- Swift on GitHub — watch a thousand projects launch.
- HTTP API Design Guide — extracted from work on the Heroku Platform API.
- End-to-End PGP in Gmail — Google releases an open source Chrome extension to enable end-to-end OpenPGP on top of gmail. This is a good thing. As noted FSF developer Ben Franklin wrote: Those who would give up awkward key signing parties to purchase temporary convenience deserve neither.
- Close Your Comments; Build Your Community (Annemarie Dooling) — I am rarely sad when a commenting platform collapses, because it usually means the community dissolved long before.
Some of AI's viable approaches lie outside the organizational boundaries of Google and other large Internet companies.
Editor’s note: this post is part of an ongoing series exploring developments in artificial intelligence.
Here’s a simple recipe for solving crazy-hard problems with machine intelligence. First, collect huge amounts of training data — probably more than anyone thought sensible or even possible a decade ago. Second, massage and preprocess that data so the key relationships it contains are easily accessible (the jargon here is “feature engineering”). Finally, feed the result into ludicrously high-performance, parallelized implementations of pretty standard machine-learning methods like logistic regression, deep neural networks, and k-means clustering (don’t worry if those names don’t mean anything to you — the point is that they’re widely available in high-quality open source packages).
Google pioneered this formula, applying it to ad placement, machine translation, spam filtering, YouTube recommendations, and even the self-driving car — creating billions of dollars of value in the process. The surprising thing is that Google isn’t made of magic. Instead, mirroring Bruce Scheneier’s surprised conclusion about the NSA in the wake of the Snowden revelations, “its tools are no different from what we have in our world; it’s just better funded.” Read more…
The Tango smartphone will help SPHERES navigate space station modules.
I work in the Intelligent Robotics Group (IRG) at NASA Ames Research Center, and when we got the chance to collaborate with our next-door neighbor Google on their new Project Tango, we knew exactly what to do: we’re sending the Project Tango smartphone to the International Space Station, where it will set our robots free.
Smart SPHERES with a space-ready Project Tango phone. Photo courtesy of NASA.
Machine Learning Mistakes, Recommendation Bandits, Droplet Robots, and Plain English
- Machine Learning Done Wrong — [M]ost practitioners pick the modeling algorithm they are most familiar with rather than pick the one which best suits the data. In this post, I would like to share some common mistakes (the don’t-s).
- Bandits for Recommendations — A common problem for internet-based companies is: which piece of content should we display? Google has this problem (which ad to show), Facebook has this problem (which friend’s post to show), and RichRelevance has this problem (which product recommendation to show). Many of the promising solutions come from the study of the multi-armed bandit problem.
- Droplets — the Droplet is almost spherical, can self-right after being poured out of a bucket, and has the hardware capabilities to organize into complex shapes with its neighbors due to accurate range and bearing. Droplets are available open-source and use cheap vibration motors and a 3D printed shell. (via Robohub)
- Apple’s App Store Approval Guidelines — some of the plainest English I’ve seen, especially the Introduction. I can only aspire to that clarity. If your App looks like it was cobbled together in a few days, or you’re trying to get your first practice App into the store to impress your friends, please brace yourself for rejection. We have lots of serious developers who don’t want their quality Apps to be surrounded by amateur hour.
The collision of software and hardware has broken down the barriers between the digital and physical worlds.
Note: this post is a slightly hydrated version of my Solid keynote. To get it out in 10 minutes, I had to remove a few ideas and streamline it a bit for oral delivery; this is the full version.
In 1995, Nicolas Negroponte told us to forget about the atoms and focus on the bits. I think “being digital” was probably an intentional overstatement, a provocation to shove our thinking off of its metastable emphasis on the physical, to open us up to the power of the purely digital. And maybe it worked too well, because a lot of us spent two decades plumbing every possibility of digital-only technologies and digital-only businesses.
By then, technology had bifurcated into two streams of hardware and software that rarely converged outside of the data center, and for most of us, unless we were with a firm the size of Sony, with a huge addressable market, hardware was simply outside the scope of our entrepreneurial ambitions. It was our platform, but rarely our product. The physical world was for other people to worry about. We had become by then the engineers of the ephemeral, the plastic, and the immaterial. And in the depth of our immersion into the virtual and digital, we became, it seems, citizens of Weblandia (and congregants of the Church of Disruption).
But pendulums always swing back. Read more…
Filesharing Box, Realised Dystopias, Spam Ecosystem Research, and Technical Interviews
- PirateBox 1.0 — turns a wireless router into a filesharing joy. v1.0 has a responsive ui, among other things for use on tablets and phones.
- Dystopia Tracker — keep on top of which scifi dystopic predictions have been realised. I’d like filters for incubators, investors, and BigCos so you can see who is investing in dystopia.
- The Harvester, the Botmaster, and the Spammer (PDF) — research paper on the spam supply chain.
- Technical Interviewing (Moishe Lettvin) — lessons learned from conducting >250 technical interviews at Google. Why do I care? Chances are, your technical interviews suck so you’re hiring poorly.