THIS IS THE VIRTUAL REALITY I WAS PROMISED

The real value of #bitcoin and #blockchain technology.

ARKit AR RPG (Proof Of Concept)

Developers @lingoded and @JesseBarksdale have been sharing on Twitter demos of Japanese-Style RPGs using iOS ARKit and Unity:

Little info is currently known about the project, but it is clear @lingoded is part of an AR gaming project called GeneReal so this is possibly part of it.

Until now, only an elite handful of computer scientists were aware that the Great A.I. Uprising secretly occurred in the late 1970s, and that we have been living in some jive-ass virtual environment ever since. Any time you hear disco music, it’s really just a glitch in the operational matrix. Can you dig it?

The Intelligence Explosion: A Short Sci Fi Film about AI Ethics

Motion capture- you never know when I may need to do one of The Rock’s Baywatch stunts/ better safe than sorry.

How Bitcoin Works

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Adversarial Examples that Fool Detectors

Computer Vision research from Jiajun Lu, Hussein Sibai and Evan Fabry examines blocking neural network object detection using what appears to look like DeepDream-esque camouflage:

An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time. To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors. If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles. In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO. The existence of such examples is surprising, as attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - makes it quite likely that adversarial patterns are strongly disrupted. We show that our construction produces adversarial examples that generalize well across sequences digitally, even though large perturbations are needed. We also show that our construction yields physical objects that are adversarial. 

The paper can be found here

Augmented Reality’s A-ha Moment

ARKit proof-of-concept demo from Trixi Studios applies an Augmented Reality portal with a ‘Take On Me’ music video drawing filter effect through an iOS device camera:

Link

Imaginary Soundscape

Online project from Qosmo generates ambient sounds to Google Streetview panoramas through Deep Learning processes, interpreting the visuals for appropriate sounds:

“Imaginary Soundscape” is a web-based sound installation, in which viewers can freely walk around Google Street View and immerse themselves into imaginary soundscape generated with deep learning models.

… Once trained, the rest was straightforward. For a given image from Google Street View, we can find the best-matched sound file from a pre-collected sound dataset, such that the output of SoundNet with the sound input is the most similar to the output of the CNN model for the image. As the sound dataset, we collected 15000 sound files from internet published under Creative Commons license and filtered with another CNN model on spectrogram trained to distinguish environmental/ambient sound from other types of audio (music, speech, etc.). 

You can try it out for yourself here, and find more background information here