In this episode we are discussing algorithms, how to democratize access to bespoke modeling, AI/ML. How mid cap and small cap companies can have access to their own algorithms and predictions, and what it would mean for us as individuals to have our own algorithms working for us. 
We are joined by Peter Cotton, PhD, the Chief Data Officer of Intech Investment Management, a multi billion hedge fund. He is also the Lead developer of the open-source prediction network, search "microprediction". Peter's a trained mathematician, competitive math olympiad and chess player. He talks about the need for comparable support for algorithms to humans, and how we need an "AR" department and "micromanagers", for our algorithms similar to how we humans have "HR departments". 
About Peter's background and amazing track record. He's developed modeling in financial services such as for closed form synthetic CDO pricing and led Morgan Stanley's efforts to create advanced correlation trading and risk management tools. Peter's led large scale data assimilation techniques in the Fixed Income markets and invented the industry's only real-time, curve-based, bond and CDS pricing service at Julius Finance Corporation, where they priced every bond and CDS every ten seconds. This predated, and gave rise to, BBG:BMRK pricing service. At J.P. Morgan Peter's pioneered the use of control theory for OTC trading. Created the privacy-preserving machine learning program. Created the ROAR crowdsourcing initiative which involved 1500 data scientists.

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