In applying the techniques of AI and machine learning to big data, organizations tend to analyze that data that they have readily available, assuming that it can be used to model the future. However, we will show that this is a highly dangerous assumption, and that organizations already possess far more powerful data. We will describe how the ‘latent variables’ hidden within call recordings, email records, social media and video can be extracted and used to develop models that are far more predictive because they identify personality, the root of motivation. We will also discuss why this is important and how companies can exploit this data towards actionable outcomes.
Topics covered will include:
· Techniques for extracting latent variables: neuro linguistic pattern matching, kinematics, tonal analysis
· Products of these latent variables: personality, emotion, thinking style, behavioural intent
· Applications of these products in real world situations.
Stuart runs one of the most innovative AI companies in the world, pioneering a new class of AI, “Behavioural AI”. This isn’t exactly surprising based on his history. Having studied Managerial Sciences in university, specifically aspect of Managerial Accounting relating to Behavioural Economics and Organizational Behaviour, Stuart went on to start an early Management Information Services company which, in the late 1980’s, ran a private network for mail and file transfer between Toronto, New York and Mexico City. Selling that company, Stuart founded one of Canada’s first Digital Agencies – before the Internet was a household thing! That company was sold in 1999 to WPP and Stuart transitioned into Finance, creating an innovative sub-prime lender, which built a substantial automotive loan portfolio which was sold shortly before the financial crisis of 2007. After that Stuart once again got interested in Technology, and specifically the early stages of AI. Today you will find him working with some of the world’s largest companies, designing and implementing AI that changes the way they understand their Big Data.
Richard came to Big Data as a user; running data intensive departments, credit portfolio management and then performance management and compensation, for Canada’s largest bank. Since 2015 he has worked with IMC to bring AI solutions to companies that change the way they think about and use their Big Data.
Before all this he worked in consulting with BCG and in financial services in the UK and Canada. Richard has degrees from the University of Oxford and INSEAD.
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