"research" entries

Four short links: 24 July 2015

Four short links: 24 July 2015

Artificial Compound Eye, Google Patent Licensing, Monitoring and Alerting, Computer-Aided Inference

  1. A New Artificial Compound Eye (Robohub) — three hexagonal photodetectors arranged in a triangular shape, underneath a single lens. These photodetectors work together and combine perceived changes in structured light (optic flow) to present a 3D image that shows what is moving in the scene, and in which direction the movement is happening.
  2. Google’s Defensive Patent Initiative (TechCrunch) — good article, despite TechCrunch origin. Two-tiered program: give away groups of patents to startups with $500k-$20M in revenue, and sell patents to startups.
  3. Bosunan open-source, MIT licensed, monitoring and alerting system by Stack Exchange.
  4. The Rise of Computer-Aided Explanation (Michael Nielsen) — Hod Lipson of Columbia University. Lipson and his collaborators have developed algorithms that, when given a raw data set describing observations of a mechanical system, will actually work backward to infer the “laws of nature” underlying those data. (Paper)
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Four short links: 22 July 2015

Four short links: 22 July 2015

Smart Headlights, Habitual Speed, AI Authors, and Programming Language Evolution

  1. Ford’s Smart Headlights — spotlights targeted by infra-red, and accumulating knowledge of fixed features to illuminate. Wonder what an attacker can do to it?
  2. Speed as a HabitYou don’t have to be militant about it, just consistently respond that today is better than tomorrow, that right now is better than six hours from now. This is chock full of good advice, and the occasional good story.
  3. Coding Creativity: Copyright and the Artificially Intelligent Author (PDF) — if AI creates cultural works (e.g., DeepDream images), who owns those works? Suggests that “work for hire” doctrine may be the way to answer that in the future. (via Andreas Schou)
  4. Punctuated Equilibrium in the Large-Scale Evolution of Programming Languages (PDF) — Here we study the large-scale historical development of programming languages, which have deeply marked social and technological advances in the last half century. We analyse their historical connections using network theory and reconstructed phylogenetic networks. Using both data analysis and network modeling, it is shown that their evolution is highly uneven, marked by innovation events where new languages are created out of improved combinations of different structural components belonging to previous languages. These radiation events occur in a bursty pattern and are tied to novel technological and social niches. The method can be extrapolated to other systems and consistently captures the major classes of languages and the widespread horizontal design exchanges, revealing a punctuated evolutionary path. (via Jarkko Hietaniemi)
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Four short links: 30 June 2015

Four short links: 30 June 2015

Ductile Systems, Accessibility Testing, Load Testing, and CRAP Data

  1. Brittle SystemsMore than two decades ago at Sun, I was convinced that making systems ductile (the opposite of brittle) was the hardest and most important problem in system engineering.
  2. tota11y — accessibility testing toolkit from Khan.
  3. Locustan open source load testing tool.
  4. Impala: a Modern, Open-source SQL Engine for Hadoop (PDF) — CRAP, aka Create, Read, and APpend, as coined by an ex-colleague at VMware, Charles Fan (note the absence of update and delete capabilities). (via A Paper a Day)
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Four short links: 17 June 2015

Four short links: 17 June 2015

Academic Publishing Concentration, Hardware Independence, Exception Monitoring, and Negotiating Tactics

  1. The Oligopoly of Academic Publishers in the Digital Era (PLoSone) — Combined, the top five most prolific publishers account for more than 50% of all papers published in 2013. (via CBC)
  2. LLVM Bitcode Gives Apple Hardware Independence (Medium) — Bob [Mansfield] has been quietly building a silicon team with the skills to rival all other players in the industry. Bob works for one of 15 companies with an ARM architecture license, giving his team carte blanche to modify and extend ARM in any way they see fit. And Bob’s CPUs only have to satisfy a single customer.
  3. Github Exception Monitoring and Response — I need another word than “porn” to describe something that makes me sigh fervently with desire to achieve at that level.
  4. 31 Negotiation Tactics (Nick Kolenda) — he mysteriously omitted my power tactics of (a) crying, (b) greeting my opposite number with the wrong name, and (c) passing a napkin covered with random scrawls as I say, “what do you make of this?”
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Four short links: 20 May 2015

Four short links: 20 May 2015

Robots and Shadow Work, Time Lapse Mining, CS Papers, and Software for Reproducibility

  1. 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.
  2. 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.
  3. Git Repository of CS PapersThe 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.
  4. Software For Reproducible ScienceThis 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.
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Four short links: 19 May 2015

Four short links: 19 May 2015

Wrist Interactions, Kubernetes Open Source Success, Product Quality, and Value of Privacy

  1. Android Wear vs Apple Watch (Luke Wroblewski) — comparison of interactions and experiences.
  2. 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.
  3. 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.
  4. 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.
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Four short links: 14 May 2015

Four short links: 14 May 2015

Human-Machine Cooperation, Concurrent Systems Books, AI Future, and Gesture UI

  1. 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.”
  2. 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…
  3. Deep Learning vs Probabilistic vs LogicAs of 2015, I pity the fool who prefers Modus Ponens over Gradient Descent.
  4. 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.
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Four short links: 20 April 2015

Four short links: 20 April 2015

Edtech Advice, MEMS Sensors, Security in Go, and Building Teams

  1. Ed Tech Developer’s Guide (PDF) — U.S. government’s largely reasonable advice for educational technology startups. Nonetheless, take with a healthy dose of The Audrey Test.
  2. 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.”
  3. Looking for Security Trouble Spots in Go — brief summary of the known security issues in and around Go code.
  4. 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.
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Four short links: 17 April 2015

Four short links: 17 April 2015

Distributed SQLite, Communicating Scientists, Learning from Failure, and Cat Convergence

  1. Replicating SQLite using Raft Consensus — clever, he used a consensus algorithm to build a distributed (replicated) SQLite.
  2. When Open Access is the Norm, How do Scientists Communicate? (PLOS) — From interviews I’ve conducted with researchers and software developers who are modeling aspects of modern online collaboration, I’ve highlighted the most useful and reproducible practices. (via Jon Udell)
  3. Meet DJ Patil“It was this kind of moment when you realize: ‘Oh, my gosh, I am that stupid,’” he said.
  4. Interview with Bruce Sterling on the Convergence of Humans and MachinesIf 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)
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Four short links: 10 April 2015

Four short links: 10 April 2015

Graph Algorithm, Touchy Robots, Python Bolt-Ons, and Building Data Products

  1. Exact Maximum Clique for Large or Massive Real Graphs — explanation of how BBMCSP works.
  2. Giving Robots and Prostheses the Human Touchthe 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.”
  3. boltons — things in Python that should have been builtins.
  4. 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.
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