Anthropic Capitalism And The New Gimmick Economy — market capitalism struggles with “public goods” (those which are inexhaustible and non-excludable, like infinitely copyable bits that any number of people can have copies of at once), yet much of the world is being recast as an activity where software manipulates information, thus becoming a public good. Capitalism and Communism, which briefly resembled victor and vanquished, increasingly look more like Thelma and Louise; a tragic couple sent over the edge by forces beyond their control. What comes next is anyone’s guess and the world hangs in the balance.
We Robot Conference Roundup — videos of talks on How to Engage the Public on the Ethics and Governance of Lethal Autonomous Weapons and other subjects. (You had my attention at “lethal autonomous weapons.”)
The Reality of AR/VR Business Models (TechCrunch) — list of potential revenue streams and superficial analysis of what they might look like in practice: hardware, e-commerce, advertising, mobile data/voice, in-app purchases, subscriptions, enterprise/b2b, and premium apps.
Deep Learning Book — text finished, prepping print production via MIT Press. Why are you using HTML format for the drafts? This format is a sort of weak DRM required by our contract with MIT Press. It’s intended to discourage unauthorized copying/editing of the book.
Dragon: A Distributed Graph Query Engine — Facebook describes its internal graph query engine. [T]he layout of these indices on storage is optimized based on a deeper understanding of query patterns (e.g., many queries are about friends), as opposed to accepting random sharding, which is common in these systems. Wisely, the system is tailored to the use cases they have and the patterns they see in access.
Almost Everyone Is Doing the API Economy Wrong (Techcrunch) — Redux: your API should help you make money when the API customer makes money, and you should set clear expectations for what’s acceptable and what’s not. But every developer should be forced to write 100 times: “if you build on a platform you don’t own, you’re building on a potential and probable future competitor.”
Traditional Economics Failed, Here’s a Blueprint — runs through the shifts happening in our thinking about the world and ourselves (simple to complex, independent to interdependent, rational calculator to irrational approximators, etc) and concludes: True self-interest is mutual interest. The best way to improve your likelihood of surviving and thriving is to make sure those around you survive and thrive. See above API note.
Blitzscaling (HBR) — as you move from village to city, functions are beginning to be differentiated; you’re really multithreading. I could write a thesis on the CAP theorem for business. And I have definitely worked for companies that have a “share nothing” approach to solving their threading issues.
Algorithm Identifies Tweets Sent Under the Influence of Alcohol (MIT TR) — notable for how they determined whether a Tweet was sent from home. They made a list of phrases like “home at last!” and had MTurkers confirm the Tweets were about being home, then used those as training data for an algorithm to identify other Tweets talking about home.
Puzzle Game to Help Program Quantum Computers (New Scientist) — Devitt has turned the problem of programming a quantum computer into a game called meQuanics. His team has developed a prototype to test the game, which you can play now, and today launched a Kickstarter campaign to fund a fully fledged version for iOS and Android phones.
The Secrets of Surveillance Capitalism — The assault on behavioral data is so sweeping that it can no longer be circumscribed by the concept of privacy and its contests. […] First, the push for more users and more channels, services, devices, places, and spaces is imperative for access to an ever-expanding range of behavioral surplus. Users are the human nature-al resource that provides this free raw material. Second, the application of machine learning, artificial intelligence, and data science for continuous algorithmic improvement constitutes an immensely expensive, sophisticated, and exclusive 21st century “means of production.” Third, the new manufacturing process converts behavioral surplus into prediction products designed to predict behavior now and soon. Fourth, these prediction products are sold into a new kind of meta-market that trades exclusively in future behavior. The better (more predictive) the product, the lower the risks for buyers, and the greater the volume of sales. Surveillance capitalism’s profits derive primarily, if not entirely, from such markets for future behavior. (via Simon St Laurent)
Thunder — Spark-driven analysis from Jupyter notebooks (open source).
What Google Learned From Its Quest to Build the Perfect Team (NY Times) — As the researchers studied the groups, however, they noticed two behaviors that all the good teams generally shared. First, on the good teams, members spoke in roughly the same proportion […] Second, the good teams all had high ‘‘average social sensitivity’’ — a fancy way of saying they were skilled at intuiting how others felt based on their tone of voice, their expressions, and other nonverbal cues.
