Speaker: Dr Iain Murray; Reader in Machine Learning, School of Informatics
Title: Will machine learning need huge compute resources and brutal exploration?
Abstract: One of the several factors that has made machine learning, and deep learning in particular, grow rapidly in the last 5+ years has been access to faster computing resources. However, getting publishable results in some areas of machine learning now requires a large amount of GPU time. I’ll give some thoughts on the extent to which this compute time is necessary or justified, and what we might be able to do about it.
Speaker: Pavlos Apostolidis; PhD student, London Centre for Nanotechnology, University College London
Title: Quantum Computing in a Nutshell
Abstract: The field of quantum computing & information has received some great interest in the last few years. Many research groups and world-leading companies work towards the implementation of a universal quantum computer. But why? What’s the fuss all about? In this talk, we will go through some of the most important promises of a quantum computer and give some examples on its usage. We will briefly talk about what makes a quantum computer so special, and clarify on some facts and misconceptions along the way. The talk includes a short introduction to the different kinds of quantum bits (and thus quantum computers), outlining some of the main characteristics of each. The main criterial that any universal quantum computer has to follow will be briefly discussed, emphasising on the importance of each one. Finally, we will talk about one of the main limitations of quantum computers, explain why that is, and look at some real research examples for advances in quantum computation architectures.