Meet DJ Patil — “It was this kind of moment when you realize: ‘Oh, my gosh, I am that stupid,’” he said.
Interview with Bruce Sterling on the Convergence of Humans and Machines — If 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)
Giving Robots and Prostheses the Human Touch — the 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.”
boltons — things in Python that should have been builtins.
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.
The Internet of Kafkaesque Things (ACLU) — As computers are deployed in more regulatory roles, and therefore make more judgments about us, we may be afflicted with many more of the rigid, unjust rulings for which bureaucracies are so notorious.
Complete Force Control in Constrained Under-actuated Mechanical Systems (Robohub) — Nori focuses on finding ways to advance the dynamic system of a robot – the forces that interact and make the system move. Key to developing dynamic movements in a robot is control, accompanied by the way the robot interacts with the environment. Nori talks us through the latest developments, designs, and formulas for floating-base/constrained mechanical systems, whole-body motion control of humanoid systems, whole-body dynamics computation on the iCub humanoid, and finishes with a video on recent implementations of whole-body motion control on the iCub. Video and download of presentation.
Unpowered Ankle Exoskeleton — “As we understand human biomechanics better, we’ve begun to see wearable robotic devices that can restore or enhance human motor performance,” says Collins. “This bodes well for a future with devices that are lightweight, energy-efficient, and relatively inexpensive, yet enhance human mobility.”
Body-Powered Processing (Ars Technica) — The new SAM L21 32-bit ARM family of microcontroller (MCUs) consume less than 35 microamps of power per megahertz of processing speed while active, and less than 200 nanoamps of power overall when in deep sleep mode—with varying states in between. The chip is so low power that it can be powered off energy capture from the body. (via Greg Linden)
The Shut-In Economy — In 1998, Carnegie Mellon researchers warned that the Internet could make us into hermits. They released a study monitoring the social behavior of 169 people making their first forays online. The Web-surfers started talking less with family and friends, and grew more isolated and depressed. “We were surprised to find that what is a social technology has such anti-social consequences,” said one of the researchers at the time. “And these are the same people who, when asked, describe the Internet as a positive thing.”
Thoughts on Amazon Dash (Matt Webb) — In a way, we’re really seeing the future of marketing here. We’ve separated awareness (advertising) and distribution (stores) for so long, but it’s no longer the way. When you get a Buy Now button in a Tweet, you’re seeing ads and distribution merging, and the Button is the physical instantiation of this same trend. […] in the future every product will carry a buy button.
Lightning Networks (Rusty Russell) — I finally took a second swing at understanding the Lightning Network paper. The promise of this work is exceptional: instant, reliable transactions across the bitcoin network. But the implementation is complex, and the draft paper reads like a grab bag of ideas; but it truly rewards close reading! It doesn’t involve novel crypto, nor fancy bitcoin scripting tricks. There are several techniques that are used in the paper, so I plan to concentrate on one per post and wrap up at the end. Already posted part II.
Facebook’s Mystery Machine — The goal of this paper is very similar to that of Google Dapper[…]. Both work [to] try to figure out bottlenecks in performance in high fanout large-scale Internet services. Both work us[ing] similar methods, however this work (the mystery machine) tries to accomplish the task relying on less instrumentation than Google Dapper. The novelty of the mystery machine work is that it tries to infer the component call graph implicitly via mining the logs, where as Google Dapper instrumented each call in a meticulous manner and explicitly obtained the entire call graph.
The Multiple Lives of Moore’s Law — A shrinking transistor not only allowed more components to be crammed onto an integrated circuit but also made those transistors faster and less power hungry. This single factor has been responsible for much of the staying power of Moore’s Law, and it’s lasted through two very different incarnations. In the early days, a phase I call Moore’s Law 1.0, progress came by “scaling up”—adding more components to a chip. At first, the goal was simply to gobble up the discrete components of existing applications and put them in one reliable and inexpensive package. As a result, chips got bigger and more complex. The microprocessor, which emerged in the early 1970s, exemplifies this phase. But over the last few decades, progress in the semiconductor industry became dominated by Moore’s Law 2.0. This era is all about “scaling down,” driving down the size and cost of transistors even if the number of transistors per chip does not go up.
Choose Boring Technology (Dan McKinley) — Adding technology to your company comes with a cost. As an abstract statement this is obvious: if we’re already using Ruby, adding Python to the mix doesn’t feel sensible because the resulting complexity would outweigh Python’s marginal utility. But somehow when we’re talking about Python and Scala or MySQL and Redis, people lose their minds, discard all constraints, and start raving about using the best tool for the job.
Sharing our Engineering Ladder — In addition to the ladder causing problems inside of my team, we were having a hard time evaluating candidates during interviews and determining what level to hire them into. Particularly at the more senior levels, it wasn’t clear what the criteria for success really looked like. So, together with my tech leads and engineering managers, we rewrote the ladder to be more specific. It has been very helpful both for the process of reviews and promotion committees as well as for the process of hiring.
Ikea’s flat-pack refugee shelter is entering production (The Verge) — The UNHCR has agreed to buy 10,000 of the shelters, and will begin providing them to refugee families this summer. […] Measuring about 188 square feet, each shelter accommodates five people and includes a rooftop solar panel that powers a built-in lamp and USB outlet. The structure ships just like any other piece of Ikea furniture, with insulated, lightweight polymer panels, pipes, and wires packed into a cardboard box. According to Ikea, it only takes about four hours to assemble.
The Trolley and the Psychopath — Not only does a “utilitarian” response (“just kill the fat guy”) not actually reflect a utilitarian outlook, it may actually be driven by broad antisocial tendencies, such as lowered empathy and a reduced aversion to causing someone harm. Questionably expanding scope of claims in the behavioural philosophy research. (via Ed Yong)
Exploit Exercises — a variety of virtual machines, documentation, and challenges that can be used to learn about a variety of computer security issues, such as privilege escalation, vulnerability analysis, exploit development, debugging, reverse engineering, and general cyber security issues.
Sirius — UMich open source “intelligent Personal Assistant” (aka Siri, Cortana, Google Now, etc.). Text recognition, image recognition, query processing components. They hope it’ll be a focal point for research in the area, the way that open source operating systems have focused university research.
MIT DragonBot Evolving to Teach Kids (IEEE Spectrum) — they’re moving from “Wizard of Oz” (humans-behind-the-scenes) control to autonomous operation. Lovely example of Flintstoning in a robotics context.
Personal Assistants Coming (Robohub) — 2015 is the year physical products will be coming to market and available for experimentation and testing. Pepper ships in the summer in Japan, JIBO ships preorders in Q3, as does Cubic in the fall and EmoSpark in the summer. […]The key to the outcome of this race is whether a general purpose AI will be able to steer people through their digital world, or whether users would rather navigate to applications that are specialists (such as American Airlines or Dominos Pizza).
There Is No Now — One of the most important results in the theory of distributed systems is an impossibility result, showing one of the limits of the ability to build systems that work in a world where things can fail. This is generally referred to as the FLP result, named for its authors, Fischer, Lynch, and Paterson. Their work, which won the 2001 Dijkstra Prize for the most influential paper in distributed computing, showed conclusively that some computational problems that are achievable in a “synchronous” model in which hosts have identical or shared clocks are impossible under a weaker, asynchronous system model.
Deep Learning Hardware Guide — One of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. Here I will guide you step by step through the hardware you will need for a cheap high performance system.
The Internet of Things That Do What You Tell Them: Cory Doctorow passionately explains how computers are already entwined in our lives, which means laws that support lock-in are much more than inconveniences.