- Louis Anthony “Tony” Cox (PhD & SM, MIT; AB, Harvard): Associate Professor of Business Analytics, Business School, University of Colorado, Denver (https://business.ucdenver.edu/tony-cox); Co-founder & Chief AI Scientist, MoirAI (https://moirai-solutions.com); Editor-in-Chief (EIC) of Risk Analysis journal; Area Editor of Real-World Applications of Journal of Heuristics; Elected member of the National Academy of Engineering (2012 – Present);
- Anthony Bak (PhD, UPenn): Head of AI Implementation, Palantir Technologies: https://www.linkedin.com/in/bakanthony/
- Emilio Lapiello: AI leader and partner of BCG GAMMA and the Marketing, Sales & Pricing and Consumer practices at Boston Consulting Group: https://www.bcg.com/about/people/experts/emilio-lapiello
- Moderated by: Ryan Lichtenwalter, Manager of Data Science Team, Transamerica
* * * * *
July 29, 2021: Dr. Tony Cox, MoirAI’s Co-founder and Chief AI Scientist, was invited to deliver AI tech talks for Target and Fox News Corporations.
Topic: Causal Artificial Intelligence (CAI) for Reliable Business Forecasting Despite New and Rapidly Changing Conditions
How well can human economic behaviors – customer purchases, viewing and responding to ads, searching for information on products, recommending channels and products to others – be predicted and controlled my marketing initiatives under novel, disruptive, and rapidly changing conditions, such as COVID-19 in 2020 or accelerating inflation in 2021? The current state-of-the-art in artificial intelligence (AI), machine learning (ML), algorithmic marketing, and advanced data analytics relies heavily on discovery (“learning”) of stable predictive patterns and statistical associations in data. This works well when the world represented data changes only slowly, so that patterns persist long enough to be learned and used. It works poorly, or not at all, when the conditions that generate data are changing quickly or have never been seen before (novelty). Current predictive analytics technology is unreliable for guiding novel interventions and business decisions when little or no relevant past data are available. Under these conditions, causal artificial intelligence (CAI) can accomplish what conventional AI/ML and predictive analytics cannot: trustworthy prediction, and characterization of remaining uncertainties, in the absence of directly relevant past experience and training data. It does so by using stable causal knowledge and generalizations from multiple diverse data sets to predict the changes in outcome probabilities (or occurrence rates) caused by alternative actions and conditions, even if they have not been encountered before. This talk discusses key ideas and recent breakthroughs in CAI that allow useful forecasts to be developed in situations with realistically uncertain and changing conditions. Many commentators believe that CAI is becoming the next revolution in applied AI/ML; we explain why, and how this technology can be used today, as well as how it is likely to evolve in the next few years, to yield dramatically more robust, adaptive, and “intelligent” marketing decisions in a rapidly changing world.
* * * * *
July 30, 2020: Dr. Tony Cox, MoirAI’s Co-founder and Chief AI Scientist, was invited to deliver a keynote speech on AI for financial risks at the HSBC Global Risk AI Forum.
Tony’s presentation was joined by 35 AI experts from all over the world.
* * * * *
Feb 21, 2020: Dr. Tony Cox, MoirAI’s Co-founder and Chief AI Scientist, was invited to deliver a keynote lecture on Causal Analytics, the NEXT big thing in AI at the Independent Institute (one of the world’s top think tanks) in Oakland, California.
* * * * *
