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Superaccurate GPS Chips Coming to Smartphones in 2018

Broadcom has released the first mass-market GPS chips that use newer satellite signals to boost accuracy to 30 centimeters

Illustration: Miguel Navarro/Getty Images
We’ve all been there. You’re driving down the highway, just as Google Maps instructed, when Siri tells you to “proceed east for one-half mile, then merge onto the highway.” But you’re already on the highway. After a moment of confusion and perhaps some rude words about Siri and her extended AI family, you realize the problem: Your GPS isn’t accurate enough for your navigation app to tell if you’re on the highway or on the road beside it.

Those days are nearly at an end. At the ION GNSS+ conference in Portland, Ore., today Broadcom announced that it is sampling the first mass-market chip that can take advantage of a new breed of global navigation satellite signals and will give the next generation of smartphones 30-centimeter accuracy instead of today’s 5 meters. Even better, the chip works in a city’s concrete canyons, and it consumes half the power of today’s generation of chips. The chip, the BCM47755, has been included in the design of some smartphones slated for release in 2018, but Broadcom would not reveal which.

GPS and other global navigation satellite systems (GNSSs), such as Europe’s Galileo, Japan’s QZSS, and Russia’s Glonass, allow a receiver to determine its position by calculating its distance from three or more satellites. All GNSS satellites—even the oldest generation still in use—broadcast a message called the L1 signal, which includes the satellite’s location, the time, and an identifying signature pattern. A newer generation broadcasts a more complex signal called L5 at a different frequency in addition to the legacy L1 signal. The receiver essentially uses these signals to fix its distance from each satellite based on how long it takes the signal to go from satellite to receiver.

Broadcom’s receiver first locks onto the satellite with the L1 signal and then refines its calculated position with L5. The latter is superior, especially in cities, because it is much less prone to distortions from multipath reflections than L1.

In a city, the satellite’s signals reach the receiver both directly and by bouncing off of one or more buildings. The direct signal and any reflections arrive at slightly different times, and if they overlap, they add up to form a sort of signal blob. The receiver is looking for the peak of that blob to fix the time of arrival. But the messier the blob, the less accurate that fix, and the less accurate the final calculated position will be.

However, L5 signals are so brief that the reflections are unlikely to overlap with the direct signal. The receiver chip can simply ignore any signal after the first one it receives, which is the direct path. The Broadcom chip also uses information in the phase of the carrier signal to further improve accuracy.

Though there are advanced systems that use L5 on the market now, these are generally for industrial purposes, such as oil and gas exploration. Broadcom’s BCM47755 is the first mass-market chip that uses both L1 and L5.

Why is this only happening now? “Up to now there haven’t been enough L5 satellites in orbit,” says Manuel del Castillo, associate director of GNSS product marketing at Broadcom. At this point, there are about 30 such satellites in orbit, counting a set that only flies over Japan and Australia. Even in a city’s “narrow window of sky you can see six or seven, which is pretty good,” Del Castillo says. “So now is the right moment to launch.”

Broadcom had to get the improved accuracy to work within a smartphone’s limited power budget. Fundamentally, that came down to three things: moving to a more power-efficient 28-nanometer-chip manufacturing process, adopting a new radio architecture (which Broadcom would not disclose the details of), and designing a power-saving dual-core sensor hub. In total, they add up to a 50 percent power savings over Broadcom’s previous, less accurate chip. 

In smartphones, sensor hubs take the raw data from the system’s sensors and process it to provide only the information the phone’s applications processor needs, thereby taking the computational burden and its accompanying power draw off of the applications processor. For instance, a sensor hub might monitor the accelerometer looking for signs that you had flipped your phone’s orientation from vertical to horizontal. It would then just send the applications processor the equivalent of the word “horizontal” instead of a stream of complex accelerations.

The sensor hub in the BCM47755 takes advantage of the ARM’s “big.LITTLE” design—a dual-core architecture in which a simple low-power processor core is paired with a more complex core. The low-power core, in this case an ARM Cortex M-0, handles simple, continuous tasks. The more powerful but power-hungry core, a Cortex M-4, comes in only when it’s needed.

The BCM4775 is just the latest development in a global push for centimeter-level navigation accuracy. Bosch, Geo++, Mitsubishi Electric, and U-blox established a joint venture called Sapcorda Services in August, to provide centimeter-level accuracy. Sapcorda seems to depend on using ground stations to measure errors in GPS and Galileo satellite signals due to atmospheric distortions. Those measurements would then be sent to receivers in handsets and other systems to improve accuracy.

