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StructurePro

In-development app from URCV turns an ARKit-enabled iPhone into a 3D scanner:

StructurePro combines the rich sensor data available from Apple’s ARKit with the 3d reconstruction capabilities of the industry leading mobile phone 3d reconstruction pipeline from URC Ventures.  StucturePro enables software companies to build applications that can be used by construction workers, building inspectors, or insurance claims adjusters to successfully model buildings from iPhone imagery.

… By integrating the advanced sensor data from ARKit, the URC Ventures image processing pipeline is now able to successfully handle the extreme rotations introduced by average end users, textureless surfaces such as large solid color walls, and repetitive structures such as ceiling tiles.

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HV. Self-replicating artificial intelligence program named Dorothy.

Galerians: Ash (2002) PS2

The artificial intelligence boom is here. Here’s how it could change the world around us.

A future with highways full of self-driving cars or robot friends that can actually hold a decent conversation may not be far away.

That’s because we’re living in the middle of an “artificial intelligence boom” — a time when machines are becoming more and more like the human brain.

That’s partly because of an emerging subcategory of AI called “deep learning.” It’s a process that’s often trying to mimic the human brain’s neocortex, which helps humans with language processing, sensory perception and other functions

From allowing us to be understand the Earth’s trees to teaching robots how to understand human life, deep learning is changing our world. Read more (5/26/17)

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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.

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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

Researchers make a breakthrough toward the next generation of memory devices

A*STAR and NTU researchers have created a thin film material that allows them to control the size and density of magnetic skyrmions. In addition, they have also achieved electrical detection of these skyrmions. The fabrication process for these films is compatible with current industrial methods. This discovery is a breakthrough and is a key step towards the creation of a skyrmion-based memory device, which is one of the promising contenders for the next generation of memory technologies.

The discovery has been recently published in Nature Materials.

Skyrmions are small particle-like magnetic structures about 400 times smaller than a red blood cell. They can be created in magnetic materials, and their stability at small sizes makes them ideal candidates for memory devices. Since the discovery of room temperature skyrmions in 2015, there has been a global race to create a skyrmion memory device because such a device could potentially hold more information, while using less power.

The need for more memory

Increasingly large amounts of data are created daily in our rapidly digitalised world. Moreover, cutting-edge technologies such as the Internet of Things (IOT), edge computing, and Artificial Intelligence (AI) require immediate processing of this data for effective performance. This requires the development of memory devices with increasingly higher capacities.

Read more.

5RNP (5 Robots Named Paul) is a group of robots that will draw people!

Really, you sit down in front of them, pose, and they’ll try to copy your face on their paper!

The best part is; each robot acts differently! And i swear, I’ve seen it with my own eyes. Some of them pay more attention to details, some of them are more likely to slack off, some of them use smaller lines, etc… it’s almost like they have their own personality! It was really fun to watch them.

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Plus check out Glitch Black’s music on Bandcamp!

I…I think I’m in love?