Life, learning and emergence
Developing mathematical foundations for life and artificial life, machine intelligence, and other emergent phenomena that defy reductionism.
What is life? Darwin’s theory provides a qualitative understanding of evolution. But from a physics perspective, we don’t know how life got started in the first place. What investigate the thermodynamic basis for emergent self-replication and adaptation, of which biology is just one instance. Can this be used to engineer artificial digital life? Can evolution itself be made a predictive science?
How do we make intelligent machines? Far from approaching artificial general intelligence, AI is stuck in high-dimensional curve-fitting. We seek mathematical insights that could lead to more intelligent AI, such as causal reasoning, reusable functional modules, and a representation of the environment. We investigate ways to use computation and AI to automate the search for new mathematical insights. Are there fundamental limits to AI, and what might this tell us about human intelligence?
What are the emergent properties of digital and neural computation, and might this shed light on autonomy and free will? We study information processing at the genetic level and the functional architecture of gene regulatory networks. We seek a theoretical understanding of cell programming and how to infer programming sets. Is causality itself an emergent phenomenon, as we traverse across different organisational length scales?
Applying mathematical tools to the holy grail of cellular biology: can we produce every type of human cell from within the laboratory?
Developing radical new approaches to inference and automated decision making using advances in quantum information and statistical physics.
Understanding complex dynamical behaviours generated by simple rules, such as cellular automata, polyominoes and models of competition.
Creating mathematical tools for characterizing the structure of ideal graphs and irregular networks, and the behaviour of processes on them.
Predicting the behaviour of graphs and processes on them by treating topological patterns as constraints on a random graph ensemble.