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It may hurt your brain to think about it, but it appears that the answer is possibly to be yes, or at least the numbers are almost in the same ballpark.

Astrophysicists in fact set out to answer this question about a decade ago. It’s a complicated problem to solve, but it’s somewhat easier if you throw in a couple of qualifiers — that we are talking about stars in the observable universe; and grains of sand on the whole planet, not just the seashores.

The researchers started by calculating the luminosity density of a section of the cosmos — this is a calculation of how much light is in that space. They then utilized this calculation to guess the number of stars needed to make that amount of light. This was quite a mathematical challenge!

“You have to suppose that you can have one type of star signify all types of stars,” says astrophysicist Simon Driver, Professor at the International Centre for Radio Astronomy Research in Western Australia and one of the researchers who worked on the question.

“Then let’s suppose, on average, this is a normal mass star that gives out the normal amount of light, so if I know that a part of the universe is producing this amount of light, I can now say how many stars that would associate to.”

Now armed with a guess of the number of stars within a section of the cosmos, the next challenge was to work out the size of the cosmos. Given we know that the cosmos is 13.8 billion years old, we can suppose that we exist in a sphere 13.8 billion light years in volume. But there’s a catch: the universe is possibly immeasurable in size.

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Ugh, this is just typical. You think you know the way the world works: wind blows, fire burns, wheels spin and – wait, what’s this thing doing?

What? You mean, it can actually move in any direction without so much as turning on an axis? That’s blowing my mind. I’m no gear head, but I’m sort of attached to having a steering wheel in my car, you know? Now you’re saying that self-driving cars will take those away, and now there won’t even be wheels to turn in the direction you want to go in?

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A computer simulation of a cognitive model entirely made up of artificial neurons learns to communicate through dialog starting from a state of tabula rasa —

A group of researchers from the University of Sassari (Italy) and the University of Plymouth (UK) has developed a cognitive model, made up of two million interconnected artificial neurons, able to learn to communicate using human language starting from a state of ‘tabula rasa’, only through communication with a human interlocutor. The model is called ANNABELL (Artificial Neural Network with Adaptive Behavior Exploited for Language Learning) and it is described in an article published in PLOS ONE. This research sheds light on the neural processes that underlie the development of language.

How does our brain develop the ability to perform complex cognitive functions, such as those needed for language and reasoning? This is a question that certainly we are all asking ourselves, to which the researchers are not yet able to give a complete answer. We know that in the human brain there are about one hundred billion neurons that communicate by means of electrical signals. We learned a lot about the mechanisms of production and transmission of electrical signals among neurons. There are also experimental techniques, such as functional magnetic resonance imaging, which allow us to understand which parts of the brain are most active when we are involved in different cognitive activities. But a detailed knowledge of how a single neuron works and what are the functions of the various parts of the brain is not enough to give an answer to the initial question.

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An artificial intelligence program received such high scores on a standardized test that it’d have an 80% chance of getting into a Japanese university.

The Wall Street Journal reports that the program, developed by Japan’s National Institute of Informatics, took a multi-subject college entrance exam and passed with an above-average score of 511 points out of a possible 950. (The national average is 416.) With scores like that, it has an 8 out of 10 chance of being admitted to 441 private institutions in Japan, and 33 national ones.

The AI took some time to perfect, and it still has a ways to go. The team had been working on the program since 2011, the same year IBM’s Watson dominated Jeopardy! champions Ken Jennings and Brad Rutter in a multi-day tournament. Previously, the Japanese AI program had received below-average results, but this time around, the robot did particularly well in math and history questions, which have straightforward answers, but it still received iffy marks in the physics section of the test, which requires advanced language processing skills.

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Welcome to #24 Avatar Technology Digest! We provide you with the latest news on Technology, Medical Cybernetics and Artificial Intelligence the best way we can. Here are the top stories of the last week!

1) Did you know that Disney does more than shoot box office hits and sell toys to your kids? They also have a very active Research Department that specializes in a variety of applications that can be used throughout the Disney empire. And now another interesting innovation has come out of the Research Department, as they have developed a method for generating those 3D printable robots without the need for time and energy-consuming work at all.

2) Being able to identify problems with a person’s body without subjecting them to invasive procedures is the fantasy of all Star Trek doctors. There’s even a prize offering a fortune to anyone who can effectively recreate the tricorder technology out in the real world. Now, Stanford scientists think that they’ve developed a system that, in time, could be used to spot cancerous tumors from a foot away.

3) Technology is all around us, but what happened to the robots we dreamed of as kids? The ones who could be our friends and members of our family. The robots who were as smart as our smart phone, but could walk and talk and learn and engage with us, in a way no smart phone ever could. We think the Human-like household robot Alpha 2 by Ubtech Robotics could finally be that robot, and with your support, we can make Alpha 2 a reality.

4) Imagine playing a virtual-reality boxing game, complete with a menacing opponent aiming a haymaker at your head. You get your gloves up in time to block the punch, but you feel no impact when it hits, breaking the otherwise immersive experience.
Researchers in Germany have developed Visual reality technology for an armband that lets you feel impact from virtual interactions.

TV Anchor: Olesya Yermakova @olesyayermakova
Video: Vladimir Shlykov www.GetYourMedia.ru
Hair&Make-up: Nataliya Starovoytova

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Because of its unique chemical and physical properties, graphene has helped scientists design new gadgets from tiny computer chips to salt water filters. Now a team of researchers from MIT has found a new use for the 2D wonder material: in infrared sensors that could replace bulky night-vision goggles, or even add night vision capabilities to high-tech windshields or smartphone cameras. The study was published last week in Nano Letters.

Night vision technology picks up on infrared wavelengths, energy usually emitted in the form of heat that humans can’t see with the naked eye. Researchers have known for years that because of how it conducts electricity, graphene is an excellent infrared detector, and they wanted to see if they could create something less bulky than current night-vision goggles. These goggles rely on cryogenic cooling to reduce the amount of excess heat that might muddle the image. To create the sensor, the researchers integrated graphene with tiny silicon-based devices called MEMS. Then, they suspended this chip over an air pocket so that it picks up on incoming heat and eliminates the need for the cooling mechanisms found in other infrared-sensing devices. That signal is then transmitted to another part of the device that creates a visible image. When the researchers tested their sensor, they found that it clearly and successfully picked up the image of a human hand.

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