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"programming" entries
Explaining Classifier Predictions, Formatting Currency, Questioning Magic Leap, and Curing Slack Addiction
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Why Should I Trust You?: Explaining the Predictions of Any Classifier (PDF) — LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. Torkington’s Second Law: there’s no problem with machine learning that more machine learning can’t fix.
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How Etsy Formats Currency — I’m saving this one because it chafes every time I do it, and I do it wrong every time.
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Magic Leap in Wired — massive story by Kevin Kelly on the glories of Magic Leap, which The Verge noted still left a lot of open questions, such as “what the hell IS Magic Leap’s technology” and “why does everyone who works for Magic Leap sound like they’re on acid when they talk about the technology?” Everyone who wants their pixel-free glorious VR to be true is crossing fingers hoping it’s not another Theranos. The bit that stuck from the Wired piece was People remember VR experiences not as a memory of something they saw but as something that happened to them.
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Curing Our Slack Addiction — an interesting counterpoint to the “in the future everyone will be on 15,000 Slacks” Slack-maximalist view. For AgileBits, it distracted, facilitated, and rewarded distracting behaviour, ultimately becoming a drain rather than an accelerant.
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Car Hackers Handbook, Exoskeleton Regulation, Pythonic Spreadsheet, and AI Myths
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The 2016 Car Hacker’s Handbook (Amazon) — will give you a deeper understanding of the computer systems and embedded software in modern vehicles. It begins by examining vulnerabilities and providing detailed explanations of communications over the CAN bus and between devices and systems. (via BoingBoing)
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More Exoskeletons Seeking FDA Approval — The international group of exoskeleton providers with various FDA or CE certifications is growing and currently includes: Ekso in the US; Cyberdyne in the EU and Japan; ExoAtlet from Russia; and Israel’s ReWalk. Other providers are in the process of getting approvals or developing commercial versions of their products. My eye was caught by how global the list of exoskeleton companies is.
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Dirigible Spreadsheet — open source spreadsheet that’s not just written in Python, it exposes and IS python. See also Harry Percival talking about it.
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Everything You Know About AI Is Wrong (Gizmodo) — an interesting run-through of myths and claims about AI. I’m not ready to consider all of these “busted,” but they are some nice starters-for-ten in your next pub argument about whether the Matrix is coming.
Deep-Learning Catan, Scala Tutorials, Legal Services, and Shiny Echo
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Strategic Dialogue Management via Deep Reinforcement Learning (Adrian Colyer) — a neural network learns to play Settlers of Catan. Is nothing sacred?
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scala school — Twitter’s instructional material for coming up to speed on scala.
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Robin Hood Fellowship — fellowship to use technology to increase access to legal services for New Yorkers. Stuff that matters.
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The Echo From Amazon Brims With Groundbreaking Promise (NY Times) — A bit more than a year after its release, the Echo has morphed from a gimmicky experiment into a device that brims with profound possibility. The longer I use it, the more regularly it inspires the same sense of promise I felt when I used the first iPhone — a sense this machine is opening up a vast new realm in personal computing, and gently expanding the role that computers will play in our future.
Sensing Cognitive Load, Boring is Good, Replicating SQLite, and Intro to Autonomous Robots
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An Adaptive Learning Interface that Adjusts Task Difficulty based on Brain State (PDF) — using blood flow to measure cognitive load, this tool releases new lessons to you when you’re ready for them. The system measures blood flow using functional near-infrared spectroscopy (fNIRS). Increased activation in an area of the brain results in increased levels of oxyhemoglobin. These changes can be measured by emitting frequencies of near-infrared light around 3 cm deep into the brain tissue and measuring the light attenuation caused by levels of oxyhemoglobin. I think we all want a widget on our computer that says “your brain is full, go offline to recover,” if only to validate naptime.
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Deploying Software — Your deploys should be as boring, straightforward, and stress-free as possible. cf Maciej Ceglowski’s “if you find it interesting, it doesn’t belong in production.”
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Replicating SQLite Using Raft — rqlite is written in Go and uses Raft to achieve consensus across all the instances of the SQLite databases. rqlite ensures that every change made to the database is made to a quorum of SQLite files, or none at all.
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An Introduction to Autonomous Robots — An open textbook focusing on computational principles of autonomous robots. CC-NC-ND and for sale via Amazon.
Security Advice, Common Deep Learning Interface, React Text Editing, and Sexy Docs
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Free Security Advice (grugq) — chap wearies of handing out security advice, so gathers it and shares for all.
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TensorFuse — Common interface for Theano, CGT, and TensorFlow.
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Draft.js — a framework for building rich text editors in React, powered by an immutable model and abstracting over cross-browser differences.
