There have been some spectacular fails when it comes to looking at Internet traffic, think Google Flu Trends; however, Predata, a company that helps people understand global events and market moves by interpreting signals in Internet traffic, has honed human-in-the-loop machine learning to get to the bottom of geopolitical risk and price movement.Predata uncovers predictive behavior by applying machine learning techniques to online activity. The company has built the most comprehensive predictive analytics platform for geopolitical risk, enabling customers to discover, quantify and act on dynamic shifts in online behavior. The Predata platform provides users with quantitative measurements of digital concern and predictive indicators for different types of risk events for any given country or topic.Dakota Killpack: Over the past few years, we’ve have collected a very large annotated data set about human judgment for how relevant many, many pieces of web content are to various tasks. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.Let’s jump into our episode today with the director of Machine Learning at Predata.Dakota: My name is Dakota Killpack and I'm the director of machine learning atPredata, and Predata is a company that using machine learning to look at the,the spectrum of human behavior online organizes it into useful signals aboutpeople's attention and we use those to influence how people make decisions bygiving them a factor of what people are paying attention to. Because attentionis a scarce cognitive resource. People tend to pay attention only to veryimportant things, If they're about to act in a way that might cause problemsfor our potential clients, they'll, they'll spend a lot of time online doingresearch, making preparations, and by unlocking this attention dimension to webtraffic, we're able to give some unique insights to our clients.Curtis: Can we jump into maybe a concrete use case into what you're talkingabout just to frame and put some details around how someone might use thatservice?Dakota: Absolutely. So one example that I find particularly useful forrevealing how attention works online is looking at what soybean farmers did inresponse to a tariffs earlier this year. So knowing that the, they weren'tgoing to get a very good price on soybeans at that particular moment. A lot ofthem were looking up how to store their grain online and purchasing these verylong grain storage bags, purchasing some obscure scientific equipment needed toinsert big needles into the bags to get a sample for testing the soybeans andmoisture testing devices to make sure they wouldn't grow mold. And all of thesewebpages are things that tend to get very little traffic. And when we see anincrease in traffic to all of them, at the same time, we know that a, a veryinfluential group of individuals, namely farmers, is paying attention to thistopic. Using that we're able to give early warning to our clients.Curtis: Sounds like looking for needles in a haystack of data. Right? So how doyou determine what is a useful bit of information in the context of what yourclients are looking for? Do they kind of have an idea of what you're lookingfor and then you'd go out and search for that or, or does your algorithm findanomalies in the data and then characterize those anomalies so that you canthen report that back? How does it work?Dakota: It’s a mix of both. Because the, the Internet is such a rich andcomplex domain. It's, it's very dangerous to just look for anomalies at scale.There there've been some high profile failures, most notably the Google Flu Trends