Denver Broncos Testing In-Game Analytics — their newly hired director of analytics working with the coach. With Tanney nearby, Kubiak can receive a quick report on the statistical probabilities of almost any situation. Say that you have fourth-and-3 from the opponent’s 45-yard-line with four minutes to go. Do the large-sample-size percentages make the risk-reward ratio acceptable enough to go for it? Tanney’s analytics can provide insight to aid Kubiak’s decision-making. (via Flowing Data)
Visual Review (GitHub) — Apache-licensed productive and human-friendly workflow for testing and reviewing your Web application’s layout for any regressions.
Buzz: An Extensible Programming Language for Self-Organizing Heterogeneous Robot Swarms (arXiv) — Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as Robot Operating System.
Visualising GoogleNet Classes — fascinating to see squirrel monkeys and basset hounds emerge from nothing. It’s so tempting to say, “this is what the machine sees in its mind when it thinks of basset hounds,” even though Boring Brain says, “that’s bollocks and you know it!”
Speed as a Habit — You don’t have to be militant about it, just consistently respond that today is better than tomorrow, that right now is better than six hours from now. This is chock full of good advice, and the occasional good story.
Punctuated Equilibrium in the Large-Scale Evolution of Programming Languages (PDF) — Here we study the large-scale historical development of programming languages, which have deeply marked social and technological advances in the last half century. We analyse their historical connections using network theory and reconstructed phylogenetic networks. Using both data analysis and network modeling, it is shown that their evolution is highly uneven, marked by innovation events where new languages are created out of improved combinations of different structural components belonging to previous languages. These radiation events occur in a bursty pattern and are tied to novel technological and social niches. The method can be extrapolated to other systems and consistently captures the major classes of languages and the widespread horizontal design exchanges, revealing a punctuated evolutionary path. (via Jarkko Hietaniemi)
How Not to be Wrong: The Power of Mathematical Thinking (Amazon) — Ellenberg chases mathematical threads through a vast range of time and space, from the everyday to the cosmic, encountering, among other things, baseball, Reaganomics, daring lottery schemes, Voltaire, the replicability crisis in psychology, Italian Renaissance painting, artificial languages, the development of non-Euclidean geometry, the coming obesity apocalypse, Antonin Scalia’s views on crime and punishment, the psychology of slime molds, what Facebook can and can’t figure out about you, and the existence of God. (via Pam Fox)
What Turing Himself Said About the Imitation Game (IEEE) — fascinating history. The second myth is that Turing predicted a machine would pass his test around the beginning of this century. What he actually said on the radio in 1952 was that it would be “at least 100 years” before a machine would stand any chance with (as Newman put it) “no questions barred.”
Running Effective Retrospectives — Each change to the team’s workflow is treated as a scientific experiment, whereby a hypothesis is formed, data collected, and expectations compared with actual results.
MarI/O (YouTube) — clear explanation of how an evolutionary algorithm figures out how to play Mario.
Google’s Monastic Vision for the Future of Work (New Yorker) — But it turns out that future-proofed life looks a lot like the vacuum-packed present. […] Inside, it is about turning Google into not only a lifestyle but a fully realized life. The return of the Company Town.
Molecular Programming Project — aims to develop computer science principles for programming information-bearing molecules like DNA and RNA to create artificial biomolecular programs of similar complexity. Our long-term vision is to establish molecular programming as a subdiscipline of computer science — one that will enable a yet-to-be imagined array of applications from chemical circuitry for interacting with biological molecules to nanoscale computing and molecular robotics.
The Software Analysis Workbench — provides the ability to formally verify properties of code written in C, Java, and Cryptol. It leverages automated SAT and SMT solvers to make this process as automated as possible, and provides a scripting language, called SAW Script, to enable verification to scale up to more complex systems. “Non-commercial” license.
What’s Wrong with Deep Learning? (PDF in Google Drive) — What’s missing from deep learning? 1. Theory; 2. Reasoning, structured prediction; 3. Memory, short-term/working/episodic memory; 4. Unsupervised learning that actually works. … and then ways to get those things. Caution: math ahead.
The Basic AI Drives (PDF) — Surely, no harm could come from building a chess-playing robot, could it? In this paper, we argue that such a robot will indeed be dangerous unless it is designed very carefully. Without special precautions, it will resist being turned off, will try to break into other machines and make copies of itself, and will try to acquire resources without regard for anyone else’s safety. These potentially harmful behaviors will occur not because they were programmed in at the start, but because of the intrinsic nature of goal-driven systems.
PreTTY — how to take a good-looking screencap of your terminal app in action.
Internet Trends 2015 (PDF) — Mary Meeker’s preso. Messaging + Notifications = Key Layers of Every Meaningful Mobile App, Messaging Leaders Aiming to Create Cross-Platform Operating Systems That Are Context-Persistent Communications Hubs for More & More Services. This year’s deck feels more superficial, less surprising than in years past.
When the Land Goes Under the Sea — As it turns out: People really despise being told to not replay the game. Almost universally, the reaction to that was a kernel of unhappiness amidst mostly positive reviews. In retrospect, including that note was a mistake for a number of reasons. My favorite part of game postmortems is what the designers learned about how people approach experiences.
Damage Recovery Algorithm for Robots (IEEE) — This illustrates how it’s possible to endow just about any robot with resiliency via this algorithm, as long as it’s got enough degrees of freedom to enable adaptive movement. Because otherwise the Terminators will just stop when we shoot them.
The Counselor — short fiction with ethics, AI, and how good things become questionable.
How to Turn a Liberal Hipster into a Global Capitalist (The Guardian) — In Zoe Svendsen’s play “World Factory at the Young Vic,” the audience becomes the cast. Sixteen teams sit around factory desks playing out a carefully constructed game that requires you to run a clothing factory in China. How to deal with a troublemaker? How to dupe the buyers from ethical retail brands? What to do about the ever-present problem of clients that do not pay? […] And because the theatre captures data on every choice by every team, for every performance, I know we were not alone. The aggregated flowchart reveals that every audience, on every night, veers toward money and away from ethics. I’m a firm believer that games can give you visceral experience, not merely intellectual knowledge, of an activity. Interesting to see it applied so effectively to business.
Why Are Eight Bits Enough for Deep Neural Networks? (Pete Warden) — It turns out that neural networks are different. You can run them with eight-bit parameters and intermediate buffers, and suffer no noticeable loss in the final results. This was astonishing to me, but it’s something that’s been re-discovered over and over again.
The Great Decoupling (HBR) — The Second Machine Age is playing out differently than the First Machine Age, continuing the long-term trend of material abundance but not of ever-greater labor demand.
OpenMP Support in LLVM — OpenMP enables Clang users to harness full power of modern multi-core processors with vector units. Pragmas from OpenMP 3.1 provide an industry standard way to employ task parallelism, while ‘#pragma omp simd’ is a simple yet flexible way to enable data parallelism (aka vectorization).