Photo Geolocation with Convolutional Neural Networks (arXiv) — 377MB gets you a neural net, trained on geotagged Web images, that can suggest location of the image. From MIT TR’s coverage: To measure the accuracy of their machine, they fed it 2.3 million geotagged images from Flickr to see whether it could correctly determine their location. “PlaNet is able to localize 3.6% of the images at street-level accuracy and 10.1% at city-level accuracy,” say Weyand and co. What’s more, the machine determines the country of origin in a further 28.4% of the photos and the continent in 48.0% of them.
Values in Practice (Camille Fournier) — At some point, I realized there was a pattern. The people in the company who were beloved by all, happiest in their jobs, and arguably most productive, were the people who showed up for all of these values. They may not have been the people who went to the best schools, or who wrote the most beautiful code; in fact, they often weren’t the “on-paper” superstars. But when it came to the job, they were great, highly in-demand, and usually promoted quickly. They didn’t all look the same, they didn’t all work in the same team or have the same skill set. Their only common thread was that they didn’t have to stretch too much to live the company values because the company values overlapped with their own personal values.
Exoskeletons Must be Covered by Health Insurance (VICE) — A medical review board ruled that a health insurance provider in the United States is obligated to provide coverage and reimbursement for a $69,500 ReWalk robotic exoskeleton, in what could be a major turning point for people with spinal cord injuries. (via Robohub)
New Models for the Company of the 21st Century (Simone Brunozzi) — large companies often get displaced by new entrants, failing to innovate and/or adapt to new technologies. Y-Combinator can be seen as a new type of company, where innovation is brought in as an entrepreneurial experiment, largely for seed-stage ideas; Google’s Alphabet, on the other hand, tries to stimulate innovation and risk by dividing a large company into smaller pieces and reassigning ownership and responsibilities to different CEOs.
Zephyr — Linux Foundation’s IoT open source OS project. tbh, I don’t see people complaining about operating systems. Integrating all these devices (and having the sensors actually usefully capturing what you want) seems the bigger problem. We already have fragmentation (is it a Samsung home or a Nest home?), and as more Big Swinging Click companies enter the world of smarter things, this will only get worse before it gets better.
Elemental Machines — Boston startup fitting experiments & experimenters with sensors, deep learning to identify problems (vibration, humidity, etc.) that could trigger experimental failure. [C]rucial experiments are often delayed by things that seem trivial in retrospect. “I talked to my friends who worked in labs,” Iyengar says. “Everyone had a story to tell.” One scientist’s polymer was unstable because of ultraviolet light coming through a nearby window, he says; that took six months to debug. Another friend who worked at a pharmaceutical company was testing drug candidates in mice. The results were one failure after another, for months, until someone figured out that the lab next door was being renovated, and after-hours construction was keeping the mice awake and stressing them out. (that quote from Xconomy)
Usborne Computer and Coding Books — not only do they have sweet Scratch books for kids, they also have their nostalgia-dripping 1980s microcomputer books online. I still have a pile of my well-loved originals.
Powerful People are Terrible at Making Decisions Together — Researchers from the Haas School of Business at the University of California, Berkeley, undertook an experiment with a group of health care executives on a leadership retreat. They broke them into groups, presented them with a list of fictional job candidates, and asked them to recommend one to their CEO. The discussions were recorded and evaluated by independent reviewers. The higher the concentration of high-ranking executives, the more a group struggled to complete the task. They competed for status, were less focused on the assignment, and tended to share less information with each other.
MyBinder — turn a GitHub repo into a collection of interactive notebooks powered by Jupyter and Kubernetes.
The Mortality of Companies — Geoffrey West paper: we show that the mortality of publicly traded companies manifests an approximately constant hazard rate over long periods of observation. This regularity indicates that mortality rates are independent of a company’s age. We show that the typical half-life of a publicly traded company is about a decade, regardless of business sector.
Gizmo — a microservice toolkit in Golang from NYT. (via InfoQ)
Intellectual Need and Problem-Free Activity in the Mathematics Classroom (PDF) — Although this is not an empirical study, we use data from observed high school algebra classrooms to illustrate four categories of activity students engage in while feeling little or no intellectual need. We present multiple examples for each category in order to draw out different nuances of the activity, and we contrast the observed situations with ones that would provide various types of intellectual need. Finally, we offer general suggestions for teaching with intellectual need.