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January 13 2014

Four short links: 13 January 2014

  1. s3mper (Github) — Netflix’s library to add consistency checking to S3. (via Netflix tech blog)
  2. Powerup Smartphone-Controlled Paper Airplane — boggle. You know the future is here when you realise you’re on the Internet of Trivial Things.
  3. clmtrackr (Github) — real-time face recognition, deformation, and substitution in Javascript. Boggle.
  4. Nine Wearables (Quartz) — a roundup of Glass-inspired wearables, including projecting onto contact lenses which wins today’s “most squicky idea” award.

November 01 2013

Four short links: 1 November 2013

  1. Analogy as the Core of Cognition (YouTube) — a Douglas Hofstadter lecture at Stanford.
  2. Why Isn’t Programming Futuristic? (Ian Bicking) — delicious provocations for the future of programming languages.
  3. Border Check — visualisation of where your packet go, and the laws they pass through to get there.
  4. Pi Noir — infrared Raspberry Pi camera board. (via DIY Drones)

October 24 2013

Four short links: 24 October 2013

  1. Visually Programming Arduino — good for little minds.
  2. Rapid Hardware Iteration at Scale (Forbes) — It’s part of the unique way that Xiaomi operates, closely analyzing the user feedback it gets on its smartphones and following the suggestions it likes for the next batch of 100,000 phones. It releases them every Tuesday at noon Beijing time.
  3. Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images (PLoS One) — We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets.
  4. Kratuan Open Source client-side analysis framework to create simple yet powerful renditions of data. It allows you to dynamically adjust your view of the data to highlight issues, opportunities and correlations in the data.

July 11 2013

Four short links: 11 July 2013

  1. Sifted — 7 minute animation set in a point cloud world, using photogrammetry in film-making. My brilliant cousin Ben wrote the software behind it. See this newspaper article and tv report for more.
  2. Vehicle Tech Out of Sync with Drivers’ DevicesFord Motor Co. has its own system. Apple Inc. is working with one set of automakers to design an interface that works better with its iPhone line. Some of the same car companies and others have joined the Car Connectivity Consortium, which is working with the major Android phone brands to develop a different interface. FFS. “… you are changing your phone every other year, and the top-of-mind apps are continuously changing.” That’s why Chevrolet, Mini and some other automakers are starting to offer screens that mirror apps from a smartphone.
  3. Incentives in Notice and Takedown (PDF) — findings summarised in Blocking and Removing Illegal Child Sexual Content: Analysis from a Technical and Legal Perspective: financial institutions seemed to be relatively successful at removing phishing websites while it took on average 150 times longer to remove child pornography.
  4. OpenCV for Processing (Github) — OpenCV for Processing is based on the official OpenCV Java bindings. Therefore, in addition to a suite of friendly functions for all the basics, you can also do anything that OpenCV can do. And a book from O’Reilly, and it’ll be CC-licensed. All is win. (via Greg Borenstein)

June 17 2013

Four short links: 18 June 2013

  1. Our Backbone Stack (Pamela Fox) — fascinating glimpse into the tech used and why.
  2. Automating Card Games Using OpenCV and PythonMy vision for an automated version of the game was simple. Players sit across a table on which the cards are laid out. My program would take a picture of the cards and recognize them. It would then generate valid expression that yielded 24, and then project the answer on to the table.
  3. Ray Ozzie on PRISM — posted on Hacker News (!). In particular, in this world where “SaaS” and “software eats everything” and “cloud computing” and “big data” are inevitable and already pervasive, it pains me to see how 3rd Party Doctrine may now already be being leveraged to effectively gut the intent of U.S. citizens’ Fourth Amendment rights. Don’t we need a common-sense refresh to the wording of our laws and potentially our constitution as it pertains to how we now rely upon 3rd parties? It makes zero sense in a “services age” where granting third parties limited rights to our private information is so basic and fundamental to how we think, work, conduct and enjoy life. (via Alex Dong)
  4. Larry Brilliant’s Commencement Speech (HufPo) — speaking to med grads, he’s full of purpose and vision and meaning for their lives. His story is amazing. I wish more CS grads were inspired to work on stuff that matters, and cautioned about adding their great minds to the legion trying to solve the problem of connecting you with brands you love.

