Don’t be surprised if Stephen Wolfram, the renowned complexity theorist, software company CEO, and night owl, wants to schedule a work call with you at 9 p.m. In fact, after a decade of logging every phone call he makes, Wolfram knows the exact probability he’ll be on the phone with someone at that time: 39 percent.
Wolfram, a British-born physicist who earned a doctorate at age 20, is obsessed with data and the rules that explain it. He is the creator of the software Mathematica and of Wolfram Alpha, the nerdy “computational knowledge engine” that can tell you the distance to the moon right now, in units including light-seconds.
Now Wolfram wants to apply the same techniques to people’s personal data, an idea he calls “personal analytics.” He started with himself. In a blog post last year, Wolfram disclosed and analyzed a detailed record of his life stretching back three decades, including documents, hundreds of thousands of e-mails, and 10 years of computer keystrokes, a tally of which is e-mailed to him each morning so he can track his productivity the day before.
Last year, his company released its first consumer product in this vein, called Personal Analytics for Facebook. In under a minute, the software generates a detailed study of a person’s relationships and behavior on the site. My own report was revealing enough. It told me which friend lives at the highest latitude (Wicklow, Ireland) and the lowest (Brisbane, Australia), the percentage who are married (76.7 percent), and everyone’s local time. More of my friends are Scorpios than any other sign of the zodiac.
It looks just like a dashboard for your life, which Wolfram says is exactly the point. In a phone call that was recorded and whose start and stop time was entered into Wolfram’s life log, he discussed why personal analytics will make people more efficient at work and in their personal lives.
What do you typically record about yourself?
E-mails, documents, and normally, if I was in front of my computer, it would be recording keystrokes. I have a motion sensor for the room that records when I pace up and down. Also a pedometer, and I am trying to get an eye-tracking system set up, but I haven’t done that yet. Oh, and I’ve been wearing a sensor to measure my posture.
Do you think that you’re the most quantified person on the planet?
I couldn’t imagine that that was the case until maybe a year ago, when I collected together a bunch of this data and wrote a blog post on it. I was expecting that there would be people who would come forward and say, “Gosh, I’ve got way more than you.” But nobody’s come forward. I think by default that may mean I’m it, so to speak.
You coined this term “personal analytics.” What does it mean?
There’s organizational analytics, which is looking at an organization and trying to understand what the data says about its operation. Personal analytics is what you can figure out applying analytics to the person, to understand the operation of the person.
Why have you been analyzing Facebook data?
We are trying to feel out the market for personal analytics. Most people are not recording all their keystrokes like I am. But the one thing they are doing is leaving lots of digital trails, including on Facebook, and that is one of the pieces we’ve been experimenting with.
We’ve accumulated a lot of Facebook data—you’re seeing the story of people’s lives, played out in the level of data. You can see relationship status as a function of age, or the evolution of the clustering of friends at different ages. It’s really quite fascinating to see how all this stuff is just right there in the data.
Isn’t a lot of what you find kind of obvious? Like friends from college aren’t connected to the ones from grammar school?
Yes, but then you get a case where the data analysis is buggy. You get some curve, and your reaction is, “Oh, yeah, I understand why the curve is that way, I’ve got an argument for it.” But then, oops, there was a bug in the analysis and actually the curve is something different. That reminds you things aren’t quite so obvious. If you actually measure it, that’s doing science.
What’s the connection to the search engine you built?
Right now Wolfram Alpha is strong on public knowledge: accumulating and searching the knowledge of the civilization. But what you have to do in personal analytics is try to accumulate the knowledge of a person’s life. Then the two can actually be integrated, and I’ll give a kind of silly example. You might ask: “Who do I know that can go out into their backyard and go and look at the night sky right now?” For that you have to be able to compute who is in nighttime, who doesn’t have cloudy weather, and things like this. And we can compute all that stuff.
What do you see as the big applications in personal analytics?
Augmented memory is going to be very important. I’ve been spoiled because for years I’ve had the ability to search my e-mail and all my other records. I’ve been the CEO of the same company for 25 years, and so I never changed jobs and lost my data. That’s something that I think people will just come to expect. Pure memory augmentation is probably the first step.
The next is preëmptive information delivery. That means knowing enough about people’s history to know what they’re going to care about. Imagine someone is reading a newspaper article, and we know there is a person mentioned in it that they went to high school with, and so we can flag it. I think that’s the sort of thing it’s possible to dramatically automate and make more efficient.
Then there will be a certain segment of the population that will be into the self-improvement side of things, using analytics to learn about ourselves. Because we may have a vague sense about something, but when the pattern is explicit, we can decide, “Do we like that behavior, do we not?” Very early on, back in the 1990s, when I first analyzed my e-mail archive, I learned that a lot of e-mail threads at my company would, by a certain time of day, just resolve themselves. That was a useful thing to know, because if I jumped in too early I was just wasting my time.
What technologies are needed to do personal analytics at a large scale?
It’s data science and the whole cluster of technologies that come with that. Then it’s having computational knowledge about the world and being able to make queries in natural language. Then you need to sense things about the world, whether it’s with sensors or being able to do visual recognition to know what one is seeing. Then the final thing is just all the plumbing infrastructure to get all of these devices to communicate and feed their information to a place where one can do analysis.
Where do you stand on commercializing these ideas?
The personal analytics of Facebook for Wolfram Alpha is a deployed project, and there will be more of those in the personal-analytics space. We think we can do terrific things, but you have to be able to get to the data. That has been the holdup. The data isn’t readily available. Recently we’ve been working with different companies to try and make sure we can connect their sensors to kind of a generic analytics platform, to take people’s data, move it to the cloud, and do analytics on it.
How much better do you think that people or organizations can become with some data feedback?
I think it will be fairly dramatic. It’s like asking how much more money can you make if you track your portfolio rather than just vaguely remembering what investments you made.
A quick guide to the most important AI law you’ve never heard of
The European Union is planning new legislation aimed at curbing the worst harms associated with artificial intelligence.
It will soon be easy for self-driving cars to hide in plain sight. We shouldn’t let them.
If they ever hit our roads for real, other drivers need to know exactly what they are.
This is the first image of the black hole at the center of our galaxy
The stunning image was made possible by linking eight existing radio observatories across the globe.
The gene-edited pig heart given to a dying patient was infected with a pig virus
The first transplant of a genetically-modified pig heart into a human may have ended prematurely because of a well-known—and avoidable—risk.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.