domingo, 30 de noviembre de 2014

MIT Media Lab’s Kevin Hu wants to turn the invisible visible


MIT Media Lab’s Kevin Hu wants to turn the invisible visible
Big Data, HCI, Media Lab, MIT, Visualization, 


Photo by Alan Savenor 

Last March, MIT Media Lab Grad student Kevin Hu and his colleague Amy Yu saw their Pantheon project land on the front cover of The New York Times Magazine. Not bad for two grad students who are part of a generation using today’s seemingly unlimited technological innovations to change the world.

Focused on human data interfaces, Hu and his colleagues at the MIT Media Lab are part of the Macro Connections Group led by Cesar A. Hildago, where their sole mantra is “transform data into knowledge.” One of his current projects, DIVE, automatically generates Web-based, interactive visualizations of structured data sets.

What I discovered when I sat down with Kevin is that all those maps we now explore as click-bait on BuzzFeed, The Atlantic, and The Week explaining everything from the conflict in the Middle East to which states are more adulterous, are only going to become more ubiquitous if Kevin gets his way.

DIVE is a way to turn the invisible, visible. We want to democratize data visualization so that anyone with data can map an image that explains things,” said Hu. His goal is to remove the middlemen who interpret data for us.

But he and his classmates also want to know more about human emotions with another project called Quantify. They want a computer that, or should we say “who,” can feel out the squishy stuff. Can emotions be data? When Hu and fellow lab student, Travis Rich (also his roommate, is there any other way when you are 20-something?) invented Gif Gif, the theory of Quantify took shape. I sat down with Kevin to learn how he plans to change the way we live and digest information.

What happened with Pantheon after the NY Times Magazine cover?
When the cover hit, we got a lot of attention. We had a couple hundred thousand page views. Amy Yu led the project and I joined. We ran into a lot of controversy because people, and The New York Times, focused on the rankings rather than our goal of cultural production over time. We were less interested if a celebrity were number five or six but rather the aggregate: How many physicists have changed over time, what is our country’s cultural composition? Instead we had angry e-mails from Canadians asking why Avril Lavigne was above Frank Gehry.

The real point of the project was to see cultural production and how it changes over time. We think of cultural production, in the broadest sense, as information that’s transmitted by nongenetic means, like what we’re doing right now. Anything that’s not encoded in our DNA: The shoes we wear, the coffee we drink, and the language we speak. We consider that all to be culture and we proxy it by people.

What’s on your desk today?
DIVE. It’s trying to make data visualization accessible. It’s trying to democratize the use of data visualization, like the charts you see in The New York Times. One of the real powers of Pantheon was that anyone could look at this tree map or scatter plot or diagram and understand the story being told. The trouble is it takes a long time to build this tool. The New York Times has great interactive visualizations but they have a whole team dedicated to it.

DIVE is a way that people can automatically build visualizations, allowing a journalist to easily imbed a data-driven graphic, or an educator or researcher to easily build a visual tool.

How would you define the challenge of data visualization?
The fundamental problem is that we’re trying to translate between three worlds: 

  • The world of information: Bits
  • The world of knowledge: Neurons and cells; and 
  • the world of visualization: Pixels
Until we all are cyborgs and can plug in this data and automatically get what we need, we will need pixels. Data visualization is entirely concerned with how we represent these bits in terms of pixels on a 2-D screen. How do we turn the invisible to the visible?

What drew you to explore macro-connections?
I was studying physics but I was kind of frustrated with the current research scope of physics. It seems to me that people are concerned with either what’s very big (cosmology and astrophysics), or very small (high energy physics). But I was interested in learning how to understand everyday phenomena.

I was interested in looking at people who are applying physics to social problems and to things that we do not understand such as organizational structures, social dynamics, and the spread of epidemics.

What do you see that we don’t? How do you apply physics to social structures and problems?
I think it’s mostly a cultural thing. For the longest time social sciences could only tell us how we can think about a problem, not how we can actually solve it. But now we actually have the data to solve the problem. That’s very frightening but it’s very powerful and it’s a very new phenomenon.

Ten years ago, we didn’t have the data to create things like Pantheon. Now we have Facebook and OK Cupid that have great data logs. For the first time, we have actual data about self-identity. We now have how we view ourselves and how we view others. Physics is all about modeling these phenomena.

So these ‘squishy’ social things start to seem more linear?
Yes, exactly. Exactly.

How will DIVE change our lives? And can you already see it taking place?
I can. Imagine if journalists could use data visualization in their articles. Imagine if consultants could use it. The pipe dream is that in the future we have a completely data-literate society where when we talk about policies or about disaster relief, we have real-time, high-resolution, clean data sets and anybody has the ability to think about social issues rigorously.

For many issues, we see them through another person’s interpretation. A great deal of science reporting, for instance, is very second and third hand. Very few people actually read the research paper. Data visualization allows everyone to understand issues. DIVE is trying to close that gap such that when we acquire information about the world, we can get it first hand and we can mine it ourselves.

