By David Pescovitz, Thu Apr 28 11:00:00 EEST 2005
MIT researcher Nathan Eagle is helping mobile phones get to know you better than you know yourself.
How much does your phone know about your life? Perhaps enough to predict your behavior and even match you up with a mate, says Nathan Eagle, a graduate student at the MIT Media Lab. As part of the Reality Mining project, Eagle has collected approximately 40 years of continuous data on human behavior by capturing communication, proximity, location and activity information from 100 cell phone-wielding subjects at his school. Hidden in that data are insights about complex social systems that could affect our relationships with our mobile phones and each other.
TheFeature: What is Reality Mining?
Eagle: We're at a point where we have the potential to capture an enormous amount of data on everyday human life, on the reality of our behaviors. Traditionally, that kind of data is collected through self-reported surveys or sociologists' observations. But now we can gather much more objective data on human behavior.
TheFeature: How do you gather the data?
Eagle: I primarily look at mobile phone data that can be broken down into three types: location, communication and proximity patterns. We use cell tower IDs to get approximate locations within a few blocks. Communication logs reveal who is calling and texting whom and how often. And Bluetooth scans every five minutes show who is proximate to you.
TheFeature: What new functionalities are enabled by this data?
Eagle: We can do behavior prediction. Depending on the life you lead, I can predict what you're going to do next based on very limited information. Whether it's your morning Starbucks fix or your Saturday afternoon softball game, everyone lives life in routines. One of our algorithms extracts these routine patterns from everyone's daily lives.
TheFeature: What makes someone's daily routines particularly interesting though?
Eagle: There has been a lot of work on building more user-centric interfaces. So the kind of data we gather could automatically change the phone functionality according to a certain demographic. For example, Nokia, one of the sponsors of this research, is selling the same phone to soccer moms, power executives and texting teenagers. With just a few days worth of data, we can characterize the user and their usage. Once we do that, we can customize how the phone looks and operates for specific groups of people.
TheFeature: In what way?
Eagle: Here's a basic example: Out of 100 MIT students in my study, about 30 of them use the phone's clock as an alarm every day. When you first get the phone, the clock functionality is 10 keystrokes down into the user interface. Several users have figured out how to bring this functionality to the front, but the rest of these MIT students have not. So every day, they spend the ten keystrokes to set the alarm. By looking at the usage pattern, the phone could assign a single button to the alarm clock.
TheFeature: How does your application Serendipity leverage Reality Mining data?
Eagle: We're uncovering affiliations between people. I have a similarity metric based on distance in behavior states. The end idea is that the software would notice, say, that you typically hang out at the B-Side Lounge on Friday nights. So do I and perhaps you also do other behaviors similar to me. Those things in common may mean that we would want to be introduced. That's one method of matchmaking. Another is based on proximity. The Bluetooth addresses of those people running our client get pushed to our server. Then we do a comparison based on their profiles.
TheFeature: It sounds like Friendster for the physical world?
Eagle: That's the general idea. But Serendipity is based not just on explicit user profiles (that you enter) but also implicit behavioral information.
TheFeature: So even if I don't explicitly state in my profile that I go to a particular club whenever a certain DJ is performing, the system would know that anyway?
Eagle: Yes. And that would be weighted when matching you with other people. Of course, it might not tell you all of the reasons why you're being introduced to someone due to the privacy implications.
TheFeature: Serendipity seems like it could be mired in privacy issues.
Eagle: There's certainly a lot to talk about. When we were designing the system, we talked with Match.com about the right way to pull something like this off in a way that would keep people safe and not freak them out. To avoid profile spoofing, we felt that there's a need for a trusted third party to mediate the introductions. That way, I wouldn't be able to see anything about who a nearby girl is or what she's looking for. There would just be anonymous MAC addresses that get sent to our server. Then you might get offered an introduction or not depending on the settings. But I do like the idea of potentially broadcasting some of this information. I think of it as a proximity Web page. Men might have the pages -- I have no problem revealing my name or interests -- and women could go into a bar and do a social scan and potentially meet someone.
TheFeature: What's your next step?
Eagle: As a researcher, I have hundreds of thousands of hours of continuous human behavior and I've just scratched the surface analyzing it. This data set was never available before, so I'm going to collaborate with a variety of different disciplines -- computer scientists, physicists, social scientists, people who are interested in organizational behavior and chaos theory and others. I've also launched a startup, SenseSix, to commercialize Serendipty. We're developing a client that will work on many more mobile devices. Hopefully, we'll have our first product out by the end of summer.