Life, learning and emergence

What is life, and why is artificial life so elusive? How can we characterise and augment machine learning? We seek a fundamental understanding of life, learning and other emergent phenomena, and we create systems for automated decision-making, inference and discovery.

  • Fundamental advances in machine learning

    Fundamental advances in AI

    Developing radical new approaches to inference and automated decision making using advances in quantum information and statistical physics.

  • Repairable instead of robust

    Developing a new approach to resilience in which mistakes and unexpected events are mitigated by easy repairs rather than redundancy.

  • Reprogramming the cell

    Applying mathematical tools to the holy grail of cellular biology: can we produce every type of human cell from within the laboratory?

  • Surprises from simple rules

    Understanding complex dynamical behaviours generated by simple rules, such as cellular automata, polyominoes and models of competition.

  • Intelligence of graphs

    Predicting the behaviour of graphs and processes on them by treating topological patterns as constraints on a random graph ensemble.

  • Structure of how things relate

    Creating mathematical tools for characterizing the structure of ideal graphs and irregular networks, and the behaviour of processes on them.