- Grasping with Gecko Grippers in Zero Gravity (YouTube) — biomimetic materials science breakthrough from Stanford’s Biomimetics and Dexterous Manipulation Lab proves useful in space. (via IEEE Spectrum)
- Nissan’s Self-Parking Office Chairs — clever hack, but thought-provoking: will we have an auto-navigating office chair before the self-driving auto revolution arrives? Because, you know, my day isn’t sedentary enough as it is …
- The Man Behind the Robot Revolution — profile of the man behind Willow Garage. Why he and it are interesting: Although the now defunct research-lab-startup hybrid might not ring any bells to you now, it was one of the most influential forces in modern robotics. The freewheeling robot collective jump-started the current race to apply robotics components like computer vision, manipulation, and autonomy into applications for everything from drones and autonomous cars to warehouse operations at places like Google, Amazon, and car companies like BMW. Google alone acquired three of the robot companies spawned by Willow.
- NSA’s Lousy Evaluation of Drone Strike Algorithm Effectiveness (Ars Technica) — vastly overstating the quality of the predictions. The 0.008% false positive rate would be remarkably low for traditional business applications. This kind of rate is acceptable where the consequences are displaying an ad to the wrong person, or charging someone a premium price by accident. However, even 0.008% of the Pakistani population still corresponds to 15,000 people potentially being misclassified as “terrorists” and targeted by the military—not to mention innocent bystanders or first responders who happen to get in the way. Security guru Bruce Schneier agreed. “Government uses of big data are inherently different from corporate uses,” he told Ars. “The accuracy requirements mean that the same technology doesn’t work. If Google makes a mistake, people see an ad for a car they don’t want to buy. If the government makes a mistake, they kill innocents.” (via Cory Doctorow)
"materials science" entries
Liza Kindred on the evolving role of data in fashion and the growing relationship between tech and fashion companies.
In this podcast episode, I talk with Liza Kindred, founder of Third Wave Fashion and author of the new free report “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” Kindred addresses the evolving role data and analytics are playing in the fashion industry, and the emerging connections between technology and fashion companies. “One of the things that fashion is doing better than maybe any other industry,” Kindred says, “is facilitating conversations with users.”
Gathering and analyzing user data creates opportunities for the fashion and tech industries alike. One example of this is the trend toward customization. Read more…
DNS Benchmarking, Intro to Macroeconomics, Materials-Sensing Cameras, and 3D Printing Lab Messed Around
- Namebench (Google Code) — hunts down the fastest DNS servers for your computer to use. (via Nelson Minar)
- Primer on Macroeconomics (Jig) — reading suggestions for introductions to macroeconomics suitable to understand the financial crisis and proposed solutions. (via Tim O’Reilly)
- Smarter Cameras Plumb Composition — A new type of smarter camera can take a picture but also assess the chemical composition of the objects being imaged. This enables automated inspection systems to discern details that would be missed by conventional cameras. Interesting how cameras are getting smarter: Kinect as other significant case in point. (via Slashdot)
- Not So Open — 3D printing lab at the University of Washington had to stop helping outsiders because of a crazy new IP policy from the university administration. These folks were doing amazing work, developing and sharing recipes for new materials to print with (iced tea, rice flour, and more) (via BoingBoing)
Fair Use Economy, Deconstituted Appliances, 3D Vision, Redis for Fun and Profit
- Fair Use in the US Economy (PDF) — prepared by IT lobby in the US, it’s the counterpart to Big ©’s fictitious billions of dollars of losses due to file sharing. Take each with a grain of salt, but this is interesting because it talks about the industries and businesses that the fair use laws make possible.
- Disassembled Household Appliances — neat photos of the pieces in common equipment like waffle irons, sandwich makers, can openers, etc. (via evilmadscientist)
- GelSight — gel block on a sheet of glass, lit from below with lights and then scanned with cameras, lets you easily capture 3D qualities of the objects pressed into it. Very cool demo–you can see finger prints, pulse, and even make out designs on a $100 bill.
- Redis Tutorial (Simon Willison) — Redis is a very fast collection of useful behaviours wrapped around a distributed key-value store. You get locks, IDs, counters, sets, lists, queues, replication, and more.
Smart Materials, Google OCR API, Teaching Webinar, HistEx
- Smart Materials in Architecture — Using thermal bimetals can allow architects to experiment with shape-changing buildings, Ritter said. Thermal bimetals include a combination of materials with different expansion coefficients that can cause a change in. Under changing temperatures this can lead one side of a compound to bend more than the other side, potentially creating an entirely different shape, he said. A little impractical at the moment, but think of it as hackers experimenting with what’s possible, iterating to find the fit between materials possibility and customer need. (via Liminal Existence)
- Google OCR API — The server will attempt to extract the text from the images; creating a new Google Doc for each image. Experimental at this stage, and early users report periodic crashes. Still, it’s a useful service. I wonder whether they’re seeing how people correct the scan text and using that to train the OCR algorithms. (via Waxy)
- My O’Reilly Podcast: Dan Meyer — I’m not pimping this because it’s O’Reilly (O’R do heaps of stuff I don’t mention) but because it’s the astonishingly brilliant Dan Meyer. For everything it does well, the US model of math education conditions students to anticipate narrowly defined problems with narrowly prescribed solutions. This puts them in no place to anticipate the ambiguous, broadly defined, problems they’ll need to solve after graduation, as citizens. This webcast will define two contributing factors to this intellectual impatience and then suggest a solution.
- Inflation Conversion Factors for Dollars 1774 to Estimated 2019 — in PDF and Excel format. I’ve wanted such a table in the past for answering those inevitable “… in today’s dollars?” historical business questions. (via Schuyler on Delicious)