February 28 2013

New vision in old industry

Nathan Oostendorp thought he’d chosen a good name for his new startup: “Ingenuitas,” derived from Latin meaning “freely born” — appropriate, he thought, for a company that would be built on his own commitment to open-source software.

But Oostendorp, earlier a co-founder of Slashdot, was aiming to bring modern computer vision systems to heavy industry, where the Latinate name didn’t resonate. At his second meeting with a salty former auto executive who would become an advisor, Oostendorp says, “I told him we were going to call the company Ingenuitas, and he immediately said, ‘bronchitis, gingivitis, inginitis. Your company is a disease.’”

And so Sight Machine got its name — one so natural to Michigan’s manufacturers that, says CEO and co-founder Jon Sobel, visitors often say “I spent the afternoon down at Sight” in the same way they might say “down at Anderson” to refer to a tool-and-die shop called Anderson Machine.

Sight Machine is adapting the tools and formulations of the software industry to the much more conservative manufacturing sector. Changing its name was the first of several steps the company took to find cultural alignment with its clients — the demanding engineers who run giant factories that produce things like automotive bolts.

At its heart is something of a crossover group — programmers and designers who are comfortable with Silicon Valley-style fast innovation, but who have deep roots in Midwestern industry. Sight Machine’s founders quickly realized that they needed to sell their software as a simple, effective, and modular solution and downplay the stack of open-source and proprietary software, developed by young programmers working late hours, that might make tech observers take notice.

Sight Machine staff with a full-scale mockup of an auto-plant inspection station that they use to test their system.Sight Machine staff with a full-scale mockup of an auto-plant inspection station that they use to test their system.
Sight Machine staff in the Ann Arbor warehouse where they built a full-scale mockup of an auto-plant quality-control station to test their system

“Nate saw these big bottlenecks in the way things were being done” in industrial computer vision, says Sobel. “There were no high-level frameworks like Ruby on Rails, and everything is set up to be pass-fail; there are no higher-level analytics.” Sight Machine set out to build what they hope will become “Rails for vision.”

Sight Machine’s co-founders built much of SimpleCV, an open-source computer vision library that’s designed to be accessible to people who aren’t experts in the field. (O’Reilly has published a book on SimpleCV, written by four of Sight Machine’s principals.)

Sight Machine's software measures grain-flow characteristics in a fastener, using little more than a commercial flat-bed scanner.Sight Machine's software measures grain-flow characteristics in a fastener, using little more than a commercial flat-bed scanner.
Sight Machine’s software measures grain-flow characteristics in a fastener, using little more than a commercial flat-bed scanner.

Anthony Oliver, the company’s CTO and co-founder, worked on automation at a big car plant in Toledo, Ohio, before being laid off during the recession and deciding to switch tracks. “They had 200 cameras at the plant, but they were treating them like black boxes — after the picture was done, they’d throw it out. They weren’t collecting trending patterns, doing self-correction,” he says. The plant had bought computer vision systems from integrators that weren’t making much data available for higher-level analytics.

“There’s a huge disconnect [in heavy industry] from the Internet way of doing things,” says co-founder Kurt DeMaagd, also a Slashdot co-founder. “Process engineers are very data-driven, but they haven’t tried these new tools, and they’re not working in real-time.” Oliver says the goal was to build a system that would raise immediate flags, “as opposed to saying, ‘hey, a week ago we were having quality-control problems.’”

Their system puts a clear emphasis on the value of software rather than hardware (though a few of the industrial-quality components, in particular the CCD cameras they use, remain expensive). It can stand on its own as a module in an automated factory; no system integrator necessary. And, in the modern form, it emphasizes data retention and high-level analytics.

