"mobile" entries

Four short links: 6 April 2016

Four short links: 6 April 2016

Hi-Techtiles, Recreating 3D, Mobile Deep Learning, and Correlation Games

  1. U.S. Textile Industry Turns to Tech as Gateway to RevivalWarwick Mills is joining the Defense Department, universities including the Massachusetts Institute of Technology, and nearly 50 other companies in an ambitious $320 million project to push the American textile industry into the digital age. Key to the plan is a technical ingredient: embedding a variety of tiny semiconductors and sensors into fabrics that can see, hear, communicate, store energy, warm or cool a person, or monitor the wearer’s health.
  2. 2D to 3D With Deep CNNs (PDF) — source code on github.
  3. Squeezing AI into Mobile Systems (IEEE Spectrum) — Sze, working with Joel Emer, also an MIT computer science professor and senior distinguished research scientist at Nvidia, developed Eyeriss­, the first custom chip designed to run a state-of-the-art convolutional neural network. They showed they could run AlexNet, a particularly demanding algorithm, using less than one-tenth the energy of a typical mobile GPU: instead of consuming 5 to 10 watts, Eyeriss used 0.3 W.
  4. The 8-Bit Game That Makes Statistics Addictive (The Atlantic) — that game is Guess The Correlation. “As a researcher, you read papers and a lot of the time, you eyeball the figures without even reading the text,” he says. “You see a plot—it could even be your own plot—and make a judgment based on it. Contrary to what people believe, they’re not very good at this. And I have the data to prove that.”
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Four short links: 1 April 2016

Four short links: 1 April 2016

AI Centaurs, In-Game Warfare, Global Data Protection Laws, and Chinese Chatbots

  1. Centaurs Not Butlers (Matt Jones) — In competitive chess, teams of human and non-human intelligences are referred to as ‘Centaurs’ How might we create teams of human and non-human intelligences in the service of better designed systems, products, environments?
  2. Casino-Funded In-Game Warthis was just the opening round of what could be the largest military mobilization in that game’s history. Digging deeper into the subject, we’ve been able to chart the rise of a new in-game faction, called the Moneybadger Coalition, a group of thousands of players being bankrolled by an online casino. (via BoingBoing)
  3. Data Protection Laws Around the World — useful guide to the laws in different jurisdictions. If this is your migraine, I pity you.
  4. More Chinese Mobile UI TrendsThis year, Microsoft China released an AI chatbot called 小冰 (xiǎobīng) that has been popular. She’s accessible via the web, via a standalone app, via WeChat, via Cortana, and through a dedicated button in Xiaomi’s own seldom-used messaging app. It’s fun to toss annoying questions at her and see how she responds. Some people even confide in her. She’s kind of the love child of Siri, ELIZA, and Cleverbot.
Comment: 1
Four short links: 4 March 2016

Four short links: 4 March 2016

Snapchat's Business, Tracking Voters, Testing for Discriminatory Associations, and Assessing Impact

  1. How Snapchat Built a Business by Confusing Olds (Bloomberg) — Advertisers don’t have a lot of good options to reach under-30s. The audiences of CBS, NBC, and ABC are, on average, in their 50s. Cable networks such as CNN and Fox News have it worse, with median viewerships near or past Social Security age. MTV’s median viewers are in their early 20s, but ratings have dropped in recent years. Marketers are understandably anxious, and Spiegel and his deputies have capitalized on those anxieties brilliantly by charging hundreds of thousands of dollars when Snapchat introduces an ad product.
  2. Tracking VotersOn the night of the Iowa caucus, Dstillery flagged all the [ad network-mediated ad] auctions that took place on phones in latitudes and longitudes near caucus locations. It wound up spotting 16,000 devices on caucus night, as those people had granted location privileges to the apps or devices that served them ads. It captured those mobile ID’s and then looked up the characteristics associated with those IDs in order to make observations about the kind of people that went to Republican caucus locations (young parents) versus Democrat caucus locations. It drilled down further (e.g., ‘people who like NASCAR voted for Trump and Clinton’) by looking at which candidate won at a particular caucus location.
  3. Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit (arXiv) — We describe FairTest, a testing toolkit that detects unwarranted associations between an algorithm’s outputs (e.g., prices or labels) and user subpopulations, including sensitive groups (e.g., defined by race or gender). FairTest reports statistically significant associations to programmers as association bugs, ranked by their strength and likelihood of being unintentional, rather than necessary effects. See also slides from PrivacyCon. Source code not yet released.
  4. Inferring Causal Impact Using Bayesian Structural Time-Series Models (Adrian Colyer) — understanding the impact of an intervention by building a predictive model of what would have happened without the intervention, then diffing reality to that model.
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Four short links: 1 March 2016

Four short links: 1 March 2016

Phone Kit, Circular Phone, TensorFlow Intro, and Change Motivation

  1. Seeed RePhoneopen source and modular phone kit.
  2. Cyrcle — prototype round phone, designed by women for women. It’s clearly had a bit more thought put into it than the usual “pink it and shrink it” approach … circular to fit in smaller and shaped pockets, and software features strict notification controls: the device would only alert you to messages or updates from an inner circle.
  3. TensorFlow for Poets (Pete Warden) — I want to show how anyone with a Mac laptop and the ability to use the Terminal can create their own image classifier using TensorFlow, without having to do any coding.
  4. Finding the Natural Motivation for Change (Pia Waugh) — you can force certain behaviour changes through punishment or reward, but if people aren’t naturally motivated to make the behaviour change themselves then the change will be both unsustainable and minimally implemented. Amen!
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Four short links: 29 January 2016

