This Is Actually The 2nd Machine Learning Program!

Hype Cycle : Machine Learning

Project from Universal Everything is a series of films exploring human-machine collaboration, here presenting performative dance with human and abstracted forms:

Hype Cycle ­is a series of futurist films exploring human-machine collaboration through performance and emerging technologies.

Machine Learning is the second set of films in the Hype Cycle series. It builds on the studio’s past experiments with motion studies, and asks: when will machines achieve human agility?

Set in a spacious, well-worn dance studio, a dancer teaches a series of robots how to move. As the robots’ abilities develop from shaky mimicry to composed mastery, a physical dialogue emerges between man and machine – mimicking, balancing, challenging, competing, outmanoeuvring.

Can the robot keep up with the dancer? At what point does the robot outperform the dancer? Would a robot ever perform just for pleasure? Does giving a machine a name give it a soul?

These human-machine interactions from Universal Everything are inspired by the Hype Cycle trend graphs produced by Gartner Research, a valiant attempt to predict future expectations and disillusionments as new technologies come to market.

More Here

A robot communicates through an interface.

Saturn 3 (1980)

Automatic Machine Knitting of 3D Meshes

Research from Carnegie Mellon Textiles Lab have put forward a framework to turn 3D model file into a physical knitted object:

We present the first computational approach that can transform 3D meshes, created by traditional modeling programs, directly into instructions for a computer-controlled knitting machine. Knitting machines are able to robustly and repeatably form knitted 3D surfaces from yarn, but have many constraints on what they can fabricate. Given user-defined starting and ending points on an input mesh, our system incrementally builds a helix-free, quad-dominant mesh with uniform edge lengths, runs a tracing procedure over this mesh to generate a knitting path, and schedules the knitting instructions for this path in a way that is compatible with machine constraints. We demonstrate our approach on a wide range of 3D meshes.

More Here

Google’s DeepMind AI just taught itself to walk

This skeleton robot salamander just wiggled its way into my heart.

Tele-Present Water by David Bowen

I rarely use the phrase ‘mind blown’, but this is one of those rare occurrences.

An art installation which combines real-time data, mechanical puppetry, and a physical grid representation usually employed virtually with computers:

This installation draws information from the intensity and movement of the water in a remote location. Wave data is being collected in real-time from National Oceanic and Atmospheric Administration data buoy station 46246, 49.985 N 145.089 W (49°59'7" N 145°5'20" W) on the Pacific Ocean. The wave intensity and frequency is scaled and transferred to the mechanical grid structure resulting in a simulation of the physical effects caused by the movement of water from halfway around the world.

Link to the artist’s website for this work can be found here

Current AI programming techniques are incompatible for creation of human intelligence

        When artificial intelligence first came about in the first computing machines, computing was restricted by technology and computing power. The easiest way to get around giving intelligent functions to a machine was to give it basic sets of rules. These finite set of rules took up small amounts of memory, and could be used dependent on the input and goal. The rules could be combined to create more complex functions, exponentially increasing the amount of total functions available.

       The current computing grew from these beginnings, now using complex algorithmic and recursive functions using basic rules to further increase the amount of computing possibilities. The search for true artificial intelligence, one comparable to our amount of intelligence and conscious awareness, is in the works. A robotic creation using transistors and circuits and algorithmic programming whilst having the conscious and cognitive abilities that humans possess is the end goal. We know so far that even the most advanced artificial intelligence makes semantic and perceptual errors about the physical world.

       The problem with creating an artificial intelligence like our own is the rule-based computing which is the seed of modern computing intelligence. The seed planted was a rule-based one, and since we used these seeds to grow modern computing, we now have this type of computing available. I strongly believe this rule-based computing will never allow for true human-based artificial intelligence to be used. Human cognition and consciousness is not a rule-based system, and rule-based systems are not able to perform the amount and type of processing that the human mind does.

       The human mind processes information in bottom-up and top-down processes by integrating sensory info and semantic knowledge in integration centers of the brain. The mind can take this info and again reanalyze in in a seemingly subjective fashion, or by applying further conscious reason to perform a reaction to the input info. The mind has the ability to consciously engage ideas in the brain in a way that doesn’t seem to obey rules.