Sight Machine’s first client manufactures bolts — fasteners, as they’re called by industrial insiders — checking the integrity of their steel at the beginning and end of each batch by slicing one open and scanning it on a cheap flatbed scanner. Software discerns the dimensions of the steel’s grain (compression lines that form when the head of the bolt is pounded out) and provides an instantaneous quantitative measure of quality. The previous method had involved employees looking at bolts through microscopes.

Swarming in a tank at a fish farm. Sight Machine's object: to signal the feeding system to stop putting food in the tank once the fish had eaten enough to be satisfied. Photo: courtesy Sight MachineSwarming in a tank at a fish farm. Sight Machine's object: to signal the feeding system to stop putting food in the tank once the fish had eaten enough to be satisfied. Photo: courtesy Sight Machine
Swarming in a tank at a fish farm. Sight Machine’s object: to signal the feeding system to stop putting food in the tank once the fish had eaten enough to be satisfied. Photo: courtesy Sight Machine.

Another client, a fish farm in Michigan, uses Sight Machine’s software to manage feeding — determining when the fish have had their fill by measuring changes in movement and switching off the farm’s auto feeders. Another proposal for an Ann Arbor-based deli and mail-order house would use Sight Machine software to watch for bunch-ups in the production line and tell managers to send help to, say, the jam-labeling station if the employee there is having trouble filling orders fast enough.

And in a live demonstration that I saw at the company’s industrial-park development space, Sight Machine software detected a misapplied badge on the back of an SUV. That’s a problem that could be corrected quickly and easily at an automaker’s quality-control checkpoint, but if a car that doesn’t have four-wheel drive makes it to a dealer’s lot with a chrome “4×4″ badge glued to the tailgate, the logistics and inventory costs of fixing the problem will be substantial.

Cameras photograph a car as it rolls through an inspection station, and signal in real-time whether trim features like badges, taillights, and rims are correct. Photo: Jon BrunerCameras photograph a car as it rolls through an inspection station, and signal in real-time whether trim features like badges, taillights, and rims are correct. Photo: Jon Bruner
Cameras photograph a car as it rolls through an inspection station, and signal in real-time whether trim features like badges, taillights, and rims are correct. Photo: Jon Bruner.

In addition to a real-time check like this one, Sight Machine's software can also aggregate results for higher-level analysis. Engineers can, for instance, call up photos of every yellow car with a bent antenna. Photo: courtesy Sight Machine.In addition to a real-time check like this one, Sight Machine's software can also aggregate results for higher-level analysis. Engineers can, for instance, call up photos of every yellow car with a bent antenna. Photo: courtesy Sight Machine.
In addition to a real-time check like this one, Sight Machine’s software can also aggregate results for higher-level analysis. Engineers can, for instance, call up photos of every yellow car with a bent antenna.
Photo: courtesy Sight Machine.

Industrial firms tend to be conservative in adopting new systems, for a reason: the costs of a plant outage are huge (consider that a large auto assembly plant might produce more than 60 vehicles per hour — an outage of just one minute is equivalent to one car’s worth of lost production). They also tend to have enormous amounts of capital tied up in big, integrated production systems, making changes costly.

Sight Machine’s founders have approached both of those obstacles carefully. The company’s developers work in an industrial park in Ann Arbor, Mich., where they built a mockup of an auto-plant inspection station to test their software under factory conditions. Several of them have worked in heavy manufacturing, including automotive and defense, and the company has its roots at the decidedly machine-oriented Maker Works, a maker space across the street that offers Michigan’s industrial prototypers access to plasma cutters and CNC mills.

Early prototypes got some upgrades to meet the expectations of plant managers used to heavy-duty equipment. Sight Machine’s system uses costly CCD cameras instead of cheaper consumer-grade cameras. Design director Kyle Lawson says, to make the company’s first camera mount he “took a Logitech webcam stand, broke it down, and filled it with pennies to make it feel industrial.” (Now he fabricates heavy-duty camera mounts himself at Maker Works.)

As for the problem of weaving a new assembly line component into an old plant, Sight Machine’s software is free-standing and can be provided as a service or licensed to run locally — minimal integration with plant controls needed. That’s an important consideration for a cautious assembly-line manager who’s experimenting with a startup’s software.

