A few weeks ago, a bunch of us spent two long evenings in the office assembling a MakerBot. Hudson Lines made an awesome timelapse video of it.

Betaworks builds a MakerBot from hudson on Vimeo.

Special thanks to the always awesome Adam (from MakerBot) for helping us breeze through the final configuration and calibration steps.

On Friday, January 28th I hopped in a cab and went up to NPR’s Bryant Park recording studio for a fifteen minute chat with Ira Flatow, host of Science Friday. I’ve been a big fan of Ira and Science Friday since I discovered the show years ago, and it was a very exciting honor to be a guest.

The image at the right is a snapshot I took with my phone while nervously waiting outside the studio.

The title of the segment is the rather dramatic Privacy At Stake As Sites Track Online Preferences. Our conversation wound around the issues of tracking user data online, and the potential opportunities and dangers that all users of online services face.

NPR has the full broadcast and transcript online.

By far the most fun and unexpected aspect of this was the number of people who wrote to me to ask questions or say that they appreciated my perspective. Many of them don’t typically follow technology news or startups, and it’s exciting to hear from people who heard the interview and were intrigued.

The video from my keynote at Strange Loop 2010 is up!

You can watch the video here: Machine Learning: A Love Story

The original abstract:

Machine learning has come a long way in recent years — from a long-marginalized field so old it still has the word “machine” in the name, to the last, best hope for making sense of our massive flows of data.

The art of ‘data science’ is asking the right questions; the answers are generally trivial or impossible. This talk will focus more on questions than on answers. I’ll give a brief history of the field with a focus on the fundamental math and algorithmic tools that we use to address these kinds of problems, then walk through several descriptive and predictive scenarios.

Finally, I’ll show one example system using bit.ly data in-depth, from the backend infrastructure through the algorithms and data processing layer to show a functioning product.

Attendees should expect to hear some good stories of data gone right and data gone awry, and walk away with a few new clever tricks.

The presentation was calibrated for the audience in the room, but I’ll be happy to answer any questions in the comments below!

How can twitter be so popular and successful if it’s down all the time?

We base statements like this on the assumption that quality of a web application maps linearly to the application’s stability. This is obviously true for most sites most of the time, but things get interesting at the edge where rare, unpredictable failure actually enables more complex human interactions around the service.

Unlike e-mail, twitter etiquette doesn’t demand that you read or reply to every message from every person you follow (or who follows you). Combine that lightweight social touch with occasional technical issues and human communication patterns, and we start to see some interesting behavior.

Twitter’s lack of reliability as a platform allows us to use the technical failings to mask our own social imperfections. How often have you heard or said something like “I was sure I was following you” or “I must not have gotten that DM” or even “I think I tweeted that…”? Even just a small percent of users behaving this way changes the social expectations.

I’d love to construct an experiment to figure out whether this idea has merit, and if so, what the optimal amount of unavailable operations for social deniability is. Should 1 in 100 actions fail? 1 in 10,000? 1 in 1,000,000? Does it matter if any fail, as long as we believe that every so often failure occurs? (How often do things really get lost in the mail, anyway?)

It’s amusing to conceive of a system that succeeds socially because it often fails technically.