- Making Remote Work — The reality of a remote workplace is that the connections are largely artificial constructs. People can be very, very isolated. A person’s default behavior when they go into a funk is to avoid seeking out interactions, which is effectively the same as actively withdrawing in a remote work environment. It takes a tremendous effort to get on video chats, use our text based communication tools, or even call someone during a dark time. Very good to see this addressed in a post about remote work.
- Google Big Picture Group — public output from the visualization research group at Google.
- Using CMOS Sensors in a Cellphone for Gamma Detection and Classification (Arxiv) — another sense in your pocket. The CMOS camera found in many cellphones is sensitive to ionized electrons. Gamma rays penetrate into the phone and produce ionized electrons that are then detected by the camera. Thermal noise and other noise needs to be removed on the phone, which requires an algorithm that has relatively low memory and computational requirements. The continuous high-delta algorithm described fits those requirements. (via Medium)
- Affordable Arduino-Compatible Centimeter-Level GPS Accuracy (IndieGogo) — for less than $20. (via DIY Drones)
All predictions are for entertainment purposes only!
It is a generally accepted requirement that all technology pundits attempt a yearly prognostication of the coming 12 months. Having consulted my crystal ball, scryed the entrails of a falcon, and completed a 3 day fasting ritual in a sweat lodge set up inside a Best Buy, I will now tempt the Gods of Hubris and make my guesses for the year in mobile.
As robots integrate more and more into our lives, they'll simply become part of normal, everyday reality — like dishwashers.
(Note: this post first appeared on Forbes; this lightly edited version is re-posted here with permission.)
We’ve watched the rising interest in robotics for the past few years. It may have started with the birth of FIRST Robotics competitions, continued with the iRobot and the Roomba, and more recently with Google’s driverless cars. But in the last few weeks, there has been a big change. Suddenly, everybody’s talking about robots and robotics.
It might have been Jeff Bezos’ remark about using autonomous drones to deliver products by air. It’s a cool idea, though I think it’s farfetched, but that’s another story. Amazon Prime isn’t Amazon’s first venture into robotics: a year and a half ago, they bought Kiva Systems, which builds robots that Amazon uses in their massive warehouses. (Personally, I think package delivery by drone is unlikely for many, many reasons, but that’s another story, and certainly no reason for Amazon not to play with delivery in their labs.)
But what really lit the fire was Google’s acquisition of Boston Dynamics, a DARPA contractor that makes some of the most impressive mobile robots anywhere. It’s hard to watch their videos without falling in love with what their robots can do. Or becoming very scared. Or both. And, of course, Boston Dynamics isn’t a one-time buy. It’s the most recent in a series of eight robotics acquisitions, and I’d bet that it’s not the last in the series. Read more…
Plus ça change, plus c'est la même chose.
As the end of December approaches, it’s time to take a look at the year that was. In a lot of ways, 2013 was a status quo year for mobile, with nothing earthshaking to report, just a steady progression of what already is getting more, um, is-y?
We started the year with Apple on top in the tablet space, Android on top in the handset space, and that’s how we ended the year. Microsoft appears to have abandoned the handset space after a decade of attempts to take market-share, and made their move on the tablet space instead with the Surface. In spite of expensive choreographer board room commercials, the Surface didn’t make a huge dent in Apple’s iPad dominance. But Microsoft did better than Blackberry, whose frantic flailing in the market has come to represent nothing so much as a fish out of water.
Flexible Data, Google's Bottery, GPU Assist Deep Learning, and Open Sourcing
- Google’s Seven Robotics Companies (IEEE) — The seven companies are capable of creating technologies needed to build a mobile, dexterous robot. Mr. Rubin said he was pursuing additional acquisitions. Rundown of those seven companies.
- Hebel (Github) — GPU-Accelerated Deep Learning Library in Python.
- What We Learned Open Sourcing — my eye was caught by the way they offered APIs to closed source code, found and solved performance problems, then open sourced the fixed code.
3D Fossils, Changing Drone Uses, High Scalability, and Sim Redux
- CT Scanning and 3D Printing for Paleo (Scientific American) — using CT scanners to identify bones still in rock, then using 3D printers to recreate them. (via BoingBoing)
- Growing the Use of Drones in Agriculture (Forbes) — According to Sue Rosenstock, 3D Robotics spokesperson, a third of their customers consist of hobbyists, another third of enterprise users, and a third use their drones as consumer tools. “Over time, we expect that to change as we make more enterprise-focused products, such as mapping applications,” she explains. (via Chris Anderson)
- Serving 1M Load-Balanced Requests/Second (Google Cloud Platform blog) — 7m from empty project to serving 1M requests/second. I remember when 1 request/second was considered insanely busy. (via Forbes)
- Boil Up — behind the scenes for the design and coding of a real-time simulation for a museum’s science exhibit. (via Courtney Johnston)
Mobile Payment is going to take a lot of cooperation by a lot of competing interests, or a clever end-run
There was a time when the two big unsolved puzzles of online finance were micropayments and mobile payments. Micropayments were a problem because no one seemed willing to make sub-dollar transfers economically viable, while mobile payments had a chicken-and-egg solution / vendor paradox. Sites like PayPal and Square seem to have finally resolved the micropayment issue, as are more out-of-left-field ideas like Bitcoins. Mobile payment is still a morass of competing solutions, however.
For a while, Near Field seemed to be the sword that would slay the dragon, but Apple’s continual refusal to adopt the technology would leave a big segment of the mobile market out of the play. Even if someone comes up with a new point of sale (POS) terminal leveraging the more universal Bluetooth Low Energy, the real challenge isn’t the hardware. The problem is getting dozens of POS vendors and all the banks that issue cards to sign onto a new standard, and getting enough stores and retail venues to adopt it. Chicken and the egg once again.
Rich Text Editing, Structural Visualisation, DDoS Protection, Realtime DDoS Map
- Sir Trevor — nice rich-text editing. Interesting how Markdown has become the way to store formatted text without storing HTML (and thus exposing the CSRF-inducing HTML-escaping stuckfastrophe).
- Slate for Excel — visualising spreadsheet structure. I’d be surprised if it took MSFT or Goog 30 days to acquire them.
- Project Shield — Google project to protect against DDoSes.
- Digital Attack Map — DDoS attacks going on around the world. (via Jim Stogdill)
Google Code Analysis, Deep Learning, Front-End Workflow, and SICP in JS
- Steve Yegge on GROK (YouTube) — The Grok Project is an internal Google initiative to simplify the navigation and querying of very large program source repositories. We have designed and implemented a language-neutral, canonical representation for source code and compiler metadata. Our data production pipeline runs compiler clusters over all Google’s code and third-party code, extracting syntactic and semantic information. The data is then indexed and served to a wide variety of clients with specialized needs. The entire ecosystem is evolving into an extensible platform that permits languages, tools, clients and build systems to interoperate in well-defined, standardized protocols.
- Deep Learning for Semantic Analysis — When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effect of contrastive conjunctions as well as negation and its scope at various tree levels for both positive and negative phrases.
- Fireshell — workflow tools and framework for front-end developers.