- Seeed RePhone — open source and modular phone kit.
- Cyrcle — prototype round phone, designed by women for women. It’s clearly had a bit more thought put into it than the usual “pink it and shrink it” approach … circular to fit in smaller and shaped pockets, and software features strict notification controls: the device would only alert you to messages or updates from an inner circle.
- TensorFlow for Poets (Pete Warden) — I want to show how anyone with a Mac laptop and the ability to use the Terminal can create their own image classifier using TensorFlow, without having to do any coding.
- Finding the Natural Motivation for Change (Pia Waugh) — you can force certain behaviour changes through punishment or reward, but if people aren’t naturally motivated to make the behaviour change themselves then the change will be both unsustainable and minimally implemented. Amen!
"machine learning" entries
Four short links: 1 March 2016
Phone Kit, Circular Phone, TensorFlow Intro, and Change Motivation
Four short links: 29 February 2016
Robots & Decisions, Brain Modem, Distributed Devops Clue, and Robots in Law
- Learning Models for Robot Decision Making (YouTube) — a talk at the CMU Robotics Institute.
- Brain Modem — a tiny sensor that travels through blood vessels, lodges in the brain and records neural activity. The “stentrode” (stent + electrode) is the size of a paperclip, and Melbourne researchers (funded by DARPA) have made the first successful animal trials.
- The Past and Future are Here, It’s Just Not Evenly Distributed (Usenix) — slides, audio and video.
- Robots in American Law — This article closely examines a half century of case law involving robots. […] The first set highlights the role of robots as the objects of American law. Among other issues, courts have had to decide whether robots represent something “animate” for purposes of import tariffs, whether robots can “perform” as that term is understood in the context of a state tax on performance halls, and whether a salvage team “possesses” a shipwreck it visits with an unmanned submarine. (via BoingBoing)
Four short links: 26 February 2016
High-Performing Teams, Location Recognition, Assessing Computational Thinking, and Values in Practice
- 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.
- Assessing the Development of Computational Thinking (Harvard) — we have relied primarily on three approaches: (1) artifact-based interviews, (2) design scenarios, and (3) learner documentation. (via EdSurge)
- 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.
Four short links: 25 February 2016
Security Advice, Common Deep Learning Interface, React Text Editing, and Sexy Docs
- Free Security Advice (grugq) — chap wearies of handing out security advice, so gathers it and shares for all.
- TensorFuse — Common interface for Theano, CGT, and TensorFlow.
- Draft.js — a framework for building rich text editors in React, powered by an immutable model and abstracting over cross-browser differences.
- 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.
Four short links: 24 February 2016
UX Metrics, Page Scraping, IoT Pain, and NLP + Deep Learning
- Critical Metric: Critical Responses (Steve Souders) — new UX-focused metrics […] Start Render and Speed Index.
- Automatically Scrape and Import a Table in Google Spreadsheets (Zach Klein) — =ImportHtml("URL", "table", num) where “table” is the element name (“table” or a list tag), and num is the number of the element in case there are multiple on the page. Bam!
- Getting Visibility on the iBeacon Problem (Brooklyn Museum) — the Internet of Things is great, but I wouldn’t want to have to update its firmware. As we started to troubleshoot beacon issues, we wanted a clean slate. This meant updating the firmware on all the beacons, checking the battery life, and turning off the advanced power settings that Estimote provides. This was a painstakingly manual process where I’d have to go and update each unit one-by-one. In some cases, I’d use Estimote’s cloud tool to pre-select certain actions, but I’d still have to walk to each unit to execute the changes and use of the tool hardly made things faster. Perhaps when every inch of the world is filled with sensors, Google Street View cars will also beam out firmware updates.
- NLP Meets Deep Learning — easy to follow slide deck talking about how deep learning is tackling NLP problems.
Four short links: 17 February 2016
0G Gecko Grippers, Self-Parking Chairs, Willow Garage, and Death by Optimistic Algorithm Assessment
- Grasping with Gecko Grippers in Zero Gravity (YouTube) — biomimetic materials science breakthrough from Stanford’s Biomimetics and Dexterous Manipulation Lab proves useful in space. (via IEEE Spectrum)
- Nissan’s Self-Parking Office Chairs — clever hack, but thought-provoking: will we have an auto-navigating office chair before the self-driving auto revolution arrives? Because, you know, my day isn’t sedentary enough as it is …
- The Man Behind the Robot Revolution — profile of the man behind Willow Garage. Why he and it are interesting: Although the now defunct research-lab-startup hybrid might not ring any bells to you now, it was one of the most influential forces in modern robotics. The freewheeling robot collective jump-started the current race to apply robotics components like computer vision, manipulation, and autonomy into applications for everything from drones and autonomous cars to warehouse operations at places like Google, Amazon, and car companies like BMW. Google alone acquired three of the robot companies spawned by Willow.
