Skynet is coming.

[krenshala]

I don't understand. You're saying that android viruses can infect iPhones despite they have a different operating system? If this is so, why do they call them "android viruses"?

No, I'm saying that iPhones are at best just as vulnerable as Andoid phones to those "viruses", and in some ways they are more vulnerable.

[GIThruster]

IT guys always make me feel stupid--but in a good way.

"Any sufficiently advanced technology is indistinguishable from magic." ~Arthur C. Clarke

[krenshala]

When I interviewed for my current job (mid level sys-admin), my soon-to-be boss' boss thought I was there for a senior sys-admin role when he did a quick interview. His questions and how smart he clearly was made me feel like I was so dumb I needed help with complex tasks like not falling over and breathing. Myself, I try not to make people feel dumb about computer stuff though I know I fail at it sometimes (kids and their toys, they'll tell you more than you need, and confuse things sometimes).

[choff]

The beginnings of the liquid metal terminator.

http://news.discovery.com/tech/alternat ... 150311.htm

[Diogenes]

Iris Scanner Identifies a Person 40 Feet Away

Like similar biometric technologies — fingerprint or facial recognition systems — the Carnegie Mellon project uses mathematical pattern-recognition techniques. The technology captures images from a live photographic or video feed and runs them through a database to find a potential match.

Like fingerprints, every iris is unique — thanks to enormously complex patterns that remain the same throughout a person’s lifetime. High-resolution cameras can capture images of the iris from a distance using light in the near-infrared wavelength band.

http://news.discovery.com/tech/gear-and ... 150410.htm

[DeltaV]

Probabilistic programming does in 50 lines of code what used to take thousands
http://phys.org/news/2015-04-probabilis ... sands.html

[DeltaV]

Researchers create first neural-network chip built just with memristors
http://phys.org/news/2015-05-neural-net ... stors.html

[williatw]

Cheap Centimeter-Precision GPS For Cars, Drones, Virtual Reality




Photo: Cockrell School of Engineering/University of Texas at Austin

The GPS navigation system in your mobile phone can get you to the airport or the closest coffee shop. But it can’t pinpoint your exact location on a city sidewalk; that location can be off by 10 meters.

Engineers at the University of Texas at Austin have now made a small, cheap GPS system for mobile devices that gives centimeter-precision positioning accuracy. Such centimeter precision could let drones deliver packages to your porch, autonomous vehicles navigate safely, and be used in precision farming. It could also allow for some neat virtual reality tricks and games if coupled with a smartphone camera, says Todd Humphreys, an aerospace engineering researcher at UT Austin. “People could have an engaging mind-blowing experience with physically immersive virtual reality,” he says.

Centimeter-accurate GPS systems aren’t new. They’ve been used for 20 years in agriculture and surveying. Experimental self-driving cars like the one being developed by Google also rely on them. But these systems use fancy, dinner plate-sized antennas that can cost anywhere from $1,000 to over $3,000 with receivers that are even pricier, Humphreys says. “If we want to see autonomous or even semi-autonomous vehicles go mainstream, we need a cheap way to provide centimeter or decimeter-scale accuracy.”

So he and his colleagues decided to experiment with the cheap, low-quality antennas found in smartphones. Unlike the fancy expensive ones, these antennas, which Humphreys describes as looking like “smashed paper clips,” are especially susceptible to crucial errors that can reduce GPS positioning accuracy.

One such error, called multipath error, is due to RF signals from GPS satellites that reach the antenna after bouncing off the ground or objects like trees and buildings. The other error happens when those trees and buildings block the RF signal. Both cause the receiver to miscalculate the direct distance between itself and the satellite, which is required to calculate location.

The UT Austin team has created software over the past six years that uses sophisticated signal processing techniques to process the data from a smartphone GPS antenna and reduce the effect of those errors. The software currently runs on a separate computer from the smartphone, but Humphreys says it would eventually run on the phone’s internal processor.

The engineers described the technology recently in GPS World magazine. Since then, they have been experimenting with a $5 antenna that is of higher quality than a smartphone antenna. Humphreys says that they were able to use this antenna and their software with a Samsung Galaxy phone to generate a 3-D map of their building’s rooftop referenced to global coordinates with an accuracy of two centimeters. They were also able to precisely track a virtual reality headset with the same precision.

The team now has a startup, Radiosense LLC, to further evolve the technology. They are also working with Samsung, which has supported the research so far, to develop a low-cost snap-on system, containing the antenna and software-based receiver. Humphreys says the receiver should eventually cost less than $50. Samsung is also interested in developing the technology for the automobile space, he says.

http://spectrum.ieee.org/tech-talk/tran ... and-drones

[choff]

Hopefully you don't walk into a tree with that headgear blinding you.

[GIThruster]

Centimeter-accurate GPS systems aren’t new. They’ve been used for 20 years in agriculture and surveying.

I totally did not know that. I didn't know DOD had that kind of precision. Count me flabbergasted.

[williatw]

DEEP LEARING WILL SOON GIVE US SUPER-SMART ROBOTS


Yann LeCun. Josh Valcarcel/WIRED

Yann LeCun is among those bringing a new level of artificial intelligence to popular internet services from the likes of Facebook, Google, and Microsoft.

