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.
Designing the Human-Robot Relationship (O’Reilly) — We can use those same principles [Jakob Nielsen’s usability heuristics] and look for implications of robots serving our higher ordered needs, as we move from serving needs related to convenience or performance to actually supporting our decision making to emerging technologies, moving from being able to do anything or be magic in terms of the user interface to being more human in the user interface.
Why Are Geospatial Databases So Hard To Build? — Algorithms in computer science, with rare exception, leverage properties unique to one-dimensional scalar data models. In other words, data types you can abstractly represent as an integer. Even when scalar data types are multidimensional, they can often be mapped to one dimension. This works well, as the majority of [what] data people care about can be represented with scalar types. If your data model is inherently non-scalar, you enter an algorithm wasteland in the computer science literature.
The Web’s Grain (Frank Chimero) — What would happen if we stopped treating the web like a blank canvas to paint on, and instead like a material to build with?
Bruce Sterling on Convergence of Humans and Machines — I like to use the terms “cognition” and “computation”. Cognition is something that happens in brains, physical, biological brains. Computation is a thing that happens with software strings on electronic tracks that are inscribed out of silicon and put on fibre board. They are not the same thing, and saying that makes the same mistake as in earlier times, when people said that human thought was like a steam engine.
Smart Pocket Watch — I love to see people trying different design experiences. This is beautiful. And built on Firefox OS!
Knowledge-Based Trust (PDF) — Google research paper on how to assess factual accuracy of web page content. It was bad enough when Google incentivised people to make content-free pages. Next there’ll be a reward for scamming bogus facts into Google’s facts database.
Machine Learning Done Wrong — When dealing with small amounts of data, it’s reasonable to try as many algorithms as possible and to pick the best one since the cost of experimentation is low. But as we hit “big data,” it pays off to analyze the data upfront and then design the modeling pipeline (pre-processing, modeling, optimization algorithm, evaluation, productionization) accordingly.
Ten Simple Rules for Lifelong Learning According to Richard Hamming (PLoScompBio) — Exponential growth of the amount of knowledge is a central feature of the modern era. As Hamming points out, since the time of Isaac Newton (1642/3-1726/7), the total amount of knowledge (including but not limited to technical fields) has doubled about every 17 years. At the same time, the half-life of technical knowledge has been estimated to be about 15 years. If the total amount of knowledge available today is x, then in 15 years the total amount of knowledge can be expected to be nearly 2x, while the amount of knowledge that has become obsolete will be about 0.5x. This means that the total amount of knowledge thought to be valid has increased from x to nearly 1.5x. Taken together, this means that if your daughter or son was born when you were 34 years old, the amount of knowledge she or he will be faced with on entering university at age 17 will be more than twice the amount you faced when you started college.
Apache NiFi — incubated open source project for data flow.
Tug Hospital Robot (Wired) — It may have an adult voice, but Tug has a childlike air, even though in this hospital you’re supposed to treat it like a wheelchair-bound old lady. It’s just so innocent, so earnest, and at times, a bit helpless. If there’s enough stuff blocking its way in a corridor, for instance, it can’t reroute around the obstruction. This happened to the Tug we were trailing in pediatrics. “Oh, something’s in its way!” a woman in scrubs says with an expression like she herself had ruined the robot’s day. She tries moving the wheeled contraption but it won’t budge. “Uh, oh!” She shoves on it some more and finally gets it to move. “Go, Tug, go!” she exclaims as the robot, true to its programming, continues down the hall.
Improving the Robustness of Complex Networks with Preserving Community Structure (PLoSone) — To improve robustness while minimizing the above three costly changes, we first seek to verify that the community structure of networks actually do identify the robustness and vulnerability of networks to some extent. Then, we propose an effective 3-step strategy for robustness improvement, which retains the degree distribution of a network, as well as preserves its community structure.
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.