Recent blog posts by natematias

Six Emerging Forms of Digital Cooperation

At the Berkman Center here in Boston, Brian Keegan and I co-facilitate a working group on cooperation research (email list here) that meets to discuss recent papers, offer feedback on technology design, and share a broad conversation among reearchers, designers, activists, and mediamakers.

Today, I shared six cooperative technologies that I learned about at the Mozilla Festival in London:

Mozilla Lightbeam

Data Science VM: Set Up Your Server in Four Steps

Data Science VM is a script that automatically launches and configures a data science system on your computer or in the cloud in a half hour or less, across Linux, Windows, and OSX.

In my experience, any new machine for a serious project takes 3-5 days to set up. During my first semester at MIT, I spent weeks installing MediaCloud (it's easier now, I hear). I lost around 3 days each when my laptop was stolen in March of 2012, when my MacBook Pro died just before my thesis deadline, and when I started a summer internship at Microsoft. Setup time is also a major problem during hack days; I've attended too many events where the event ends just as the participants finish setting up their machines.

What It Includes

Society, Politics and the Algorithm: Social Science in the Lab

Kate Crawford introduces this session by reminding us that Technology is social and cultural. Microsoft's Social Media Collective looks at how social networks and social practices interrelate.

We're here at the Microsoft Research New England 5th anniversary symposium, where a fascinating collection of scholars are discussing computer science, big data, politics, society, and machine learning. This post was collaboratively created using NewsPad. I also wrote a post on a session from this morning: Progress and New Challenges in Machine Learning/Big Data

Progress and Challenges in Machine Vision, Computational Social Science, Polling, Machine Learning

Today, I'm live-blogging the 5th anniversary symposium of Microsoft Research New England, a gathering to discuss computer science, big data, politics, society, and machine learning.

The session on problems and challenges in machine learning and big data included fascinating talks on machine vision, computational social science, polling techniques, and machine learning.

What Goes Wrong When Computer Vision Fails?

"What happens when image recognition sees a car in a photo of a duck?" Antonio Torralba is associate professor at MIT in machine learning and computer vision.