Building AI to Better Understand & Respond to Your Customers
Businesses are talking to their customers more and more. Every customer conversation is an opportunity to directly engage with your customer, to recommend them products, to better understand them and the user's experience. But supporting these conversations and extracting insights from them is expensive and difficult. re:infer provides a solution powered by the latest advances in AI that does the heavy lifting to help businesses better understand and interact with their customers. Using Machine Learning to understand customer conversations is hard. More often than not, customers use informal, poorly spelt and ambiguous language. Because of this, traditional computer science techniques that use hand coded rules fail. And since most businesses have insufficient training data, traditional machine learning methods do not work in this context either. To solve this problem we take a different approach. In this talk I'll describe the business problem we're solving, the engineering constraints it imposes and how we've built a novel, deep learning, natural language processing system to solve it.
AI and machine learning has dominated my entire professional career — I’ve been working in this space for over 10 years. I gained my PhD and MSc in AI from the research groups at UCL and Edinburgh. My research has been published in the leading AI journals and conferences including NIPs, AISTATs, Neural Computation, the Journal of Machine Learning Research and NeuroImage. Outside of academia and before re:infer, I helped to build production AI technologies for problems in search, ad-tech, consumer intent modelling and finance.