The widespread success of deep learning in a variety of domains is being hailed as a new revolution in artificial intelligence. It has taken 20 years to go from defeating Kasparov at Chess to Lee Sedol at Go. But what have the real advances been across this time? The fundamental change has been in terms of data availability and compute availability. The underlying technology has not changed much in the last 20 years. So what does that mean for areas like medicine and health? Significant challenges remain, improving the data efficiency of these algorithms and retaining the balance between individual privacy and predictive power of the models. In this talk we will review these challenges and propose some ways forward.
Neil Lawrence is a Professor of Machine Learning and Computational Biology at the University of Sheffield. His main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular focus on applications in personalized health and applications in the developing world. He is well known for his work with Gaussian processes, and has proposed Gaussian process variants of many of the succesful deep learning architectures. He is highly active in the machine learning community, most recently Program Chairing the NIPS conference in 2014 and General Chairing (alongside Corinna Cortes) in 2015.