these machines so many actions across different settings. "One natural idea is to use simulation as a training ground. While there has been significant progress over the last few years in the physics engines that power robotics simulators, one of the remaining challenges has been creating sufficiently rich and diverse simulation content to capture the complexity of the real world," says Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science (EECS), Aeronautics and Astronautics, and Mechanical Engineering at MIT, and a principal investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

Robots walking down the street, surrounded by astounded onlookers, is an increasingly common sight. But these machines aren't yet the do-it-all assistants you'd want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it's labor-intensive and time-consuming to physically teach these machines so many actions across different settings. "One natural idea is to use simulation as a training ground. While there has been significant progress over the last few years in the physics engines that power robotics simulators, one of the remaining challenges has been creating sufficiently rich and diverse simulation content to capture the complexity of the real world," says Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science (EECS), Aeronautics and Astronautics, and Mechanical Engineering at MIT, and a principal investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with Intelligent Systems.

Musicians have been experimenting with artificial intelligence for a few years now. For example, in 2019, an AI trained on Schubert's music completed his Unfinished Symphony and last October the Beethoven Orchestra in Bonn performed an AI-generated version of Beethoven's last symphony. But what are the limits of AI music? Can an AI really be considered creative? And is it possible for an AI to improvise with musicians live on stage?

This story has begun with JukeBox, a music model developed by OpenAI and trained on more than a million songs and music pieces. It makes use of transformers (like GPT-3), influencing the music piece coherency, with inner logic, specific style, and full-audio generation. Normally, I use music written by JukeBox as a soundtrack for my movies (like Empty Room or Bloomsday MMXX). Unique sounds, never played before and also free to use in your film (better than inflationary applied stock music), always appealed to a movie maker in me. But this one was different. The soundtrack itself told a story in an unknown language, an unusual narrative generated by machines but touching the human heart.