- Why Should I Trust You?: Explaining the Predictions of Any Classifier (PDF) — LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. Torkington’s Second Law: there’s no problem with machine learning that more machine learning can’t fix.
- How Etsy Formats Currency — I’m saving this one because it chafes every time I do it, and I do it wrong every time.
- Magic Leap in Wired — massive story by Kevin Kelly on the glories of Magic Leap, which The Verge noted still left a lot of open questions, such as “what the hell IS Magic Leap’s technology” and “why does everyone who works for Magic Leap sound like they’re on acid when they talk about the technology?” Everyone who wants their pixel-free glorious VR to be true is crossing fingers hoping it’s not another Theranos. The bit that stuck from the Wired piece was People remember VR experiences not as a memory of something they saw but as something that happened to them.
- Curing Our Slack Addiction — an interesting counterpoint to the “in the future everyone will be on 15,000 Slacks” Slack-maximalist view. For AgileBits, it distracted, facilitated, and rewarded distracting behaviour, ultimately becoming a drain rather than an accelerant.
The O’Reilly Hardware Podcast: Virtual reality, robotics, and today’s hardware landscape.
In this new episode of the Hardware Podcast, David Cranor and I talk with Rob Coneybeer, managing director and co-founder of Shasta Ventures, one of the critical first investors in hardware startups including Nest, Fetch Robotics, and Turo (formerly RelayRides).
- Why Nest looked like an appealing investment back in 2010
- Coneybeer’s focus on virtual reality and robotics as the next big things for hardware startups.
- Why it’s essential for hardware startups to have a long-term plan for improving products after they’re in place, and the importance of over-the-air software updates.
- The consumer psychology of selling a compelling hardware product, and when to aim for high price and high value. “People are willing to spend money when there’s something that’s really revolutionary,” says Coneybeer.
- The current state of venture capital investments in hardware startups. While raising later rounds is becoming more difficult, Coneybeer says: “the most interesting, innovative hardware companies will always find capital.”
An overview of the 3D animation process using Blender.
Creating 3D animations is like writing software. Both processes require
knowing certain industry terms. Some animation terms are:
- Setting up the scene with cameras, lights, and other effects
Let’s define each of these, and then we’ll dig into some code with Blender’s API.
Modeling is the process of creating 3D models. One way is to represent the 3D model as points in 3D space. Each point, or vertex, has 3 coordinates: an X, an Y, and a Z coordinate, to define its location in 3D space. A pair of vertices can be connected by an edge, and edges bound polygons called faces. These faces define the surface of the model. Modeling is all about creating these sets of vertices, edges, and faces.
To create a model, we usually start with a primitive shape (like a sphere or a cube) and reshape it into what we’d like. Individual vertices, edges, and faces can be repositioned. New vertices, edges, and faces can be added to the basic model through simple operations. Two common ones are extrusion and subdivision.