Prof. Simon Colton

Queen Mary University of London

Simon Colton is an AI researcher with particular focus on issues of Computational Creativity, where we engineer software to take on creative responsibilities in art and science projects. He undertakes projects advancing the state of the art in generative technologies such as evolutionary approaches and deep learning, and uses these to help develop software such as The Painting Fool, The WhatIf Machine, the Wevva game designer, the HR3 automated code generator, and the Art Done Quick casual creator for visual art. In turn, these software systems and their output are used in cultural projects such as a poetry readings, art exhibitions, game jams, and even the production of a West-End musical. This enables Simon to undertake much public engagement, with coverage from the BBC, The Guardian, MIT Tech Review, The New Scientist and many others.

These practical and cultural projects inform an evolving philosophical discourse around what it means for machines to be creative, and Simon has co-authored numerous essays driving forward our understanding of this important topic. In this way, he has helped to introduce ideas such as automated framing of products and processes, issues of authenticity and the notion of the machine condition, i.e., what the lived experience of a machine is, and how this could be expressed by that machine through creative production.

He is particularly interested in supervising students in project where we apply generative technologies to applications in videogame design, visual art, software engineering, music and text generation. One particular current interest is stretching the boundaries of both what can be achieved by, and our understanding of, generation deep learning methods such as generative adversarial networks (GANs) and auto encoders. Another current interest is the design of casual creators, which are creativity support tools where the focus is on users having fun, rather than on efficient, professional production of artefacts. He is currently developing a casual creator for visual art called Art Done Quick for public release, which employs evolutionary and deep learning techniques to deliver a fun-first experience while users make decorative art pieces. Any project involving generative technologies is of interest to Simon.

Research Areas:

• Game AI

• Game Audio and Music

• Game Design

• Computational Creativity

• Player Experience

• Casual Creators

• Generative Deep Learning