Tag: New York Times R&D Group

New York Times Research and Development Group

Questions

1)Much of the work done at NYT labs seems to share a common thread of socially conscious design. Can you speak to the significance of transparency in your design process?

2)In your experience working with NYT labs do you feel that is important to have a single expertise or to have a more generalized set of skills?

3)A lot of your work is open-source. Can you speak about the lab’s interest in making these tools available to the public?


Visit

During our first visit in New York City we toured the New York Times Research & Development Group’s office. There we met with Noah Feehan who introduced us to the space, as well as some examples of the work he and his colleagues do for the Times. Noah explained that the Research & Development Group deals with imagining trends and technologies that will emerge within the next three to five years. Noah first showed us the Kepler project, a visualization of metadata published by The Times since 1913. Kepler ambiently displays connected topics of interest written by the publication and combines each topic with relevant data referencing the audience’s activity on The Times website and Twitter.

Noah Feehan at NYT labs

Noah Feehan demoing Kepler

 

Noah then walked us through the office resting first to speak about the Cascade project. Cascade is a realtime visualization of readers viewing The Times web or mobile sites. This project helps The Times recognize and understand pathways in which their readership are taking through their online portals.

 

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Project Cascade – Photographed by Karina Kurmanbayeva

 

We then moved on to briefly speak about other tools that the Research & Development group have actually created to assist their own work. One example Noah showed us was Streamtools, an open-source toolkit for working with live streams of data.

streamtools-big

Streamtools interface

 

Noah then showed us The Listening Table, a very different kind of project from the rest we had just viewed. The Listening Table is a semantic listening device that looks to explore the relationship between recorded data and real human understanding.

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Noah demos The Listening Table

 

Lastly, Noah showed us project Madison, a crowd-sourced archive of advertisements that have appeared in The Times. This project explores collaborative publishing and how human archival techniques compares to digital archival tools.

Reflections

One important theme that stood out to me when touring the New York Times Research & Development Group’s office was the social consciousness evident in their work. Each project not only considered what the emerging technologies are but also how these technologies will be embodied in our day to day lives. There was an emphasis and understanding of the human element in each project that I really appreciated. As Noah Feehan discussed we are already recording data signals in many ways but it is important moving forward to change the way we listen to those signals, and what meaning truly lies within them. This visit in particular really inspired me to think more critically about my design process and how it can become more transparent and socially conscious.

Links

http://nytlabs.com/

http://makezine.com/2015/03/11/new-tech-times/

http://blog.nytlabs.com/2015/03/20/video-recent-projects-and-research/

New York Times

New York Times Research and Development

How do you gain readers?

How do you sustain your relationship with current readers?

How do you deal with the digital age as a newspaper company?
Noah Freehan was our guide on this tour of projects he had been working on and helped develop at New York Times R & D lab. The first project he showed us was a map of semantics accessed from the Times’ articling system. Noah told us how these articles were important because they are a slice of history recorded from the last 120 years. These articles don’t only contain facts from the past but also capture the culture at the time such as language used, topics and issues discussed. The map connected the articles through topics and tags developed for each topic and article. The purpose of this map was to examine the audience and determine the areas that most interested them. This kind of data representation used in this way is a tool for the New York Times to interpret the interests of the audience using the articling tag system.

The R & D department had several other projects which processed data from the audience who accessed the Times daily articles. These projects experimented with different ways of collecting this data and also visually representing it. One global model compiled user data such as rss feeds, and their location to represent the audience and what topic they were looking at on the NY Times website. This model had three modes which represented information in different ways. A few of these projects tracked buzzwords that users would use or search for to signal a trend in interest. This is how the company obtained the data to stay relevant to today’s society.

I was very impressed with the Times’ innovative efforts to identify and capture their audience using data collected electronically. This answer my questions about how an old established newspaper company is adapting to the digital age and obsolete qualities of the newspaper. It turns out that the New York Times had actually harnessed the power of the internet and data together to maintain their success. When I first heard that we were visiting New York Times I was skeptical, though their innovative ways of using and gathering data and also preserving data proves them as a worthy business leader in the future.

Noah showed us a few experimental projects. One was the Sound Table, which had a central microphone to record sound, and several touch sensitive strips on the surface which when touched, triggered a 60 second recording of the previous 30 seconds of conversation and the proceeding 30 seconds of conversation. This Sound Table displayed the text results on a monitor in normal conversation form. What was really interesting was the way the computer algorithm worked to capture buzzwords that the user said based on how important the particular word seemed in the context of the sentence. This AI almost replicates human recognition of language. These “watchwords” were underlined in bold to signal some kind of significance for the user to interpret.

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