- $700 Ceramic-Spitting 3D Printer (Make Magazine) — ceramic printing is super interesting, not least because it doesn’t fill the world with plastic glitchy bobbleheads.
- Mathematics in the Age of the Turing Machine (Arxiv) — a survey of mathematical proofs that rely on computer calculations and formal proofs. (via Victoria Stodden)
- Failing at Microservices — deconstructed a failed stab at microservices. Category three engineers also presented a significant problem to our implementation. In many cases, these engineers implemented services incorrectly; in one example, an engineer had literally wrapped and hosted one microservice within another because he didn’t understand how the services were supposed to communicate if they were in separate processes (or on separate machines). These engineers also had a tough time understanding how services should be tested, deployed, and monitored because they were so used to the traditional “throw the service over the fence”to an admin approach to deployment. This basically lead to huge amounts of churn and loss of productivity.
- Transient Attributes for High-Level Understanding and Editing of Outdoor Scenes — computer vision doing more amazing things: annotate scenes (e.g., sunsets, seasons), train, then be able to adjust images. Tweak how much sunset there is in your pic? Wow.
ENTRIES TAGGED "ai"
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…