Science
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 at Predata, and Predata is a company that using machine learning to look at the, the spectrum of human behavior online organizes it into useful signals about people's attention and we use those to influence how people make decisions by giving them a factor of what people are paying attention to. Because attention is a scarce cognitive resource. People tend to pay attention only to very important things, If they're about to act in a way that might cause problems for our potential clients, they'll, they'll spend a lot of time online doing research, making preparations, and by unlocking this attention dimension to web traffic, 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 talking about just to frame and put some details around how someone might use that service? Dakota: Absolutely. So one example that I find particularly useful for revealing how attention works online is looking at what soybean farmers did in response to a tariffs earlier this year. So knowing that the, they weren't going to get a very good price on soybeans at that particular moment. A lot of them were looking up how to store their grain online and purchasing these very long grain storage bags, purchasing some obscure scientific equipment needed to insert big needles into the bags to get a sample for testing the soybeans and moisture testing devices to make sure they wouldn't grow mold. And all of these webpages are things that tend to get very little traffic. And when we see an increase in traffic to all of them, at the same time, we know that a, a very influential group of individuals, namely farmers, is paying attention to this topic. 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 do you determine what is a useful bit of information in the context of what your clients are looking for? Do they kind of have an idea of what you're looking for and then you'd go out and search for that or, or does your algorithm find anomalies in the data and then characterize those anomalies so that you can then report that back? How does it work? Dakota: It’s a mix of both. Because the, the Internet is such a rich and complex 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