Behind the Hoverboard Craze (BoingBoing) — Bernstein is interested in this phenomenon as “memeufacturing” — a couple of social-media stars (or garden-variety celebs) post viral videos of themselves using an obscure gadget, and halfway around the world, factories shut down their e-cig lines and convert them, almost overnight, to hoverboard manufacturing lines. Bernstein cites a source who says that there are 1,000 hoverboard factories in South China.
neural-vqa — VIS+LSTM model for Visual Question Answering. Scroll to the end and see the questions it’s answering about photos.
Open Season in Editing Genes of Animals (NY Times) — “We’re going to see a stream of edited animals coming through because it’s so easy,” said Bruce Whitelaw, a professor of animal biotechnology at the Roslin Institute at the University of Edinburgh. “It’s going to change the societal question from, ‘If we could do it, would we want it?’ to, ‘Next year we will have it; will we allow it?’”
RTS AI (PDF) — standard techniques used for playing classic board games, such as game tree search, cannot be directly applied to solve RTS games without the definition of some level of abstraction, or some other simplification. Interestingly enough, humans seem to be able to deal with the complexity of RTS games, and are still vastly superior to computers in these types of games. Talks about the challenges in writing AIs for Real-Time Strategy games.
Algorithms for Affective Sensing — Results show that the system achieves a six-emotion decision-level correct classification rate of 80% for an acted dataset with clean speech. This PhD thesis is research into algorithm for determining emotion from speech samples, which does so more accurately than humans in a controlled test. (via New Scientist)
DeepDive — Stanford project to create structured data (SQL tables) from unstructured information (text documents) and integrate such data with an existing structured database. DeepDive is used to extract sophisticated relationships between entities and make inferences about facts involving those entities. Code is open source (Apache v2 license). (via Infoworld)
Visual Microphone (MIT) — turn everyday objects — a glass of water, a potted plant, a box of tissues, or a bag of chips — into visual microphones using high-speed photography to detect the small vibrations caused by sound. (via Infoworld)
GIF It Up — very clever remix campaign to use heritage content—Friday is your last day to enter this year’s contest, so get creating! My favourite.
Uber’s Drivers: Information Asymmetries and Control in Dynamic Work — Our conclusions are two-fold: first, that the information asymmetries produced by Uber’s system are fundamental to its ability to structure indirect control over its workers; and second, that Uber relies heavily on the evolving rhetoric of the algorithm to justify these information asymmetries to drivers, riders, as well as regulators and outlets of public opinion.
ANNABELL — unsupervised language learning using artificial neural networks, install your own four year old. The paper explains how.
Spinnaker — an open source, multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence.
Hospital Hacking (Bloomberg) — interesting for both lax regulation (“The FDA seems to literally be waiting for someone to be killed before they can say, ‘OK, yeah, this is something we need to worry about,’ ” Rios says.) and the extent of the problem (Last fall, analysts with TrapX Security, a firm based in San Mateo, Calif., began installing software in more than 60 hospitals to trace medical device hacks. […] After six months, TrapX concluded that all of the hospitals contained medical devices that had been infected by malware.). It may take a Vice President’s defibrillator being hacked for things to change. Or would anybody notice?
The O-Ring Theory of DevOps (Adrian Colyer) — Small differences in quality (i.e, in how quickly and accurately you perform each stage of your DevOps pipeline) quickly compound to make very large differences between the performance of the best-in-class and the rest.
Low-Power Deep Learning — it’s a media release for proprietary tech, but interesting that people are working on low-power deep learning neural nets. As Pete Warden noted, this kind of research will be at the center of smart sensors. (via Pete Warden)
Tesla’s Self-Improving Autopilot — it learns when you “rescue” (aka take control back from autopilot), so it’s getting better day by day. Musk said that Model S owners could add ~1 million miles of new data every day, which is helping the company create “high-precision maps.” Navteq, Google Maps, Waze … new map data is still valuable.
The Digital Revolution in Higher Education Has Already Happened (Clay Shirky) — and no-one noticed. I read half of this before going “holy crap this is good, who wrote it?” I’m a Shirky junkie (I bet his laundry lists cite Habermas and the Peace of Westphalia). At the current rate of growth, half the country’s undergraduates will have at least one online class on their transcripts by the end of the decade. This is the new normal. But, As long as we discuss online education as a pedagogic revolution rather than an organizational one, we aren’t even having the right kind of conversation. The dramatic adoption of online education is not mainly a change in the content of classes. It’s a change in the institutional form of college, a demand for more flexibility by students who have to manage the increasingly complicated triangle of work, family, and school.
System Automatically Converts 2-D to 3-D (MIT) — hilarious strategy! They constrained their domain: broadcast soccer games. The MIT and QCRI researchers essentially ran this process in reverse. They set the very realistic Microsoft soccer game “FIFA13” to play over and over again, and used Microsoft’s video-game analysis tool PIX to continuously store screen shots of the action. For each screen shot, they also extracted the corresponding 3-D map. […] For every frame of 2-D video of an actual soccer game, the system looks for the 10 or so screen shots in the database that best correspond to it. Then it decomposes all those images, looking for the best matches between smaller regions of the video feed and smaller regions of the screen shots. Once it’s found those matches, it superimposes the depth information from the screen shots on the corresponding sections of the video feed. Finally, it stitches the pieces back together. Brute-forcing soccer. Ok, perhaps “hilarious” for a certain type of person. I am that person.
Gmail Suggesting Replies — In developing Smart Reply, we adhered to the same rigorous user privacy standards we’ve always held — in other words, no humans reading your email. This means researchers have to get machine learning to work on a data set that they themselves cannot read, which is a little like trying to solve a puzzle while blindfolded — but a challenge makes it more interesting!
The Selective Laziness of Reasoning — Among those participants who accepted the manipulation and thus thought they were evaluating someone else’s argument, more than half (56% and 58%) rejected the arguments that were in fact their own. Moreover, participants were more likely to reject their own arguments for invalid than for valid answers. This demonstrates that people are more critical of other people’s arguments than of their own, without being overly critical: They are better able to tell valid from invalid arguments when the arguments are someone else’s rather than their own.
How Big is the Gig Economy? (Medium) — this is one example in which the Labor Department and Bureau of Labor Statistics really have shirked their responsibility to try and assess the size and growth of this dynamic shift to our economy.
The Twelve Networking Truths — RFC1925 is channeling the epigram-leaking protagonist of Robert Heinlein’s Time Enough for Love. It is easier to move a problem around (for example, by moving the problem to a different part of the overall network architecture) than it is to solve it. This is true for most areas of life: generally easier to make it someone else’s problem than to solve it.
The Decay of Twitter (The Atlantic) — In other words, on Twitter, people say things that they think of as ephemeral and chatty. Their utterances are then treated as unequivocal political statements by people outside the conversation. Because there’s a kind of sensationalistic value in interpreting someone’s chattiness in partisan terms, tweets “are taken up as magnum opi to be leapt upon and eviscerated, not only by ideological opponents or threatened employers but by in-network peers.”