- NSA’s Lousy Evaluation of Drone Strike Algorithm Effectiveness (Ars Technica) — vastly overstating the quality of the predictions. The 0.008% false positive rate would be remarkably low for traditional business applications. This kind of rate is acceptable where the consequences are displaying an ad to the wrong person, or charging someone a premium price by accident. However, even 0.008% of the Pakistani population still corresponds to 15,000 people potentially being misclassified as “terrorists” and targeted by the military—not to mention innocent bystanders or first responders who happen to get in the way. Security guru Bruce Schneier agreed. “Government uses of big data are inherently different from corporate uses,” he told Ars. “The accuracy requirements mean that the same technology doesn’t work. If Google makes a mistake, people see an ad for a car they don’t want to buy. If the government makes a mistake, they kill innocents.” (via Cory Doctorow)
Four short links: 16 February 2016
Full-on Maker, Robot Recap, Decoding Mandarin, and Sequencing Birds
- Washers and Screws (YouTube) — this chap is making his own clock from scratch, and here he is making his own washers and screws. Sometimes another person’s obsession can be calming. (via Greg Sadetsky)
- ROScon 2015 Recap with Videos (Robohub) — Shuttleworth suggests that robotics developers really need two things at this point: a robust Internet of Things infrastructure, followed by the addition of dynamic mobility that robots represent. However, software is a much more realistic business proposition for a robotics startup, especially if you leverage open source to create a developer community around your product and let others innovate through what you’ve built.
- Getting Deep Speech to Work in Mandarin (Baidu SVAIL) — TIL that some of the preprocessing traditionally used in speech-to-text systems throws away pitch information necessary to decode tonal languages like Mandarin. Deep Speech doesn’t use specialized features like MFCCs. We train directly from the spectrogram of the input audio signal. The spectrogram is a fairly general representation of an audio signal. The neural network is able to learn directly which information is relevant from the input, so we didn’t need to change anything about the features to move from English speech recognition to Mandarin speech recognition. Their model works better than humans at decoding short text such as queries.
- Sequencing Genomes of All Known Kakapo — TIL there’s a project to sequence genomes of 10,000 bird species and that there’s this crowdfunded science project to sequence the kakapo genome. There are only 125 left, and conservationists expect to use the sequenced genomes to ensure rare genes are preserved. Every genome in this species could be sequenced … I’m boggling. (via Duke)
Risto Miikkulainen on evolutionary computation and making robots think for themselves
The O'Reilly Radar Podcast: Evolutionary computation, its applications in deep learning, and how it's inspired by biology.
Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come: Stitcher, TuneIn, iTunes, SoundCloud, RSS
In this week’s episode, David Beyer, principal at Amplify Partners, co-founder of Chart.io, and part of the founding team at Patients Know Best, chats with Risto Miikkulainen, professor of computer science and neuroscience at the University of Texas at Austin. They chat about evolutionary computation, its applications in deep learning, and how it’s inspired by biology.
Finding optimal solutions
We talk about evolutionary computation as a way of solving problems, discovering solutions that are optimal or as good as possible. In these complex domains like, maybe, simulated multi-legged robots that are walking in challenging conditions—a slippery slope or a field with obstacles—there are probably many different solutions that will work. If you run the evolution multiple times, you probably will discover some different solutions. There are many paths of constructing that same solution. You have a population and you have some solution components discovered here and there, so there are many different ways for evolution to run and discover roughly the same kind of a walk, where you may be using three legs to move forward and one to push you up the slope if it’s a slippery slope.
You do (relatively) reliably discover the same solutions, but also, if you run it multiple times, you will discover others. This is also a new direction or recent direction in evolutionary computation—that the standard formulation is that you are running a single run of evolution and you try to, in the end, get the optimum. Everything in the population supports finding that optimum.
Four short links: 11 February 2016
Surviving Crashes, Thumbs-Up Thumbs-Down Learning, Faster Homomorphic Encryption, and Nerdy V-Day Cards
- 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.
- 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.
- Microsoft Speeds Up Homomorphic Encryption (The Register) — homomorphic encryption lets databases crunch data without needing keys to decode it.
- Nerdy Valentine Cards (Evil Mad Scientist) — for a nerd in your life. (via Cory Doctorow)
Four short links: 5 February 2016
Signed Filesystem, Smart Mirror, Deep Learning Tuts, and CLI: Miami
- Introducing the Keybase Filesystem — love that crypto is making its way into the filesystem.
- DIY Smart Bathroom Mirror — finally, someone is building this science-fiction future! (via BoingBoing)
- tensorflow tutorials — for budding deep learners.
- clmystery — a command-line murder mystery.