Rise of the Robots and Shadow Work (NY Times) — In “Rise of the Robots,” Ford argues that a society based on luxury consumption by a tiny elite is not economically viable. More to the point, it is not biologically viable. Humans, unlike robots, need food, health care and the sense of usefulness often supplied by jobs or other forms of work. Two thought-provoking and related books about the potential futures as a result of technology-driven change.
Time Lapse Mining from Internet Photos (PDF) — First, we cluster 86 million photos into landmarks and popular viewpoints. Then, we sort the photos by date and warp each photo onto a common viewpoint. Finally, we stabilize the appearance of the sequence to compensate for lighting effects and minimize flicker. Our resulting time-lapses show diverse changes in the world’s most popular sites, like glaciers shrinking, skyscrapers being constructed, and waterfalls changing course.
Git Repository of CS Papers — The intention here is to both provide myself with backups and easy access to papers, while also collecting a repository of links so that people can always find the paper they are looking for. Pull the repo and you’ll never be short of airplane/bedtime reading.
Software For Reproducible Science — This quality is indeed central to doing science with code. What good is a data analysis pipeline if it crashes when I fiddle with the data? How can I draw conclusions from simulations if I cannot change their parameters? As soon as I need trust in code supporting a scientific finding, I find myself tinkering with its input, and often breaking it. Good scientific code is code that can be reused, that can lead to large-scale experiments validating its underlying assumptions.
Eric Brewer on Kubernetes — interesting not only for insights into Google’s efforts around Kubernetes but for: There’s so much excitement we can hardly handle all the pull requests. I think we’re committing, based on the GitHub log, something like 40 per day right now, and the demand is higher than that. Each of those takes reviews and, of course, there’s a wide variety of quality on those. Some are easy to review and some are quite hard to review. It’s a success problem, and we’re happy to have it. We did scale up the team to try and improve its velocity, but also just improve our ability to interact with all of the open source world that legitimately wants to contribute and has a lot to contribute. I’m very excited that the velocity is here, but it’s moving so fast it’s hard to even know all the things that change day to day. Makes a welcome change from the code dumps that are some of Google’s other high-profile projects.
We Don’t Sell Saddles Here — Stewart Butterfield, to his team, on product development and quality. Every word of this is true for every other product, too.
What is Privacy Worth? (PDF) — When endowed with the $10 untrackable card, 60.0% of subjects claimed they would keep it; however, when endowed with the $12 trackable card only 33.3% of subjects claimed they would switch to the untrackable card. […] This research raises doubts about individuals’ abilities to rationally navigate issues of privacy. From choosing whether or not to join a grocery loyalty program, to posting embarrassing personal information on a public website, individuals constantly make privacy-relevant decisions which impact their well-being. The finding that non-normative factors powerfully influence individual privacy valuations may signal the appropriateness of policy interventions.
Ghosts in the Machines (Courtney Nash) — People are neither masters of machines, nor subservient to their machine-learning outcomes — we cannot, and should not, separate the two. We are actors, together, in a very complex system. David Woods calls this “joint cognitive systems.”
TLA+ (Leslie Lamport) — two tutorials: “Principles of Concurrent Computing” and “Specification of Concurrent Systems.” Ironically, I see people grizzling that the book on distributed systems hasn’t been linearised. I wonder if you can partition it into the two tutorials and still have full availability…
Touché (Disney Research) — measur[es] capacitive response of object and human at multiple frequencies, a technique that we called Swept Frequency Capacitive Sensing. The signal travels through different paths depending on its frequency, capturing the posture of human hand and body as well as other properties of the context. The resulted data is classified using machine learning algorithms to identify gestures that are then used to trigger desired responses of the user interface.
The Crazy-Tiny Next Generation of Computers — 1 cubic millimeter-sized sensors are coming. The only sound you might hear is a prolonged groan. That’s because these computers are just one cubic millimeter in size, and once they hit the floor, they’re gone. “We just lose them,” Dutta says. “It’s worse than jewelry.”
The New Science of Building Great Teams (Sandy Pentland) — fascinating discussion of MIT’s Human Dynamics lab’s research into how great teams function. The data also reveal, at a higher level, that successful teams share several defining characteristics: 1. Everyone on the team talks and listens in roughly equal measure, keeping contributions short and sweet. 2. Members face one another, and their conversations and gestures are energetic. 3. Members connect directly with one another—not just with the team leader. 4. Members carry on back-channel or side conversations within the team. 5. Members periodically break, go exploring outside the team, and bring information back.
Meet DJ Patil — “It was this kind of moment when you realize: ‘Oh, my gosh, I am that stupid,’” he said.
