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picoFlamingo Project
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Nvidia’s new Xavier SoC is an AI supercomputer for cars

Nvidia’s work in reinventing itself as a leader in automated car technology continues with the launch of a new system-on-a-chip, called Xavier and launched today at the GPU Technology Conference Europe. The Xavier SoC manages 20 trillion operations per second, while only using 20 watts of power. Xavier is designer to be used as an AI brain for use in self-driving cars specifically, and Nvidia CEO Jen-Hsun Huang told attendees at the conference that it’s “the greatest SoC endeavor” he’s ever encountered, which he notes is particularly meaningful given how long Nvidia has been in the business of making silicon.
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Working on AI
Nice article about the work at Deepmind on AI. In this case on the scenario where a neural net must be able to learn more then one thing. So to make it practical, a neural net that can both play Space Invaders and recognise music for example.
Google DeepMind is working on progressive neural networks to bring us closer to a multi-functional 'general' artificial intelligence (AI), but we are still some distance away
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This Old-Ass Commodore 64 Is Still Being Used to Run an Auto Shop in Poland
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Colliding Black Holes Tell New Story of Stars

...based on an article from Quanta Magazine.

Just months after their discovery, gravitational waves coming from the mergers of black holes are shaking up astrophysics.

Already, the new gravitational-wave data has shaken up the field of astrophysics. In response, three dozen experts spent two weeks in August sorting through the implications at the Kavli Institute for Theoretical Physics (KITP) in Santa Barbara.

Over the two weeks in Santa Barbara, a rivalry heated up between the new “chemically homogeneous” model for the formation of black-hole binaries, proposed by Selma de Mink (an assistant professor of astrophysics at the University of Amsterdam) and colleagues earlier this year, and the classic “common envelope” model espoused by many other experts.

GW150914, the first and mightiest event of black-hole mergers that LIGO has detected so far, presented a big puzzle: LIGO was expected to spot pairs of black holes weighing in the neighborhood of 10 times the mass of the sun, but these packed roughly 30 solar masses apiece.
“They are there — massive black holes, much more massive than we thought they were,” astrophysicists Selma de Mink said to the room. “So, how did they form?”

Some experts question the new model’s efficacy. Simulations suggest that the chemically homogeneous model struggles to explain smaller black-hole binaries like those in LIGO’s second signal. Worse, doubt has arisen as to how well the theory really accounts for GW150914, which is supposed to be its main success story. “It’s a very elegant model,” * Daniel Holz of the University of Chicago said*. “It’s very compelling. The problem is that it doesn’t seem to fully work.”

So, read the article from Quanta Magazine for getting all the details. There, you can find some links to recent studies>>
https://www.quantamagazine.org/20160906-black-hole-ligo-astronomy/

► The infographic below compares the classic theory with the chemically homogeneous model.

► Infographic credit: Lucy Reading-Ikkanda

#BlackHolesevolution, #Research , #Astrophysics , #LIGO , #chemicallyhomogeneousmodel , #BlackHolesformation
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Pyrite , Calcite | #Geology #GeologyPage #Mineral

Chemical Formula
Pyrite : FeS2
Calcite : CaCO3

Locality: Panasqueira Mine - Couto Mineiro da Panasqueira, Panasqueira, Covilhã, Castelo Branco District, Portugal

Dimensions: 4.5 cm x 3.5 cm x 1.5 cm

Photo Copyright © Fabre Minerals

Geology Page
www.geologypage.com
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Pseudobrookite | #Geology #GeologyPage #Mineral

Chemical Formula: Fe2TiO5

Locality: Emmelberg, Üdersdorf, Daun, Eifel, Rhineland-Palatinate, Germany

FOV: 1.42 mm

Photo Copyright © Chinellato Matteo

Geology Page
www.geologypage.com
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Cederblad 214, the Cosmic Question Mark

At top sits the emission nebula known as Cederblad 214, which is part of a larger star-forming complex called NGC 7822.

The dot at bottom is a smaller nebula called Sharpless 170.

The entire piece of punctuation spans about 40 light-years in the constellation Cepheus.

► Image in HST-palette from ionized elements, Red=Sulfur, Green=Hydrogen & Blue=Oxygen. North is Right
Credit: J.P. Metsavainio

► Image source>> http://news.nationalgeographic.com/news/2012/02/pictures/120217-best-space-pictures-183-sun-saturn-moons-science/#/space183-question-mark-nebula_48869_600x450.jpg

Further reading

https://en.wikipedia.org/wiki/NGC_7822

https://en.wikipedia.org/wiki/Cepheus_(constellation)

http://www.sharplesscatalog.com/sharpless/SH2-170.aspx


#Universe, #Cederblad214, #NGC7822, #Sharpless170, #space, #Astronomy, #Astrophotography
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Long time since I last played with one of my small boards. Let's start with something simple
#RaspberryPi #SpyCam #gstreamer 
It has been a long time since we played with our Rpi. In the meantime we have got a Rpi2, Rpi3 and even manage to get a Rpi Zero, together with a camera module. We have some plans for these little guys, specially within the frame of the MIA project. But first we have to get started with all toys ...
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15 Top Open Source Artificial Intelligence Tools

Artificial Intelligence (AI) is one of the hottest areas of technology research. Companies like IBM, Google, Microsoft, Facebook and Amazon are investing heavily in their own R&D, as well as buying up startups that have made progress in areas like machine learning, neural networks, natural language and image processing. Given the level of interest, it should come as no surprise that a recent artificial intelligence report from experts at Stanford University concluded that "increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030."

See the slideshow at http://www.datamation.com/open-source/slideshows/15-top-open-source-artificial-intelligence-tools.html
These open source AI applications are on the cutting edge of artificial intelligence research.
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Nvidia expands role in AI market, launches two new deep-learning Tesla chips

At the GPU Technology Conference currently underway in Beijing, Nvidia CEO Jen-Hsun Huang (pictured) unveiled the Tesla P4 and Tesla P40 GPUs that form part of the Pascal architecture-based deep learning platform. The company had officially entered the deep learning market in April with the announcement of the 15-billion transistor Tesla P100 chip, aimed at deep learning. “With the Tesla P100 and now Tesla P4 and P40, Nvidia offers the only end-to-end deep learning platform for the data center, unlocking the enormous power of AI for a broad range of industries,” said Ian Buck, general manager of accelerated computing at Nvidia. While the Tesla P100 focuses on training tasks, the Tesla P4 and P40 has been designed specifically for inferencing. In response to queries from devices and users, the GPU’s are able to recognize speech, images or text using trained deep neural networks. According to Nvidia, the Tesla P4 and P40 chips have a 45 times faster response rate compared to CPUs while they are four times faster than the GPU chips unveiled less than a year ago.
Nvidia Corp. has unveiled two more graphics processing unit (GPU) chips aimed at the fast-growing branch of artificial intelligence called deep learning. At
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Multi-core programming is hard, but ARM just made it harder.
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