Alan Turing laid the foundation for the modern computer in the 1930s, building machines that could break secret codes and play chess. In doing so, he also founded the field of artificial intelligence (AI). The 2010s have seen significant developments in AI. For example, there are now programs that can drive a car, beat the world go champion and write summaries of medical research papers.

Despite many impressive advances, modern AI programs still have major limitations. Problems that are easy for humans to solve are often impossible for AI programs. For example, there are no household robots today that can help with the dishes, laundry and cleaning in our homes. On the other hand, there are industrial robots that can work in precisely designed and predictable environments. The problem is that modern AI programs have very limited adaptability. This is because they are equipped with a fixed architecture that is then trained once and for all to perform a specific task. Thus, severe limitations are built into the systems from the start.

In this project, we start from the unique ability of animals to adapt to different environments. Our approach is to mimic a number of fundamental mechanisms for how animals learn and make decisions. In particular, we mimic the plasticity of natural nervous systems, providing a dynamic architecture that constantly adapts to new situations. The project is a collaboration between researchers from Chalmers and Harvard.