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Dexy — a free-form literate documentation tool for writing any kind of technical document incorporating code. Dexy helps you write correct documents, and to easily maintain them over time as your code changes.
Surviving Crashes, Thumbs-Up Thumbs-Down Learning, Faster Homomorphic Encryption, and Nerdy V-Day Cards
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All File Systems are Not Created Equal: On the Complexity of Crafting Crash Consistent Applications (Paper a Day) — an important subject for me. BOB, the Block Order Breaker, is used to find out what behaviours are exhibited by a number of modern file systems that are relevant to building crash consistent applications. ALICE, the Application Level Intelligent Crash Explorer, is then used to explore the crash recovery behaviour of a number of applications on top of these file systems.
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BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 (Arxiv) — instead of complex positive/negative floating-point weights, this uses +1 and -1 (which I can’t help but think of as “thumbs up”, “thumbs down”) to get nearly state-of-the-art results because a run-time, BinaryNet drastically reduces memory usage and replaces most multiplications by 1-bit exclusive-not-or (XNOR) operations, which might have a big impact on both general-purpose and dedicated Deep Learning hardware. GPLv2 code available.
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Microsoft Speeds Up Homomorphic Encryption (The Register) — homomorphic encryption lets databases crunch data without needing keys to decode it.
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Nerdy Valentine Cards (Evil Mad Scientist) — for a nerd in your life. (via Cory Doctorow)
Experimental Support, Coding Books, Bad Decisions, and GitHub to Jupyter
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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)
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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.
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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.
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MyBinder — turn a GitHub repo into a collection of interactive notebooks powered by Jupyter and Kubernetes.
Augmented Intelligence, Social Network Limits, Microsoft Research, and Google's Go
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Chimera (Paper a Day) — the authors summarise six main lessons learned while building Chimera: (1) Things break down at large scale; (2) Both learning and hand-crafted rules are critical; (3) Crowdsourcing is critical, but must be closely monitored; (4) Crowdsourcing must be coupled with in-house analysts and developers; (5) Outsourcing does not work at a very large scale; (6) Hybrid human-machine systems are here to stay.
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Do Online Social Media Remove Constraints That Limit the Size of Offline Social Networks? (Royal Society) — paper by Robin Dunbar. Answer: The data show that the size and range of online egocentric social networks, indexed as the number of Facebook friends, is similar to that of offline face-to-face networks.
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Microsoft Embedding Research — To break down the walls between its research group and the rest of the company, Microsoft reassigned about half of its more than 1,000 research staff in September 2014 to a new group called MSR NExT. Its focus is on projects with greater impact to the company rather than pure research. Meanwhile, the other half of Microsoft Research is getting pushed to find more significant ways it can contribute to the company’s products. The challenge is how to avoid short-term thinking from your research team. For instance, Facebook assigns some staff to focus on long-term research, and Google’s DeepMind group in London conducts pure AI research without immediate commercial considerations.
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Google’s Go-Playing AI — The key to AlphaGo is reducing the enormous search space to something more manageable. To do this, it combines a state-of-the-art tree search with two deep neural networks, each of which contains many layers with millions of neuron-like connections. One neural network, the “policy network,” predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win. The other neural network, the “value network,” is then used to reduce the depth of the search tree — estimating the winner in each position in place of searching all the way to the end of the game.
Modern C, Colorizing Photos, Flashing Toy Drones, and Web + Native
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How to C in 2016 — straightforward recommendations for writing C if you have to.
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Using Deep Learning to Colorize Old Photos — comes with a trained TensorFlow model to play with.
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Open Source Firmware for Toy Drones — The Eachine H8 is a typical-looking mini-quadcopter of the kind that sell for under $20.[…] takes you through a step-by-step guide to re-flashing the device with a custom firmware to enable acrobatics, or simply to tweak the throttle-to-engine-speed mapping for the quad. (via DIY Drones)
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Mobile Web vs. Native Apps or Why You Want Both (Luke Wroblewski) — The Web is for audience reach and native apps are for rich experiences. Both are strategic. Both are valuable. So when it comes to mobile, it’s not Web vs. Native. It’s both. The graphs are impressive.
Python Viz, Linux Scavenger Hunt, Sandbox Environment, and Car Code
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Folium — makes it easy to visualize data that’s been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map.
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scavenger-hunt — A scavenger hunt to learn Linux commands.
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SEE — F-Secure’s open source Sandboxed Execution Environment (SEE) is a framework for building test automation in secured Environments.
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The Problem with Self-Driving Cars: Who Controls the Code? (Cory Doctorow) — Here’s a different way of thinking about this problem: if you wanted to design a car that intentionally murdered its driver under certain circumstances, how would you make sure that the driver never altered its programming so that they could be assured that their property would never intentionally murder them?