Developing Confidence in Model Explainability and Adversarial Examples

"How do you come to a rational conclusion as to what a company is worth?"  A seemingly simple question with little-to-no clear answer.

For John Alberg, a background in computer science and a passion for machine learning led him to view the problem through the lens of data.  "If it is true that you can use publicly available information to buy companies for less than their economic worth," he thought, "then you should be able to see it in the data."

And thus was born Euclidean, an investment firm that marries machine learning with a deep value mentality.

Our conversation spanned more than 2.5 hours and covered everything from the basics of machine learning, to the evolution of Euclidean's approach over the last decade, to the implications of adversarial examples in neural networks.

This podcast, an abridged version of our conversation, picks up the thread mid-way through, where I have asked John to expand upon his experience with his startup, Employease, and how it influenced his value-based thinking at Euclidean.

I hope you enjoy.