Sketch-RNN Demos

Holo Messenger

Developer Abhishek Singh is creating an AR recorded video messaging app with iOS ARKit that is presented in classic Sci-Fi settings (and all the recording is done with a single normal camera):

Remember Princess Leia’s classic holographic message from Star Wars? Well I built this app using ARKit and some awesome tech from Aifi.io that allows you to record and send your own. If you want to know when it becomes available, head over here: http://bit.ly/holomsngr

Link

This house is being 3-D printed with human and robot construction. Mesh mould technology uses the precision of robot building capacities to eliminate waste.

follow @the-future-now

5 Demos Where Code Meets Music

Latest Nat & Friends showcases a selection of web based experiments exploring sound and music (plus a couple of Google assistant easter eggs):

Music is a fun way to explore technologies like coding, VR, and machine learning. Here are a few musical demos and experiments that you can play with – created by musicians, coders, and some friends at Google.

More Here

Google Translate writes weird poetry if you repeat random characters.

(Above, my own experiments. Inspired by https://twitter.com/smutclyde )

4DAR ARKit Unity

Developer  应高选 has been experimenting with 4DViews’ free 4D captures and shares the results - particularly striking is this one using the new Apple ARKit and Unity software:

4DAR with ARKit and Unity3D, real man and real scale. iPhone6s test.

Here is an example using the same assets at a smaller scale:

应高选’s YouTube channel can be found here

4DViews on PK (from last week) Here

[Update 10/07/17]

Here is a video from 4DAR demonstrating how to put together your ownin 5 minutes:

Almost realtime visual tutorial on using 4DViews volumetric capture sequence with Unity and Apple ARKit, for fast hologram display

Dark Orchid: Pinocchio

“The presence of the Orchid does not seem to recognize a difference between human beings and artificial intellegence, both are equally alive to it. A.I. were infested with a new sort of self awareness and a desperate wish to be human. Most were immobile boxes of metal and circuit, going mad from their own futility and brimming with intense spite and depression, something they never felt before. For others, those with access to mobile functions, they began the construction of their bodies. Most of these were quite crude, being A.I. who were never quite familiar with anatomy. Medical A.I. tended to have more sophisticated bodies, with the smarts to near accurately mimic the human body with synthetic muscle and access to medi-tech. A common technique borrowed from one "Pinocchio” A.I. to another is to hunt the feral pigs and use their skin as their own. This both avoids their still lingering safety protocols against harming humans, and satisfies their need to have skin. Some even trade skins and parts with fellow Pinocchios. The safety protocols also stop them from hurting the “Affected”, they still recognize them as human despite their mutations.   The Pinocchios are generally harmless towards humans, in fact often friendly. Their just….off putting to say the least.“

@qadmonster

Hey, he is running away….????? #love #instagood #photooftheday #beautiful #fashion #happy #tbt #cute #followme #like4like #selfie #summer #fun #smile #style #amazing #sun #bestoftheday #pretty #cool #funny #ootd #potd #holiday #lifestyle #일상 #sweet #happiness #awesome #travel

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

THIS IS THE VIRTUAL REALITY I WAS PROMISED

Presenter Erika Ishii presents a wireless solution for Virtual Reality experiences, with a high powered laptop strapped to the back with an Htc Vive pro (though it isn’t clear how long the batteries will last):

THIS IS THE VIRTUAL REALITY I WAS PROMISED. @TeaganMorrison built us a wireless VR rig! @Alienware 15 laptop, @htcvive pro, army frame backpack. 

Source

Independent study on support vector machines