Interview with Bruce Sterling on the Convergence of Humans and Machines — If you are a human being, and you are doing computation, you are trying to multiply 17 times five in your head. It feels like thinking. Machines can multiply, too. They must be thinking. They can do math and you can do math. But the math you are doing is not really what cognition is about. Cognition is about stuff like seeing, maneuvering, having wants, desires. Your cat has cognition. Cats cannot multiply 17 times five. They have got their own umwelt (environment). But they are mammalian, you are a mammalian. They are actually a class that includes you. You are much more like your house cat than you are ever going to be like Siri. You and Siri converging, you and your house cat can converge a lot more easily. You can take the imaginary technologies that many post-human enthusiasts have talked about, and you could afflict all of them on a cat. Every one of them would work on a cat. The cat is an ideal laboratory animal for all these transitions and convergences that we want to make for human beings. (via Vaughan Bell)
Giving Robots and Prostheses the Human Touch — the team, led by mechanical engineer Veronica J. Santos, is constructing a language of touch that both a computer and a human can understand. The researchers are quantifying this with mechanical touch sensors that interact with objects of various shapes, sizes, and textures. Using an array of instrumentation, Santos’ team is able to translate that interaction into data a computer can understand. The data is used to create a formula or algorithm that gives the computer the ability to identify patterns among the items it has in its library of experiences and something it has never felt before. This research will help the team develop artificial haptic intelligence, which is, essentially, giving robots, as well as prostheses, the “human touch.”
boltons — things in Python that should have been builtins.
Everything We Wish We’d Known About Building Data Products (DJ Patil and RusJan Belkin) — Data is super messy, and data cleanup will always be literally 80% of the work. In other words, data is the problem. […] “If you’re not thinking about how to keep your data clean from the very beginning, you’re fucked. I guarantee it.” […] “Every single company I’ve worked at and talked to has the same problem without a single exception so far — poor data quality, especially tracking data,” he says.“Either there’s incomplete data, missing tracking data, duplicative tracking data.” To solve this problem, you must invest a ton of time and energy monitoring data quality. You need to monitor and alert as carefully as you monitor site SLAs. You need to treat data quality bugs as more than a first priority. Don’t be afraid to fail a deploy if you detect data quality issues.
The Internet of Kafkaesque Things (ACLU) — As computers are deployed in more regulatory roles, and therefore make more judgments about us, we may be afflicted with many more of the rigid, unjust rulings for which bureaucracies are so notorious.
Complete Force Control in Constrained Under-actuated Mechanical Systems (Robohub) — Nori focuses on finding ways to advance the dynamic system of a robot – the forces that interact and make the system move. Key to developing dynamic movements in a robot is control, accompanied by the way the robot interacts with the environment. Nori talks us through the latest developments, designs, and formulas for floating-base/constrained mechanical systems, whole-body motion control of humanoid systems, whole-body dynamics computation on the iCub humanoid, and finishes with a video on recent implementations of whole-body motion control on the iCub. Video and download of presentation.
Unpowered Ankle Exoskeleton — “As we understand human biomechanics better, we’ve begun to see wearable robotic devices that can restore or enhance human motor performance,” says Collins. “This bodes well for a future with devices that are lightweight, energy-efficient, and relatively inexpensive, yet enhance human mobility.”
Body-Powered Processing (Ars Technica) — The new SAM L21 32-bit ARM family of microcontroller (MCUs) consume less than 35 microamps of power per megahertz of processing speed while active, and less than 200 nanoamps of power overall when in deep sleep mode—with varying states in between. The chip is so low power that it can be powered off energy capture from the body. (via Greg Linden)
The Shut-In Economy — In 1998, Carnegie Mellon researchers warned that the Internet could make us into hermits. They released a study monitoring the social behavior of 169 people making their first forays online. The Web-surfers started talking less with family and friends, and grew more isolated and depressed. “We were surprised to find that what is a social technology has such anti-social consequences,” said one of the researchers at the time. “And these are the same people who, when asked, describe the Internet as a positive thing.”
Thoughts on Amazon Dash (Matt Webb) — In a way, we’re really seeing the future of marketing here. We’ve separated awareness (advertising) and distribution (stores) for so long, but it’s no longer the way. When you get a Buy Now button in a Tweet, you’re seeing ads and distribution merging, and the Button is the physical instantiation of this same trend. […] in the future every product will carry a buy button.
Lightning Networks (Rusty Russell) — I finally took a second swing at understanding the Lightning Network paper. The promise of this work is exceptional: instant, reliable transactions across the bitcoin network. But the implementation is complex, and the draft paper reads like a grab bag of ideas; but it truly rewards close reading! It doesn’t involve novel crypto, nor fancy bitcoin scripting tricks. There are several techniques that are used in the paper, so I plan to concentrate on one per post and wrap up at the end. Already posted part II.
Facebook’s Mystery Machine — The goal of this paper is very similar to that of Google Dapper[…]. Both work [to] try to figure out bottlenecks in performance in high fanout large-scale Internet services. Both work us[ing] similar methods, however this work (the mystery machine) tries to accomplish the task relying on less instrumentation than Google Dapper. The novelty of the mystery machine work is that it tries to infer the component call graph implicitly via mining the logs, where as Google Dapper instrumented each call in a meticulous manner and explicitly obtained the entire call graph.
The Multiple Lives of Moore’s Law — A shrinking transistor not only allowed more components to be crammed onto an integrated circuit but also made those transistors faster and less power hungry. This single factor has been responsible for much of the staying power of Moore’s Law, and it’s lasted through two very different incarnations. In the early days, a phase I call Moore’s Law 1.0, progress came by “scaling up”—adding more components to a chip. At first, the goal was simply to gobble up the discrete components of existing applications and put them in one reliable and inexpensive package. As a result, chips got bigger and more complex. The microprocessor, which emerged in the early 1970s, exemplifies this phase. But over the last few decades, progress in the semiconductor industry became dominated by Moore’s Law 2.0. This era is all about “scaling down,” driving down the size and cost of transistors even if the number of transistors per chip does not go up.