Neil Lawrence: Deploying Machine Learning: Intellectual Debt and AutoAI

Detailed notes from this talk can be found on Neil's website here: http://inverseprobability.com/talks/notes/deploying-machine-learning-systems-intellectual-debt-and-auto-ai.html

Speaker: Neil Lawrence (University of Cambridge, Alan Turing Institute)

Title: Deploying Machine Learning: Intellectual Debt and AutoAI

Abstract: From the dawn of cybernetics, and across the last eight decades, we’ve worked to make machine learning methods successful. But now that these methods are being widely adopted we need to deal with the consequences of success. Many of those consequences can only be understood when a holistic approach to the machine learning problem is considered: the deployment of a method within a context for a particular objective. In this circumstance, it’s easy to see that questions of interpretability, fairness and transparency are all contextual. In this talk we summarize this challenge using Jonathan Zittrain’s term of “intellectual debt”, we discuss how it pans out in reality and how this challenge could be addressed using machine learning techniques to give us “Auto AI”.

This work is sponsored by an ATI Senior AI Fellowship.

Bio: Neil Lawrence is the inaugural DeepMind Professor of Machine Learning. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the physical world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.