This series of blog posts is based on a talk the author presented at ODSC Europe 2019 alongside Ayub Hanif, VP at JPMorgan’s Quantitative and Derivatives Strategy. We’ll look at how recent developments in automated machine learning and interpretability can help market participants build, test, and understand powerful AI models that support and enhance their investment processes. But first, some context is needed. Part 1: Beyond the Market-Predicting Magic Box In which we conclude that the existence of this article is strong evidence that we don’t know how to use AI to predict asset prices, either, and try to get real.

DataRobot Intelligent Value Estimator for 2020 MLB Team and Player Performance Recently, we predicted how the 60-game 2020 MLB season would unfold -- for teams and for individual players. Please see our original post for all of the details of how we build the models with DataRobot’s leading artificial intelligence platform. Who is projected to win each division in the…

In our previous post, we talked about how red AI means adding computational power to “buy” more accurate models in machine learning, and especially in deep learning. We also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is. We…