Software and industry are inching closer; the industrial Internet will make it easier for innovators to turn physical-world problems into software problems, and then solve them using rich open-source tools and pervasive networks. Along the way, I think we’ll see lots of stories like Sight Machine’s.


This is a post in our industrial Internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

January 28 2013

Four short links: 28 January 2013

  1. Aaron’s Army — powerful words from Carl Malamud. Aaron was part of an army of citizens that believes democracy only works when the citizenry are informed, when we know about our rights—and our obligations. An army that believes we must make justice and knowledge available to all—not just the well born or those that have grabbed the reigns of power—so that we may govern ourselves more wisely.
  2. Vaurien the Chaos TCP Monkeya project at Netflix to enhance the infrastructure tolerance. The Chaos Monkey will randomly shut down some servers or block some network connections, and the system is supposed to survive to these events. It’s a way to verify the high availability and tolerance of the system. (via Pete Warden)
  3. Foto Forensics — tool which uses image processing algorithms to help you identify doctoring in images. The creator’s deconstruction of Victoria’s Secret catalogue model photos is impressive. (via Nelson Minar)
  4. All Trials Registered — Ben Goldacre steps up his campaign to ensure trial data is reported and used accurately. I’m astonished that there are people who would withhold data, obfuscate results, or opt out of the system entirely, let alone that those people would vigorously assert that they are, in fact, professional scientists.

January 25 2013

The driverless-car liability question gets ahead of itself

Megan McArdle has taken on the question of how liability might work in the bold new world of driverless cars. Here’s her framing scenario:

Imagine a not-implausible situation: you are driving down a brisk road at 30 mph with a car heading towards you in the other lane at approximately the same speed. A large ball rolls out into the street, too close for you to brake. You, the human, knows that the ball is likely to be followed, in seconds, by a small child; you slam on the brakes (perhaps giving yourself whiplash) or swerve, at considerable risk of hitting the other car.

What should a self-driving car do?  More to the point, if you hit the kid, or the other car, who gets sued?

The lawyer could go after you, with your piddling $250,000 liability policy and approximately 83 cents worth of equity in your home. Or he could go after the automaker, which has billions in cash, and the ultimate responsibility for whatever decision the car made. What do you think is going to happen?

The implication is that the problem of concentrated liability might make automakers reluctant to take the risk of introducing driverless cars.

I think McArdle is taking a bit too much of a leap here. Automakers are accustomed to having the deepest pockets within view of any accident scene. Liability questions raised by this new kind of intelligence will have to be worked out — maybe by forcing drivers to take on the liability for their cars’ performance via their insurance companies, and insurance companies in turn certifying types of technology that they’ll insure. By the time driverless cars become a reality they’ll probably be substantially safer than human drivers, so the insurance companies might be willing to accept the tradeoff and everyone will benefit.

(Incidentally, I’m told by people who have taken rides in Google’s car that the most unnerving part of it is that it drives like your driver’s ed teacher told you to — at exactly the speed limit, with full stops at stop signs and conservative behavior at yellow lights.)

But we’ll probably get the basic liability testing out of the way before a car like Google’s hits the road in large numbers. First will come a wave of machine vision-based driver-assist technologies like automatic cruise control on highways (similar to some kinds of technology that have been around for years). These features present liability issues similar to those in a fully driverless car — can an automaker’s driving judgment be faulted in an accident? — but in a somewhat less fraught context.

The interesting question to me is how the legal system might handle liability for software that effectively drives a car better than any human possibly could. In the kind of scenario that McArdle outlines, a human driver would take intuitive action to avoid an accident — action that will certainly be at least a little bit sub-optimal. Sophisticated driving software could do a much better job at taking the entire context of the situation into account, evaluating several maneuvers, and choosing the one that maximizes survival rates through a coldly rational model.

That doesn’t solve the problem of liability chasing deep pockets, of course, but that’s a problem with the legal system, not the premise of a driverless car. One benefit that carmakers might enjoy is that driverless cars could store black box-type recordings, with detailed data on the context in which every decision was made, in order to show in court that the car’s software acted as well as it possibly could have.

