- Google’s Seven Robotics Companies (IEEE) — The seven companies are capable of creating technologies needed to build a mobile, dexterous robot. Mr. Rubin said he was pursuing additional acquisitions. Rundown of those seven companies.
- Hebel (Github) — GPU-Accelerated Deep Learning Library in Python.
- What We Learned Open Sourcing — my eye was caught by the way they offered APIs to closed source code, found and solved performance problems, then open sourced the fixed code.
There's a lot of new ground to be explored in large-scale image processing.
Jetpac is building a modern version of Yelp, using big data rather than user reviews. People are taking more than a billion photos every single day, and many of these are shared publicly on social networks. We analyze these pictures to discover what they can tell us about bars, restaurants, hotels, and other venues around the world — spotting hipster favorites by the number of mustaches, for example.
Treating large numbers of photos as data, rather than just content to display to the user, is a pretty new idea. Traditionally it’s been prohibitively expensive to store and process image data, and not many developers are familiar with both modern big data techniques and computer vision. That meant we had to cut a path through some thick underbrush to get a system working, but the good news is that the free-falling price of commodity servers makes running it incredibly cheap. Read more…
A conference report on the IP transition.
Although readers of this blog know quite well the role that the Internet can play in our lives, we may forget that its most promising contributions — telemedicine, the smart electrical grid, distance education, etc. — depend on a rock-solid and speedy telecommunications network, and therefore that relatively few people can actually take advantage of the shining future the Internet offers.
Worries over sputtering advances in bandwidth in the US, as well as an actual drop in reliability, spurred the FCC to create the Technology Transitions Policy Task Force, and to drive discussion of what they like to call the “IP transition”.
Last week, I attended a conference on the IP transition in Boston, one of a series being held around the country. While we tussled with the problems of reliability and competition, one urgent question loomed over the conference: who will actually make advances happen?
Flexible Data, Google's Bottery, GPU Assist Deep Learning, and Open Sourcing
Computing should enable us to have richer lives; it shouldn’t become life.
At a recent meeting, Tim O’Reilly, referring to the work of Tristan Harris and Joe Edelman, talked about “software of regret.” It’s a wonderfully poetic phrase that deserves exploring.
For software developers, the software of regret has some very clear meanings. There’s always software that’s written poorly and incurs too much technical debt that is never paid back. There’s the software you wrote before you knew what you were doing, but never had time to fix; the software that you wrote under an inflexible and unrealistic schedule; and those three lines of code that are an awful hack, but you couldn’t get to work any other way.
That’s not what Tim was talking about, though. The software of regret is software that you use for an hour or two, and then hate yourself for using it. Facebook? Candy Crush? Tumblr? Words with Friends? YouTube? Pick your own; they’re fun for a while, but after a couple of hours, you wonder where the evening went and wish you had done something worthwhile. It’s software that only views us as targets for marketing: as views, eyeballs, and clicks. Can we change the metrics? As Edelman says, rather than designing to maximize clicks and page views, can we design to maximize fulfillment? Could Facebook measure friendships nurtured, rather than products liked?
Computing should enable us to have richer lives; it shouldn’t become life. That’s really what the software of regret is all about: taking over your life and preventing you from engaging with a world that is ultimately a lot richer than a flat, but high-resolution, screen. It’s certainly harder to avoid writing the software of regret than it is to avoid writing spaghetti code that will make your life miserable when the bug reports start rolling in. But probably more important. Do stuff that matters.
Surveillance Demarcation, NYT Data Scientist, 2D Dart, and Bayesian Database
- Reform Government Surveillance — hard not to view this as a demarcation dispute. “Ruthlessly collecting every detail of online behaviour is something we do clandestinely for advertising purposes, it shouldn’t be corrupted because of your obsession over national security!”
- Brian Abelson — Data Scientist at the New York Times, blogging what he finds. He tackles questions like what makes a news app “successful” and how might we measure it. Found via this engaging interview at the quease-makingly named Content Strategist.
- StageXL — Flash-like 2D package for Dart.
- BayesDB — lets users query the probable implications of their data as easily as a SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries. Open source.
A movement to bring us into a more harmonious relationship with our bodymind and with technology.
“What are you tracking?” This is the conversation at Quantified Self (QS) meetups. The Quantified Self movement celebrates “self-knowledge through numbers.” In our current love affair with QS, we tend to focus on data and the mind. Technology helps manage and mediate that relationship. The body is in there somewhere, too, as a sort of “slave” to the mind and the technology.
From blood sugar to pulse, from keystrokes to time spent online, the assumption is that there’s power in numbers. We also assume that what can be measured is what matters, and if behaviors can be measured, they can be improved. The entire Quantified Self movement has grown around the belief that numbers give us an insight into our bodies that our emotions don’t have.
However, in our relationship with technology, we easily fall out of touch with our bodies. We know how many screen hours we’ve logged, but we are less likely to be able to answer the question: “How do you feel?”
In our obsession with numbers and tracking, are we moving further and further away from the wisdom of the body? Our feelings? Our senses? Most animals rely entirely on their senses and the wisdom of the body to inform their behavior. Does our focus on numbers, measuring, and tracking move us further and further away from cultivating a real connection to our “Essential Self”?