Here’s a little video clip from the afternoon that turned really windy. View was very obstructed due to all the smoke from wildfires. Air quality was actually listed as hazardous – so I didn’t get out much this day.
What a great series!
The National Museums Liverpool has some great videos. One set of them involved getting dressed in 18th century garb. We’ve all seen period movies. Now you can see how actors, and the original nobelmen/women of that era dressed.
Here’s what men wore and how they dressed:
Now, if you thought getting dressed as a 18th century gentleman was complex. Try being a 18th century lady. Bonus points for explaining the old nursery rhyme ‘Lucy Locket lost her pocket’, but others suggest it had a more tawdry meaning.
Time for me to shake my fist and tell you darn kids to get off my lawn. Lets set the wayback machine to the 1980’s…
Ralph Koster shares the first video game he ever wrote as well as a great flashback to what almost everyone that wanted to learn to program did back in the 80’s and 90’s. We typed in long programs by hand from books we got at the library and computer magazines. We taught ourselves BASIC and smatterings of assembly. If you were really cool, you even tried to sell your games: which was done by copying them to a floppy, printing a dot-matrix label for it, and trying to sell it in a ziplock baggie.
I started by fiddling around with the programs I typed in to see if I could change them or make them do different things.
My very first video ‘game’ on my TSR-80 consisted of a bunch of black and white dots that would fall down from the top of the screen, and you moved your dot ‘ship’ back and forth to avoid them as the enemies rained down. They came down one at a time. Ridiculously slowly. But it was probably my very first ‘game’.
My second, more ‘real’ game was a castle adventure game. You were the sole heir of a long-lost uncle and had to search his castle to find the deed within 24 hours. It was a text adventure at its heart, but there was opening graphics. I even wrote my own graphics editor with which I drew those opening screens. I believe I still have the graph paper I used.
Anyway, for anyone who learned to program in the 80’s, Koster’s video will tug some familiar heartstrings. For you younger kids, this is how it was done back in the day…
Signed distance field rendering is a technique used in Team Fortress 2, and documented by Chris Green of Valve in the SIGGRAPH 2007 paper Improved Alpha-Tested Magnification for Vector Textures and Special Effects. It allows you to render bitmap fonts without jagged edges even at high magnifications. This article describes how to implement the technique in libgdx.
Ever see that strange garbaly text on posts? Want to create it yourself? It’s generated using Zalgo (http://eeemo.net/).
Here’s an example of what it can generate:
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The Haze is a new immersive digital art installation from Japanese collective teamLab (previously) which situates guests at the center of a light-based vortex. The work uses light, fog, and sound to wrap guests in a mesmerizing cacophony of swirling spotlights which are reflected by a mirrored floor.
More astounding technology. Take any source dancer, a clip of the target person, and then make anyone do the same dance.
This will probably be used very soon to make whole hosts of dancers in movies/music videos all in perfect sync while only paying for one source dancer.
It could be used to bring back deceased dancers, or apply the dances of deceased dancers onto new artists.
Full paper and details here:
After having 2 trained bomb defusal experts play ‘Keep Talking and Nobody Explodes”,
BuzzFeed gets a real surgeon to attempt the infamously humorous, over-the-top ‘Surgeon Simulator’
In 2016, Google announced they had developed their own chip to handle machine learning workloads. They now use these custom-made chips in all their major datacenters for their most important daily functions.
Even in 2006, Google engineers had identified the need for a machine learning based hardware, but this need became acute in 2013 when the explosion of data to be mined meant they might need to double the size of their data centers. In just under 2 years, they hired, designed, and built the first versions of their TPU’s. A time that was described as ‘hectic’ by one of the chief engineers.
The units were marvels of simplicity based on observing a neural network workload. Machine learning networks consist of the following repeating steps:
The chip was then designed to handle the linear algebra steps of this workload after they analyzed the key neural net configurations used in their operations. They found they needed to handle operations with anywhere from 5 to 100 million weights at a time – far more than the dozens or even hundreds of multiply units in a CPU or GPU could handle.
They then created a pipelined chip with a massive bank of 65,536 8-bit integer multipliers (compared with just a few thousand multipliers in most GPU’s and dozens in a CPU), a unified 24MB cache of SRAM that worked as registers, followed by activation units hardwired for neural net tasks.
To reduce complexity of design, they realized their algorithms worked fine using quantization, so they could utilize 8-bit integer multiplication units instead of full floating point ones.
In looking further at the workload, they utilize a ‘systolic’ system in which the results of one step flow into the next without having to be written out to memory – pumping much like the chambers of a heart where one step feeds into the next:
The results: astounding. They are able to make 225,000 predictions in the same time it takes a GPU to make 13,000, or a CPU to make only 5500.
Their simplistic design and systolic system that minimizes writes to memory greatly reduces power usage – something important when you have thousands of these units in every data center:
They have since built 2 more versions for the TPU, one in 2017 and another in 2018. The second version improved on the first after they realized the first version was bandwidth limited – so they added 16GB of high bandwidth memory to achieve 45TFLOPS. They then expanded the reach by allowing each chip to be combined into a single 4 chip module. 64 modules are combined into a single pod (making 256 individual chips).
I highly suggest giving the links a further read. It’s not only fascinating from an AI perspective, but a wonderful story of looking at a problem with outside eyes and engineering the best solution given today’s technology.