In that case, driverless cars might present a liability problem for anyone who doesn’t own one — a human driver who crashes into a driverless car will find it nearly impossible to show he’s not at fault.


This is a post in our industrial Internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

Reposted byRK RK

January 23 2013

Four short links: 23 January 2013

  1. These Glasses Thwart Facial Recognition Software (Slate) — good idea, but don’t forget to put a stone in your shoe to thwart gait recognition too.
  2. opsec for Hackers (Slideshare) — how boring and unexciting most of not getting caught is.
  3. DHS Warns Password Cracker Targeting Industrial Networks (Nextgov) — Security consultants recently concluded that there are about 7,200 Internet-facing critical infrastructure devices, many of which use default passwords. Wake me when you stop boggling. Welcome to the Internet of Insecure Things (it’s basically the Internet we already have, but Borat can pwn your hydro dam and your fridge is telling Chinese milspec hackers when you midnight snack).
  4. The Evolution of Steve Mann’s Apparatus (Beta Knowledge) — wearable computing went from “makes you look like a robot who will never get laid” to “looks like sunglasses and promiscuity is an option”.

October 24 2012

Four short links: 24 October 2012

  1. Restoration of Defocused and Blurry Images — impressive demos, and open source (GPLv3) code. All those blurred faces and documents no longer seem so safe.
  2. Peter Molyneux Profile in Wired — worth reading for: (a) Molyneux’s contribution to the genre; (b) the inspiration he drew from his satirical Twitter mirror (@PeterMolydeux) is lovely, and (c) the game jams to build the fake Molyneux games, where satire becomes reality. (via Andy Baio)
  3. Trusted Computing for Industrial Control Systems — Kaspersky reveals plans for an open source O/S for industrial control systems, so reactors and power stations and traffic systems aren’t vulnerable to StuxNet-type attacks. (via Jim Stogdill)
  4. Android Virtual Machines — faster emulation for testing than the traditional simulators.

August 15 2012

Four short links: 15 August 2012

  1. Reproducibility Initiative (Science Exchange) — a service offering researchers who will attempt to reproduce your work. Validated studies will receive a Certificate of Reproducibility acknowledging that their results have been independently reproduced as part of the Reproducibility Initiative. Researchers have the opportunity to publish the replicated results as an independent publication in the PLOS Reproducibility Collection, and can share their data via the figshare Reproducibility Collection repository. The original study will also be acknowledged as independently reproduced if published in a supporting journal. See also writeup in Nature.
  2. Designing Open Projects (PDF) — IBM report with very sensible advice on steps to take when creating open projects for engagement and participation. Should be recommended reading for all who hope to get others to help.
  3. Hustleboards — “disposable forums”, easy lightweight web-based chats. Nice and simple UI.
  4. Prosthetic Retina Helps Restore Sight in Mice (Nature) — computer-mediated vision won’t change our world, but it’ll change what we think is in our world.

August 03 2012

Four short links: 3 August 2012

  1. Urban Camouflage WorkshopMost of the day was spent crafting urban camouflage intended to hide the wearer from the Kinect computer vision system. By the end of the workshop we understood how to dress to avoid detection for the three different Kinect formats. (via Beta Knowledge)
  2. Starting a Django Project The Right Way (Jeff Knupp) — I wish more people did this: it’s not enough to learn syntax these days. Projects live in a web of best practices for source code management, deployment, testing, and migrations.
  3. FailCona one-day conference for technology entrepreneurs, investors, developers and designers to study their own and others’ failures and prepare for success. Figure out how to learn from failures—they’re far more common than successes. (via Krissy Mo)
  4. Google Fiber in the Real World (Giga Om) — These tests show one of the limitations of Google’s Fiber network: other services. Since Google Fiber is providing virtually unheard of speeds for their subscribers, companies like Apple and I suspect Hulu, Netflix and Amazon will need to keep up. Are you serving DSL speeds to fiber customers? (via Jonathan Brewer)
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