Machine Gun Position On The German R-class Zeppelin ‘LZ 63’, 1916-17

Meet the updated version of SpotMini, the robot dog by Boston Dynamics. Anyone in need of a new pet? | #djiphantom4 #djiglobal #uav #yuneec #hexacopter #djiinspire1 #quadcopter #miniquad #ironman #robotics #robot #skynet #fpv #drones #aerialphotography #octocopter #robots #djiphantom #arduino #dronepilot #drone #tesla #elonmusk #rcplane #spacex #sparkfun #nasa #raspberrypi #mavicpro via @bostondynamics (at Boston, Massachusetts)

Ever wished you could have a few more arms to get stuff done? Researchers at the University of Tokyo have developed Metalimbs. They’re strap-on robotic arms controlled by the lower body.

follow @the-future-now

SP. 114 - Ghost in the Shell (2017)

Repairing the robotic hand.

Bitsquare, decentralised #bitcoin exchange

Automatic Sample Layout

Coding experiment from Kyle McDonald arranging samples for music production in a unique way using machine learning:

I’ve been thinking about new ways of making music and working with sound. I’m especially excited about machine learning augmenting our selection of sounds, analyzing and decomposing existing recordings, and making automatic suggestions for compositions.

This shows around 30k “drum samples” from a few different sample packs, organized in 2d (position) and 3d (color). All sounds are less than 4 seconds long, but I only analyze and play the first second while scrolling through. I used librosa to extract the constant-q transform of each sound with 84 bins and 11 time steps. I used t-SNE with perplexity 100 to layout the sounds from those 924 dimensional vectors.

Link

Strange backyard sale

VR Artist Anna Zhilyaeva shares her first creation made with Tiltbrush in 2018:

This is my first Tilt Brush painting of 2018. I tried to make her look good from every angle while keeping the painting style.

You can view Anna’s work on Google Poly here

Link

math & music

When I was a freshman, studying music, I built my first computer program… and I didn’t even know I was coding:

At the time, I was learning to analyze chords by identifying the individual notes, reordering them into “thirds”, and comparing this stack to the actual arrangement to determine the inversion. I didn’t know anything about programming at the time, but my roommate was an engineer who showed me Wolfram Alpha’s Mathematica, a coding environment useful to a number of fields.

Well, I was just as “screw the rules” then, so I learned just enough to build a sort of decision tree to do my chord analysis homework for me. Above, nested If[] statements determine the interval by calculating the distance between pitches (in half-steps). Below, a similar set-up figures out the inversion of a chord.

There are a bunch of similarities to the JavaScript world I generally live in these days. It looks like Mathematica uses [] brackets instead of () parentheses and {} squiggly brackets, and presents its arguments more like an Excel function, but all the math-y bits certainly work the same… except… I wish Javascript let you string inequalities together like that!

One interesting peculiarity here - I have multiple functions with the same name. Whereas JavaScript functions don’t much care how many inputs you actually feed them, it seems I have different versions of the same keychordtype[] function for different numbers of inputs (defined here with a trailing _ underscore).

And instead of the console.log() message or the alert() pop-ups, outputs are made visible with the MessageDialogue[] function. So even though I don’t have any comments, and my nesting, naming, and order are a bit sloppy (look at those closing brackets! ridiculous!), I can still understand what’s going on - 10 years and several languages later.

tl;dr: music theory is math; different languages have different syntax, but logic is logic; Mathematica has a 2-week trial I’m eating though to take these screenshots

project: chord analysis homework helper

YouTube Artifacts

Latest AR exhibition from MoMAR (who ran a guerilla show earlier this year) returns to the Pollock Room at MoMA New York featuring works by David Kraftsow, responsible for the YouTube Artififacts bot that regularly generates animated images from distorted videos:

Welcome to The Age of the Algorithm. A world in which automated processes are no longer simply tools at our disposal, but the single greatest omnipresent force currently shaping our world. For the most part, they remain unseen. Going about their business, mimicking human behavior and making decisions based on statistical analysis of what they ‘think’ is right. If the role of art in society is to incite reflection and ask questions about the state of our world, can algorithms be a part of determining and defining people’s artistic and cultural values? MoMAR presents a series of eight pieces created by David Kraftsow’s YouTube Artifact Bot.

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Artificial Intelligence learns to beat Mario like crazy.

Shape Representation by Zippables

Computational Fabrication research from the Interactive Geometry Lab can turn 3D model files into objects with textiles, connecting parts and forming shape using zip fasteners:

Fabrication from developable parts is the basis for arts such as papercraft and needlework, as well as modern architecture and CAD in general, and it has inspired much research. We observe that the assembly of complex 3D shapes created by existing methods often requires first fabricating many small parts and then carefully following instructions to assemble them together. Despite its significance, this error prone and tedious process is generally neglected in the discussion. We present the concept of zippables – single, two dimensional, branching, ribbon-like pieces of fabric that can be quickly zipped up without any instructions to form 3D objects. Our inspiration comes from the so-called zipit bags (just-zipit.com), which are made of a single, long ribbon with a zipper around its boundary. In order to assemble the bag, one simply needs to zip up the ribbon. Our method operates in the same fashion, but it can be used to approximate a wide variety of shapes. Given a 3D model, our algorithm produces plans for a single 2D shape that can be laser cut in few parts from fabric or paper. A zipper can then be attached along the boundary by sewing, or by gluing using a custom-built fastening rig. We show physical and virtual results that demonstrate the capabilities of our method and the ease with which shapes can be assembled. 

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