Four short links: 29 January 2016

LTE Security, Startup Tools, Security Tips, and Data Fiction

  1. LTE Weaknesses (PDF) — ShmooCon talk about how weak LTE is: a lot of unencrypted exchanges between handset and basestation, cheap and easy to fake up a basestation.
  2. AnalyzoFind and Compare the Best Tools for your Startup it claims. We’re in an age of software surplus: more projects, startups, apps, and tools than we can keep in our heads. There’s a place for curated lists, which is why every week brings a new one.
  3. How to Keep the NSA Out — NSA’s head of Tailored Access Operations (aka attacking other countries) gives some generic security advice, and some interesting glimpses. “Don’t assume a crack is too small to be noticed, or too small to be exploited,” he said. If you do a penetration test of your network and 97 things pass the test but three esoteric things fail, don’t think they don’t matter. Those are the ones the NSA, and other nation-state attackers will seize on, he explained. “We need that first crack, that first seam. And we’re going to look and look and look for that esoteric kind of edge case to break open and crack in.”
  4. The End of Big Data — future fiction by James Bridle.
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Four short links: 8 January 2016

Four short links: 8 January 2016

Modern C, Colorizing Photos, Flashing Toy Drones, and Web + Native

  1. How to C in 2016 — straightforward recommendations for writing C if you have to.
  2. Using Deep Learning to Colorize Old Photos — comes with a trained TensorFlow model to play with.
  3. Open Source Firmware for Toy DronesThe Eachine H8 is a typical-looking mini-quadcopter of the kind that sell for under $20.[…] takes you through a step-by-step guide to re-flashing the device with a custom firmware to enable acrobatics, or simply to tweak the throttle-to-engine-speed mapping for the quad. (via DIY Drones)
  4. Mobile Web vs. Native Apps or Why You Want Both (Luke Wroblewski) — The Web is for audience reach and native apps are for rich experiences. Both are strategic. Both are valuable. So when it comes to mobile, it’s not Web vs. Native. It’s both. The graphs are impressive.
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Four short links: 5 January 2016

Four short links: 5 January 2016

Inference with Privacy, RethinkDB Reliability, T-Mobile Choking Video, and Real-Time Streams

  1. Privacy-Preserving Inference of Social Relationships from Location Data (PDF) — utilizes an untrusted server and computes the building blocks to support various social relationship studies, without disclosing location information to the server and other untrusted parties. (via CCC Blog)
  2. Jepson takes on Rethink — the glowingest review I’ve seen from Aphyr. As far as I can ascertain, RethinkDB’s safety claims are accurate.
  3. T-Mobile’s BingeOn `Optimization’ Is Just Throttling (EFF) — T-Mobile has claimed that this practice isn’t really “throttling,” but we disagree. It’s clearly not “optimization,” since T-Mobile doesn’t alter the actual content of the video streams in any way.
  4. qminer — BSD-licensed data analytics platform for processing large-scale, real-time streams containing structured and unstructured data.
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Four short links: 23 December 2015

Four short links: 23 December 2015

Software Leaders, Hadoop Ecosystem, GPS Spoofing, and Explaining Models

  1. Things Software Leaders Should Know (Ben Gracewood) — If you have people things and tech things on your to-do list, put the people things first on the list.
  2. The Hadoop Ecosystem — table of the different projects across the Hadoop ecosystem.
  3. Narcos GPS-Spoofing Border Drones — not only are the border drones expensive and ineffective, now they’re being tricked. Basic trade-off: more reliability or longer flight times?
  4. A Model Explanation System (PDF) — you can explain any machine-learned decision, though not necessarily the way the model came to the decision. Confused? This summary might help. Explainability is not a property of the model.
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Four short links: 9 December 2015

Four short links: 9 December 2015

Graph Book, Data APIs, Mobile Commerce Numbers, and Phone Labs

  1. Networks, Crowds, and Markets — network theory (graph analysis), small worlds, network effects, power laws, markets, voting, property rights, and more. A book that came out of a Cornell course by ACM-lauded Jon Kleinberg.
  2. Qua framework for building data APIs. From a government department, no less. (via Nelson Minar)
  3. Three Most Common M-Commerce Questions Answered (Facebook) — When we examined basket sizes on an m-site versus an app, we found people spend 43 cents in app to every $1 spent on m-site. (via Alex Dong)
  4. Phonelabs — science labs with mobile phones. All open sourced for maximum spread.
Comments: 2
Four short links: 1 December 2015

Four short links: 1 December 2015

Radical Candour, Historical Social Network, Compliance Opportunities, and Mobile Numbers

  1. Radical Candour: The Surprising Secret to Being a Good Boss — this, every word, this. “Caring personally makes it much easier to do the next thing you have to do as a good boss, which is being willing to piss people off.”
  2. Six Degrees of Francis Baconrecreates the British early modern social network to trace the personal relationships among figures like Bacon, Shakespeare, Isaac Newton, and many others. (via CMU)
  3. Last Bus Startup Standing (TechCrunch) — Vahabzadeh stressed that a key point of Chariot’s survival has been that the company has been above-board with the law from day one. “They haven’t cowboy-ed it,” said San Francisco supervisor Scott Wiener, a mass transit advocate who recently pushed for a master subway plan for the city. “They’ve been good about taking feedback and making sure they’re complying with the law. I’m a fan and think that private transportation options and rideshares have a significant role to play in making us a transit-first city.”
  4. Mobile App Developers are Sufferingthe top 20 app publishers, representing less than 0.005% of all apps, earn 60% of all app store revenue. The article posits causes of the particularly extreme power law.
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