As the head of AI research at Facebook, LeCun oversees the creation of vast “neural networks” that can recognize photos and respond to everyday human language. And similar work is driving speech recognition on Google’s Android phones, instant language translation on Microsoft’s Skype service, and so many other online tools that can “learn” over time. Using vast networks of computer processors, these systems approximate the networks of neurons inside the human brain, and in some ways, they can outperform humans themselves.

This week in the scientific journal Nature, LeCun—also a professor of computer science at New York University—details the current state of this “deep learning” technology in a paper penned alongside the two other academics most responsible for this movement: University of Toronto professor Geoff Hinton, who’s now at Google, and the University of Montreal’s Yoshua Bengio. The paper details the widespread progress of deep learning in recent years, showing the wider scientific community how this technology is reshaping our internet services—and how it will continue to reshape them in the years to come.

But as LeCun tells WIRED, deep learning will also extend beyond the internet, pushing into devices that can operate here in the physical world—things like robots and self-driving cars. Just last week, researchers at the University of California at Berkeley revealed a robotic system that uses deep learning tech to teach itself how to screw a cap onto a bottle. Early this year, big-name chip maker Nvidia and an Israeli company called Mobileye revealed that they were developing deep learning systems that can help power self-driving cars.

LeCun has been exploring similar types of “robotic perception” for over a decade, publishing his first paper on the subject in 2003. The idea was to use deep learning algorithms as a way for robots to identify and avoid obstacles as they moved through the world—something not unlike what’s needed with self-driving cars. “It’s now a very hot topic,” he says.

Yes, Google and some many others have already demonstrated self-driving cars. But according to researchers, including LeCun, deep learning can advance the state of the art—just as it has vastly improved technologies such as image recognition and speech recognition. Deep learning algorithms date back to the 1980s, but now that they can tap the enormously powerful network of machines available to today’s companies and researchers, they provide a viable way for systems to teach themselves tasks by analyzing enormous amounts of data.

“This is a chance for us to change the model of learning from very shallow, very confined statistics to something extremely open-ended,” Sebastian Thrun, who helped launched the Google self-driving car project, said of deep learning in an interview this past fall.

Thrun has left Google, but odds are, the company is already exploring the use of deep learning techniques with its autonomous cars (the first of which are set to hit the road this summer). According to Google research fellow Jeff Dean, the company is now using these techniques across dozens of services, and self-driving cars, which depend so heavily on image recognition, are one of the more obvious applications.

Trevor Darrell, one of the researchers working on deep learning robots at Berkeley, says his team is also exploring the use of the technology in autonomous automobiles. “From a researchers perspective, their are many commonalities in what it takes to move an arm to insert a peg into a hole and what it takes to navigate a car or a flying vehicle through an obstacle course,” he says.

Deep learning is particularly interesting, he says, because it has transformed so many different areas of
research. In the past, he says, researchers used very separate techniques for speech recognition, image recognition, translation, and robotics. But now one this one set of techniques—though a rather broad set—can serve all these fields.

The result: all of these fields are suddenly evolving at a much faster rate. Face recognition has hit the mainstream. So has speech recognition. And the sort of autonomous machines his team is working on, Darrell says, could reach the commercial market within the next five years. AI is here. But it will soon arrive in a much bigger way.

http://www.wired.com/2015/05/remaking-g ... -robotics/

[Diogenes]

Centimeter-accurate GPS systems aren’t new. They’ve been used for 20 years in agriculture and surveying.

I totally did not know that. I didn't know DOD had that kind of precision. Count me flabbergasted.

They measure off of calibrated ground stations nearby where they are operating. With a reference source on the ground, they can get highly accurate measurements RELATIVE TO THE REFERENCE SOURCE.


Recently I have read that they are getting centimeter accuracy to the GPS Satellites.



I do not grasp how such a thing is possible. I would think atmospheric propagation issues (changing characteristics of refraction, reflection, diffraction etc.) would preclude the possibility, plus issues relating to such precise timing on multiple transmitters and if nothing else, a healthy dose of Heisenberg uncertainty.

[Diogenes]

Humans will be cyborgs within 200 years, expert predicts

“I think it is likely in the next 200 years or so Homo sapiens will upgrade themselves into some idea of a divine being, either through biological manipulation or genetic engineering of by the creation of cyborgs: part organic, part non-organic,” Harari said during his presentation the Hay Festival in the UK, as Sarah Knapton reports for the Telegraph. “It will be the greatest evolution in biology since the appearance of life … we will be as different from today’s humans as chimps are now from us.”

http://www.sciencealert.com/wealthy-hum ... t-predicts

[paperburn1]

A large amount of GPS accuracy depends on what type equipment you use, what kind of antenna , and of course how many satellites you are able to acquire.
The flight style GPS for private pilots are typical accurate between 8 to 10 feet left and right and 15 to 30 feet in the up and down direction.
That being said the ones we use at work are three times that good. These also have fixed external antenna. your average phone is ten times worse than the above quoted figures.

[Diogenes]

A large amount of GPS accuracy depends on what type equipment you use, what kind of antenna , and of course how many satellites you are able to acquire.
The flight style GPS for private pilots are typical accurate between 8 to 10 feet left and right and 15 to 30 feet in the up and down direction.
That being said the ones we use at work are three times that good. These also have fixed external antenna. your average phone is ten times worse than the above quoted figures.

That's a long ways from "centimeter accuracy", but i've seen an article where they claim they can do this.