Augmented Reality – AR – is getting some buzz here and there throughout the last 20 years almost. With hardware becoming more powerful and optics+light hardware becoming cheaper and more efficient it’s still all but close to become widely used and available.
Many refer to some one-trick pony feature in location-based games like “Pokemon Go” to being “AR”. But actual useful cases of AR are there but not feasible with current hardware generations.
Nevertheless a team in california has taken our the scissors and keyboards and made HoloKit:
HoloKit features super sharp optics quality and a 76-degree diagonal field of view. Pairing with a smartphone, HoloKit can perform an inside out tracking function, which uses the changing perspective on the outside world to note changes in its own position. HoloKit merges the real and the virtual in a smart way. While you see through the real world, virtual objects are blended into it. Powered by the accurate gyro and camera on smart phones, HoloKit solidly places virtual objects onto your table or floor, as if they were physically there without physical makers. These virtual objects will stay in the same place even if you walk away, just like real physical objects.
HoloKit is different from screen-based AR experience like Tango. You can directly see through the headset and view the real world as is, and in the meantime the virtual objects are projected on top of the real world, as opposed to viewing both the real and the virtual through a smartphone camera.
Browsers can do many things. It’s probably your main window into the vast internet. Lots of things need visualization. And if you want to know how it’s done, maybe do one yourself, then…
So, you want to create amazing data visualizations on the web and you keep hearing about D3.js. But what is D3.js, and how can you learn it? Let’s start with the question: What is D3?
While it might seem like D3.js is an all-encompassing framework, it’s really just a collection of small modules. Here are all of the modules: each is visualized as a circle – larger circles are modules with larger file sizes.
Kind of Bloop is a chiptune tribute to Miles Davis’ Kind of Blue, a track-by-track 8-bit reinterpretation of the bestselling jazz album of all time. Launched as a Kickstarter project in April 2009, only two weeks after Kickstarter itself opened its doors, the album’s production was funded by 419 backers around the world. Kind of Bloop was released on August 17, 2009, on the 50th anniversary of Kind of Blue.
You might, or might not be aware of my passion for black clothing. I like the simplicity and absence of noise.
Anyway. You might not be aware of the wonderful world of black as-in paint.
Apparently the current record holder in blackness (measured in percent absorption of visible light) is a product called “Vanta Black”.
Vantablack is a material developed by Surrey NanoSystems in the United Kingdom and is one of the darkest substances known, absorbing up to 99.96% of visible light (at 663 nm if the light is perpendicular to the material). The name is a compound of the acronym VANTA (vertically aligned carbon nanotube arrays) and the color black.
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity.
Let me introduce you to a wonderful concept. We are using these movies as backdrop when on the stepper or spinning, essentially when doing sports or as a screensaver that plays whenever nothing else is playing on the screens around the house.
What is it you ask?
The thing I am talking about is: Walking Videos! Especially from people who walk through Tokyo / Japan. And there are lots of them!
Think of it as a relaxing walk around a neighborhood you might not know. Take in the sounds and sights and enjoy. That’s the idea of it.
If you want the immediate experience, try this:
Of course there are a couple of different such YouTube channels waiting for your subscription. The most prominent ones I know are:
In hearing distance of the place I am usually staying when in Tokyo is a train station. So if the wind is right and the window is open I hear all these train station chimes and sounds.
If you don’t know what it is, let Wikipedia educate you:
A train melody is a succession of musically expressive tones played when a train is arriving at or about to depart from a train station. As part of train passenger operations, a train melody includes a parade of single notes organized to follow each other rhythmically to form a lilting, singular musical thought.
In Japan, departing train melodies are arranged to invoke a relief feeling in a train passenger after sitting down and moving with the departing train. In contrast, arriving train melodies are configured to cause alertness, such as to help travelers shake off sleepiness experienced by morning commuters.
Tocotronic is one of the bands I listened to during my teens/twenties. That dates me, that dates the band.
It’s very german. You’ll find their music on most streaming platforms. I recommend starting with the albums “wir kommen um uns zu beschweren” and “es ist egal aber”.
Anyhow. Now one member of the band starts a podcast!
Jan Müller ist seit über 25 Jahren Mitglied der Band Tocotronic. Er ist mit dem Format des Interviews bestens vertraut und kann sich als Fragender gut in die Perspektive seiner Gäste hineinversetzen. Die persönliche Auswahl seiner Gesprächspartner*innen bildet die Grundlage für authentische Gespräche, die stets von Interesse und Respekt geprägt sind. Mit “Reflektor” startet Jan seinen ersten Podcast.
If you’ve ever been to Japan you must have noticed that everything and anything makes sounds and talks. Elevators, escalators, doors, train stations, gates, vending machines – you name it, it makes sounds and talks.
It’s so apparent but yet quite comforting that I enjoyed it. It became an additional channel of information without the need of point-and-call all the time. Highly effective for me.
Japan utilizes sounds to a degree that every detail seems to count. Take the station gates you rush through to get to your train platform. It’ll sound differently depending on how you pay, what status your ticket had and even how close your IC-cards balance is to being empty.
The franchise was started by Kow Yokoyama in the 1980’s. Yokoyama-san was a scratch-build modeller, artist and sculptor. Among his works he built machines of war that would fight in the 29th century, but took their visual cues from early 19th century weaponry and the early NASA space program. All his models were pieced together from numerous kits including armor, aircraft, cars and found objects (like ping pong balls).
With recent announcements around human brain and brain-machine interface research like Neuralink the topic is seemingly seeing some more investments now.
As this whole topic is special to my heart I am interested in all things brain simulations. Thus here’s my personal “logbook entry” on the re-appearance of this topic:
This leads to one of the arguments for whole-brain simulation: it’ll help us solve the “biological imitation game,” a Turing test-like assay that pits digitally reconstructed brains against real ones. Iterations of the test help select increasingly more accurate models for a given task, which eventually become the most promising ideas for how specific biological networks operate. And because these models are based on mathematical equations, they could become the heart of next-generation AI.
I am following the proceedings of ACM SIGGRAPH conferences for more than 20 years now and with the recent years development in computational capacity it seems that many more algorithms and ideas make it to an application near you.
Take this one contribution by Yuanming Hu for example – the Taichi open source computer graphics library:
Taichi is an open-source computer graphics library that aims to provide easy-to-use infrastructures for computer graphics R&D. It’s written in C++14 and wrapped friendly with Python.
Yuanming Hu has been working on the development of Taichi since his third undergrad year (2016), mainly in his spare time. He would like to thank Prof. Toshiya Hachisuka and Prof. Seiichi Koshizukafor making possible his internship at UTokyo, where the initial parts of Taichi were developed.
If you ever want to quickly explain what augmented reality could be to a person not knowing yet, you might want to use this (and other) use cases for a visual explanation:
I achieved this by separating the artwork and text into many individual layers, that I placed in receding layers of 3D depth, in a 3D program on the computer. And made sure everything outside the borders of the book is excluded, to give it the ‘portal’ effect.