- Material Design in the Google I/O App (Medium) — steps through design thinking as they put Google’s new design metaphor in place. I’ve been chewing on material design. It brings an internal consistency and logic to the Android world that Apple’s iOS and OS X visual worlds have been losing over the years. How long until web users expect this consistency too?
- Stewart and Slack (Wired) — profile of Foo Stewart Butterfield and his shiny Slack startup.
- Neural Networks and Deep Learning — a free online book to teach you … well, neural networks and deep learning.
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…