I’ve got a pet project: I want to create a robot that goes to meetings for me.
I’m talking about lousy meetings. Purpose-less meetings. Meetings where in theory, I’m showing up to maintain good relationships with my colleagues – but, really, I get antsy, my resentment spill over...
Look, If I could create a machine to go smile and nod and make appropriate, basic responses, I could get more done AND be a better colleague. And if I could market it, I’d make a zillion dollars.
If anybody can advise me on whether – and how – this could work, it's Kristian Hammond. He’s a computer-science professor at Northwestern University, and he’s been studying artificial intelligence for more than 20 years. And at least two of the projects he’s worked on make him perfect for this robot thing.
One of them is a company called Narrative Science, where Hammond is Chief Technology Officer. They’ve been around a couple of years, and they’ve gotten a lot of attention for building software that writes — well, generates — stories, including news stories, based on data.
Like recap stories for Little League games, for example. Parents use an iPhone app to enter all the box-score data, and the software kicks out a writeup you can email to grandma.
The computer experts at Narrative Science work with journalists — and they develop detailed templates for stories, including ways to mine the box score data, to identify: “What’s the main idea here? What’s the lede?”
Here’s how one sample story begins: Cole Benner did all he could to give Hamilton A's-Forcini a boost, but it wasn't enough to get past the Manalapan Braves Red.
Narrative Science also trains its software to pick key moments to highlight, like the following: The inning got off to a hot start when Bullen singled, bringing home Cappola.
These are all real examples, and as you can tell, the Narrative Science guys give their machine a vocabulary of distinctive sports-writer verbs. (Another sample: The Amelia Bulldogs had no answer for Ringland, who cruised on the rubber. What does that one even mean?)
Narrative Science produced more than 300,000 Little League stories last year. For this year, Hammond says they’re on track to produce 2 million.
They also play in the big leagues: Narrative Science software writes quarterly-earnings previews stories for forbes.com.
(For example: Wall Street is high on Nike (NKE), expecting it to report earnings that are up 10.5% from a year ago when it reports its fourth quarter earnings on Thursday, June 28, 2012.)
Hey, wait: Does this thing already replace journalists?
Not exactly. As Hammond pointed out when we talked, no editor is going to pay a human reporter to cover a Little League game – “because how many people are going to read that story — twenty?” He said that covering stories that would otherwise be ignored is “emblematic” of their work in media.
“It's not as though companies are saying, ‘Oh, we're going to pull reporters or journalists off of a project,’” Hammond said. “It's that, 'We've never been able to do X. We've never been able to write earnings previews for ALL of the companies. We've never been able to cover all of the games.'”
Good. This is pretty close to what I want: It does something I don't think is worth my time. I don’t want to go cover a Little League game. Earnings-preview stories? They’re pretty dull too.
So that’s one of the projects that makes Hammond the guy to ask for advice on my robot.
The second one was a part of his core academic research — which is a big-picture inquiry into how people think – especially how they tell stories, and what makes stories compelling.
This project, called “Buzz,” trolled the blogosphere for compelling personal stories, then had two-dimensional cartoon avatars perform them like dramatic monologues. “Buzz” eventually went on display as an art installation with a grid of nine cartoon-avatar faces, taking turns to tell their stories. One would talk, and the others would turn to listen. That’s my meeting right there.
But another part of “Buzz” — finding the stories themselves — is also important for my project: My robot has to pay special attention when something interesting happens — maybe even alert me. How did Hammond and his colleagues train “Buzz” to identify interesting stories?
“It’s hundreds of cheap tricks,” Hammond said. “And none of them work well, but they work well in tandem. So we would look for excessive use of personal pronouns, words that had to do with relationships, emotionally impactful words.”
(For example, a sample story on the Buzz website begins this way: My husband and I got into a fight on Saturday night... and it ended in hugs and sorries. But now I’m feeling fragile.)
So: This guy’s got machines that can go someplace I don’t wanna go, pretend to listen, and actually pick out what might be interesting or important. I think we might be onto something.
What else could I get it to do? I sat down with Hammond to find out.
First request: I'd like it to be me – to speak in my voice, and, more importantly, learn how I behave in meetings. Maybe it would get to know me better than I know myself: Oh, Dan leans forward when this one topic gets mentioned. Or: When Dan is bored, he scratches himself in this funny way.
Hammond said this kind of thing is in the works. “Intel just announced that it's putting a huge effort into machine-learning of individual behaviors,” he said. “The example being, if you forget your keys, and you do so habitually, the system will learn to remind you to find your keys and remember where you put them.”
Oh, yes.This is perfect, we need this at my house, my wife needs this. (I want it to say, kindly: “You know, you were wearing that blue jacket yesterday. Have you looked in the pockets there?”)
“In the long run,” Hammond said, “The computer is supposed to be a device that helps us in all aspects of our life and in order to help somebody really well, you have to know them.”
An empathetic machine?
“A knowledgeable machine,” said Hammond. “I hesitate to say empathetic.”
Did that idea – an empathetic machine – maybe creep him out a little bit?
“No,” he said. “I work on artificial intelligence. From the beginning of humans being able to think, we've always wanted to know how that works. And artificial intelligence is just saying, we need to know how that works at a level at which we can turn it into an algorithm. But the entire field of psychology is devoted to understanding humans. But you would never look at psychology and say, ‘Wow! Psychology's creepy.’”
When I steered Hammond back to my selfish and mercenary project, I got a nice surprise:
“I think you should build it,” he said. “I really do. It's absolutely something that should be built.”
Yes! There was, of course, a word of caution:
“I gotta tell you,” Hammond said. “You miss enough of these meetings, and they're gonna stop inviting you.”
Well, that would be perfect, wouldn't it? And what a selling point: You don’t even have to buy the machine – just rent it until they stop inviting you to the meetings.
I am totally building this thing.