You sent me a test called Place Pulse before this interview. Why? 
Travis and I had this vision that we want to give computers the capability to reason about objects the way that we do. When computers think of gifs, they think of bits. When we think of gifs or videos, we think of their content. We may think of this video as being very emotionally compelling or this picture being very angry. That’s how we may think of an image but that’s not how a computer does.

Are you’re trying to give the computer emotions?
Yes, to give it the capability to think of media emotionally. It can reason very well, better than humans for anything that’s very computational and linear. But when we try to attach emotional intelligence to computers, we are not yet there. A computer cannot yet measure that this atrium is very clean, but a human can. We need a human in the loop.

We’ve built this comparison tool off the Quantify platform. You can imagine a whole list of comparable media that we can better measure if only we had the tools. How useful would it be if you could search Netflix this way? Or compare articles of clothing and know which one looks better on you or which one is more acceptable? Or compare experiences and know which one is more painful? Quantify allows this.

How do you convince people to give you that data?
We made it fun. Travis and I also built Gif Gif last March and it inspired Quantify. We have two million votes already. People like viewing gifs and contributing to knowledge but furthermore, we can give you a sense of what you like. What is your emotional profile? How did you vote in comparison to others? That makes it more interesting to share.

Who uses Gif Gif now?
People from all over the world use it. I’d say that the demographics are probably mostly teenagers because, really, who’s voting on gifs at 2 p.m. on Tuesday?

There is also a display in the lab called Mirror Mirror linked to Gif Gif. It’s a mirror with a webcam that uses facial recognition to measure emotions and it gives you back a gif. People love it and we didn’t expect people to love it, but it turns out that five-year-old children touring the lab and 60-year-old executives are all in front of it trying to say hi or trying to be angry.

What public opinion would you like to change?
I’d like to change the public’s opinion about experimentation. Human experimentation is an incredibly loaded term, for many very good reasons, and when Facebook said that they were experimenting with people’s newsfeeds, there was outrage. I think it’s kind of absurd. This is how software companies make tools. They test on their users and provide a service for free, and in exchange they use your data set. Clearly, if they give it to the wrong people, there’s potential for evil and abuse. I would like to see people be open and accepting of the fact that by contributing a little bit of anonymous information, they can help scientists better understand bigger issues like information flow, social network formation. I think that that should definitely change.

Why is the Media Lab so illustrious?
What I really look forward to every morning is the conversations I have with the people here in the lab. Gif Gif and Pantheon and DIVE – all those ideas really merged organically and there’s no real source: they all kind of came from the network and from conversations. A lot of people imagine people at the lab as people off in the air dreaming about what the next big thing will be, but really it’s just regular people having conversations and they happen to be asking, ‘what could be impactful?’ We’re aiming towards more paradigm shifts than incremental research. Is this going to be a game changer? Most of the time, the answer is ‘no,’ by definition, but it’s nice to be in a place where that is one of the first questions.

How do you keep going when things get tough on a project?
I’m taking this class at the lab called Tools For Wellbeing, as there’s a big initiative here about wellbeing, especially since MIT isn’t doing so well in that category. Pattie Maes actually teaches it sometimes. Last week’s subject was reframing. How do I reframe the situation? My answer to your question would definitely be to reframe it. Let’s say I’m trying to make this product but a feature isn’t working out. Well, one, can we design around that? Two, can we make do without it?

You dropped out of high school to go to Simon’s Rock School. How was that for you?
Simon’s Rock was probably the most formative two years of my life. It’s 300 kids in the Berkshires in the middle of nowhere in a pretty high-stress academic environment. It was very formative for me and I would do it again, but I don’t know if I’d enjoy it that much.

It’s considered an ‘early college.’ When I transferred to MIT from there, they accepted most of my Simon’s Rock credits.

Where do you get your news?
My media diet is 

  • one third Twitter and Facebook, 
  • one third very specific news sites that I like such as The New Yorker, New York Times, Huffington Post – the classic ones – The Economist – that sort of stuff – and 
  • then one third Reddit.
What event are you looking forward to?
I’m looking forward to the MIT Media Lab’s Spring Members’ Meeting, which is sometime in April. During this meeting, lab sponsors (companies) come by for three days for research demos and updates. I’d love to get member’s feedback on DIVE, FOLD, and QUANTIFY when they’re further along, since outsiders are always candid with their comments and needs.

Secret source?
It’s a lame answer but McDonald’s. I’m a huge McDonald’s fan.

What do you order?
Fries and McFlurry. I grew up with McDonald’s and Lunchables. I try to eat healthier now but that’s definitely my go to. I go there at least twice a week.

That stuff’s poison!
It’s true but it’s too good.

Heidi Legg interviews visionaries and thinkers around us at TheEditorial.com Follow Heidi on Twitter - Facebook


















ORIGINAL: Beta Boston


By Heidi Legg

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