- 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.
- Vaurien the Chaos TCP Monkey — a 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)
- 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)
- 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.
"computer vision" entries
Informed Citizenry, TCP Chaos Monkey, Photographic Forensics, Medical Trial Data
Who will pay damages when a driverless car gets into an accident?
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. Read more…
Thwarting Facial Recognition Software, Operations Security, Password Cracking SCADA Systems, and Wearables Evolved
- 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.
- opsec for Hackers (Slideshare) — how boring and unexciting most of not getting caught is.
- 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).
- 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”.
Deblurring Images, Games Design, Secure Control, and Faster Emulation
- Restoration of Defocused and Blurry Images — impressive demos, and open source (GPLv3) code. All those blurred faces and documents no longer seem so safe.
- 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)
- 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)
- Android Virtual Machines — faster emulation for testing than the traditional simulators.
CV Camouflage, Best Practices, Failure Conference, and Fiber Lessons
- Urban Camouflage Workshop — Most 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)
- 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.
- FailCon — a 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)
- 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)
Jan Erik Solem describes elements and useful tools for computer vision
In this interview, Jan Erik Solem, author of the upcoming book "Programming Computer Vision with Python," describes the uses for some common operations, and choices programmers have.
Internet Cafe Culture, Image Processing, Library Mining, and MediaWiki Parsing
- Chinese Internet Cafes (Bryce Roberts) — a good quick read. My note: people valued the same things in Internet cafes that they value in public libraries, and the uses are very similar. They pose a similar threat to the already-successful, which is why public libraries are threatened in many Western countries.
- SIFT — the Scale Invariant Feature Transform library, built on OpenCV, is a method to detect distinctive, invariant image feature points, which easily can be matched between images to perform tasks such as object detection and recognition, or to compute geometrical transformations between images. The licensing seems dodgy–MIT code but lots of “this isn’t a license to use the patent!” warnings in the LICENSE file. (via Joshua Schachter)
- The Secret Life of Libraries (Guardian) — I like the idea of the most-stolen-books revealing something about a region; it’s an aspect of data revealing truth. For a while, Terry Pratchett was the most-shoplifted author in England but newspapers rarely carried articles about him or mentioned his books (because they were genre fiction not “real” literature). (via Brian Flaherty)
- Sweble — MediaWiki parser library. Until today, Wikitext had been poorly defined. There was no grammar, no defined processing rules, and no defined output like a DOM tree based on a well defined document object model. This is to say, the content of Wikipedia is stored in a format that is not an open standard. The format is defined by 5000 lines of php code (the parse function of MediaWiki). That code may be open source, but it is incomprehensible to most. That’s why there are 30+ failed attempts at writing alternative parsers. (via Dirk Riehle)
Long Tail, Copyright vs Preservation, Diminished Reality, and Augmented Data
- Mechanical Turk Requester Activity: The Insignificance of the Long Tail — For Wikipedia we have the 1% rule, where 1% of the contributors (this is 0.003% of the users) contribute two thirds of the content. In the Causes application on Facebook, there are 25 million users, but only 1% of them contribute a donation. […] The lognormal distribution of activity, also shows that requesters increase their participation exponentially over time: They post a few tasks, they get the results. If the results are good, they increase by a percentage the size of the tasks that they post next time. This multiplicative behavior is the basic process that generates the lognormal distribution of activity.
- Copyright Destroying Historic Audio — so says the Library of Congress. Were copyright law followed to the letter, little audio preservation would be undertaken. Were the law strictly enforced, it would brand virtually all audio preservation as illegal. Copyright laws related to preservation are neither strictly followed nor strictly enforced. Consequently, some audio preservation is conducted.
- Diminished Reality (Ray Kurzweil) — removes objects from video in real time. Great name, “diminished reality”. (via Andy Baio)
- Data Enrichment Service — using linked government data to augment text with annotations and links. (via Jo Walsh on Twitter)