       Humans can be argued to be mostly a tabula rasa (blank slate) at birth, with arguably some innate abilities; perhaps there are some undefined “rules”. To create a “fully grown” and “mature” robot instantaneously, as well as endow it with all the knowledge of the world and processing an adult human would possess is a disastrous thought. We can’t program a mature robot, we need to grow it. Create a robot with the ability to learn, and to perform connections by repeated pairings of stimuli. A robot would be endowed with the learning abilities of which humans possess, so that it may learn connections in the world and be endowed with human-type knowledge and ability. The way we “program” robots now with artificially intelligent algorithms does not begin to scratch the surface of human knowledge ability.

       A robotic creation as a “newborn” with very few programmed rules besides rules for stimuli pairing, feature detection, whilst integrating the perceptual info similar in fashion to how the info bonds and integrates in the human brain is essential. No need for large highly complex algorithmic programs, we set a few basic algorithms, and allow the robot to “learn” the world on its own. While this is a long process, I believe it is the closest approximation to a human-like artificial intelligence. We bare the robot, and allow it to grow and mature in the human world by interaction with the world and gaining knowledge in the fashion that we do. This is the only way to create a robot which can be perceptually and semantically comparable to a human.

This post was inspired by this video on cognitive science: https://youtu.be/0T_nOzpBYxU

The Genius of Marie Curie

Growing up in Warsaw in Russian-occupied Poland, the young Marie Curie, originally named Maria Sklodowska, was a brilliant student, but she faced some challenging barriers. As a woman, she was barred from pursuing higher education, so in an act of defiance, Marie enrolled in the Floating University, a secret institution that provided clandestine education to Polish youth. By saving money and working as a governess and tutor, she eventually was able to move to Paris to study at the reputed Sorbonne. here, Marie earned both a physics and mathematics degree surviving largely on bread and tea, and sometimes fainting from near starvation. 

In 1896, Henri Becquerel discovered that uranium spontaneously emitted a mysterious X-ray-like radiation that could interact with photographic film. Curie soon found that the element thorium emitted similar radiation. Most importantly, the strength of the radiation depended solely on the element’s quantity, and was not affected by physical or chemical changes. This led her to conclude that radiation was coming from something fundamental within the atoms of each element. The idea was radical and helped to disprove the long-standing model of atoms as indivisible objects. Next, by focusing on a super radioactive ore called pitchblende, the Curies realized that uranium alone couldn’t be creating all the radiation. So, were there other radioactive elements that might be responsible?

In 1898, they reported two new elements, polonium, named for Marie’s native Poland, and radium, the Latin word for ray. They also coined the term radioactivity along the way. By 1902, the Curies had extracted a tenth of a gram of pure radium chloride salt from several tons of pitchblende, an incredible feat at the time. Later that year, Pierre Curie and Henri Becquerel were nominated for the Nobel Prize in physics, but Marie was overlooked. Pierre took a stand in support of his wife’s well-earned recognition. And so both of the Curies and Becquerel shared the 1903 Nobel Prize, making Marie Curie the first female Nobel Laureate.

In 1911, she won yet another Nobel, this time in chemistry for her earlier discovery of radium and polonium, and her extraction and analysis of pure radium and its compounds. This made her the first, and to this date, only person to win Nobel Prizes in two different sciences. Professor Curie put her discoveries to work, changing the landscape of medical research and treatments. She opened mobile radiology units during World War I, and investigated radiation’s effects on tumors.

However, these benefits to humanity may have come at a high personal cost. Curie died in 1934 of a bone marrow disease, which many today think was caused by her radiation exposure. Marie Curie’s revolutionary research laid the groundwork for our understanding of physics and chemistry, blazing trails in oncology, technology, medicine, and nuclear physics, to name a few. For good or ill, her discoveries in radiation launched a new era, unearthing some of science’s greatest secrets.

From the TED-Ed Lesson The genius of Marie Curie - Shohini Ghose

Animation by Anna Nowakowska

Dildo Generator

Online 3D experiment by Ikaros Kappler which is described as a “Extrusion/Revolution Generator” ….

Created with three.js, you can alter the bezier curves and angle of the form, and is designed with 3D printing in mind (models can be exported and saved, as well as calculated weight in silicone).

Try it out for yourself (if you wish) here

Did you know the guy from Elysium really played Chappie?