Adaptive Neural Networks through Epigenetic Processes

The recent increased availability of both data and computation has led to global interest in artificial neural networks - these have been shown to be highly effective in creating artificial intelligence particularly when functioning at scale (e.g., depth). Deploying neural networks typically involves training to establish appropriate weights between inputs and outputs. However such training is scenario dependent, with retraining needed if there is a significant change in the underlying environment. Therefore it is highly desirable for future neural networks to have the ability to self-adapt in relation to changes in their deployment scenario, so that flexibility is apparent and performance is maintained without full-scale retraining. This will become more important as AI engages with dynamic situations where neural networks may need to ideally learn and adapt with greater agility. In this project the aim is to take inspiration from genetic algorithms (GAs) for neural network self-adaptation. Longstanding work has demonstrated that GAs can successfully be used to evolve a high performance population of neural networks (e.g., Evolving Neural Networks through Augmenting Topologies - NEAT) while more recent work by Uber AI labs has demonstrated that genetic algorithms are a remarkably competitive alternative to training deep neural networks for reinforcement learning. The project will involve working in the relatively new field of epigenetic algorithms, and using these to promote adaptation of neural networks that are represented in a genetic form. Genetic processes inspire epigenetic algorithms, where the interaction with the environment influences the genes held by an individual between cycles of population reproduction. Epigenetics is a phenomenon that is now recognised in biology, pharmacology and medicine with many applications. The methodology will involve developing suitable representations, epigenetic techniques and evaluation methods to explore the problem of self-adaption in neural networks. This is an ambitious goal and this project will seek to establish fundamental steps in this area by assessing benchmarks that are well understood. This PhD is suitable for someone keen to gain in-depth knowledge of state-of-the-art deep learning and neuroevolution, and an interest in developing future AI with new and general capabilities. The project will be carried out in collaboration with IBM UK, Dstl, Cardiff University Crime and Security Institute and with international cooperation (University partners in the US via the Distributed Analytics and Information Sciences Distributed Technology Alliance).