Jeremiah Lowin – Machine Learning in Investing

**Patrick O'Shaughnessy** (0:04)
Hello, and welcome everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com.

**SPEAKER_3** (0:24)
Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaughnessy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.
Clients of O'Shaughnessy Asset Management may maintain positions in the securities discussed in this podcast.

**Patrick O'Shaughnessy** (0:49)
My guest this week is one of my best and oldest friends, Jeremiah Lowin. Jeremiah has had a fascinating career starting with advanced work in statistics before moving into risk management in the hedge fund world. Through his career, he has studied data, risk, stats, and machine learning, the last of which is the topic of our conversation today. He has now left the world of finance to found a company called Prefect, which is a framework for building data infrastructure. Prefect was inspired by observing frictions between data scientists and data engineers and solves these problems with a functional API for defining and executing data workflows. These problems, while wonky, are ones I can relate to working in the quantitative investing world, and others that suffer from them out there will be nodding their heads right now. In full and fair disclosure, both me and my family are investors in Jeremiah's business. You won't have to worry about that potential conflict of interest in today's conversation though, because our focus is on the deployment of machine learning technologies in the realm of investing. What I love about talking to Jeremiah is that he is both an optimist and a skeptic. He loves working with new statistical learning technologies, but often thinks they are overhyped or entirely unsuited to the tasks they are being used for.
We get into some deep detail on how tests are set up in this world, the importance of data, and how the minimization of error is a guiding light in machine learning and perhaps all of human learning too. Let's dive in.
Where we will start then is really with a question about what these models or methods are useful for and what they are not useful for.
And the first time we talked, you used this idea of this is just souped up linear regression in a lot of interesting ways, but maybe we'll just begin there with machine learning is an exciting set of tools. What should people think about when deciding whether or not these are even appropriate things to consider?

**Jeremiah Lowin** (2:29)
Yeah, it's a great question. It is an exciting set of tools. I personally am so excited about them. I once just cold turkey quit a job to go learn about this stuff, but it's very, very easy to end up with a chainsaw when all you need is a butter knife.
And that is sort of I don't think people realize sometimes when they've ended up with a chainsaw, but that is the danger. That's what we're looking out for. And so I was a little bit tongue-in-cheek when I said it's all just souped up linear regression. But if you actually look at the math that's taking place, building an AI model is easy. It's just layering a bunch of regression models on top of each other with a little bit of finessing. It's training it that's really hard and where you could spend multiple careers and multiple lifetimes gaining expertise.
But building it and putting together is really easy. And that's sort of why you end up with a much more complicated tool than you probably thought you were getting or frankly even need.

**Patrick O'Shaughnessy** (3:19)
So what types of problems from starting very simply is this appropriate for? Maybe you can talk about classification versus regression and things like that.
Why is everyone so excited about this? What are the main problems?

**Jeremiah Lowin** (3:31)
It's very important, I think, to understand when we talk about AI, what are we really talking about? We tend to look at these things as if they're black boxes and to some degree they are. But the truth is we know what's happening in that black box with some certainty. And AIs are really good at only one thing, which is discovering complex correlations in data. In human terms, that's something we call experience.
And so the table stakes for having an effective AI is doing something that requires experience.
And there's a subtext here, which is that AIs are really dumb.