Japan’s US $1.9 billion Quasi-Zenith Satellite System (QZSS) also relies on error correction, but it additionally improves on urban navigation by adding a set of satellites that guarantees one is visible directly overhead even in the densest part of Tokyo. The third of those four satellites launched in August. A fourth is planned for October, and the system is to come online in 2018.

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10 Biggest Cyber Crimes And Data Breaches

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Infographic Recap: 10 Biggest Cyber Crimes And Data Breaches

 

You may not think you’re at risk of a cyber-attack, but in today’s digital world, it’s a huge problem. Hackers can learn anything about you by stealing data, such as social security numbers, computer passwords, credit card information, health care data, bank account information, and more. According to an IBM study, as reported by CBS, approximately 1.5 million cyber-attacks occur annually. That’s three attacks per minute!

Businesses are at a huge risk of attack. If a hacker can get ahold of their customer data files, they can easily get their hands on hundreds or thousands of people’s data in one go. Depending on the data they’ve stolen, they can use the information to steal identities, access personal finance accounts, get ahold of trade secrets, and more. Despite 32 percent of organizations being affected by cybercrime, almost half don’t think they need an incident response plan in place according to PwC. That’s because many of them-44 percent according to UK research—don’t believe they’ll be targeted.

Anyone, whether they’re an individual or a business organization, is at risk of becoming victim to a cyber-attack. These attacks work in many ways. Hackers can get ahold of your information through computer viruses, phishing scams, denial-of-service attacks, and even in-person scams where hackers access your on-site computer or company servers. All of these methods can be used to target a large company or a single individuals.

What’s even more frightening is that cybercrime is on the rise. The global cost of cybercrime was around $500 billion in 2015. That figure is expected to rise to $2 trillion by 2019, says Juniper Research. The average cost of a single data breech will hit $150 million by 2020.

If you don’t think you’re at risk of a cyber-attack, think again. Some of the biggest companies with the largest set of resources have been hit hard by cyber criminals. The infographic below highlights the top 10 cybercrimes and data breaches that have been reported to date, chosen based on either size or significance. These attacks include everything from stealing credit card data to social security numbers, birthdates, health data, and more from big companies like Yahoo, Home Depot, Google, and even government agencies like the IRS. It just goes to show that anyone holding sensitive customer data is at risk and that it takes very careful measures to mitigate your risk of a breech.

Get the details of these top 10 massive breaches below. This infographic is made by TheBestVPN.com team.

TheBestVPN - infographic


 

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5 Things To Watch In AI And Machine Learning

Five Things To Watch In AI And Machine Learning In 2017

 

Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). During the year, we saw nearly every high tech CEO claim the mantel of becoming an “AI Company”. However, only a few companies were actually able to monetize their significant investments in AI, notably Amazon AMZN +0.71%, Baidu , Facebook FB +1.75%, Google GOOGL +3.77%, IBM IBM -0.02%, Microsoft MSFT +0.29%, Tesla Motors TSLA +1.75% and NVIDIA NVDA -1.29%. But 2016 was nonetheless a year of many firsts. As a posterchild for the potential for ML, Google Deep Mind mastered the subtle and infinitely complex game of GO, soundly beating the reigning world champion. And more than a few cool products were introduced that incorporated Machine Learning, from the first autonomous vehicles to new “intelligent” household assistants such as Google Home and Amazon Echo. But will 2017 finally usher in the long-promised age of Artificial Intelligence?

NVIDIA's Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

NVIDIA’s Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

Two domains: AI and Machine Learning. These terms are not interchangeable. Machine learning, a completely different way to program a computer by training it with a massive ocean of sample data, is real and is here to stay. General Artificial Intelligence remains a distant goal and is perhaps 5-20 years away depending on the specific domain of the “intelligence” being learned. To be sure, computers trained using Machine Learning hold tremendous promise, as well as the potential for massive disruption in the workplace. But these systems remain a far cry from genuine intelligence. Just ask Apple AAPL -0.14% Siri, and you will see what I mean. The hype around AI, and confusion over what the term actually means, will inevitably lead to some disillusionment as the limitations of this technology become apparent.

With that context in mind, here’s what I expect for the coming year for Machine Learning and AI.

1. Hardware accelerators for Machine Learning will proliferate.

Today, nearly all training of deep neural networks (DNNs) is performed using NVIDIA GPUs. Conversely, DNN inference, or the actual use of a trained network can be done efficiently on CPUs, GPUs, FPGAs, or even specialized ASICs such as the Google TPU, depending on the type of data being analyzed. Both training and inference markets will be hotly contested in 2017, as Advanced Micro Devices GPUs, Intel’s newly acquired Nervana chips, NVIDIA, Xilinx and several startups all launch accelerators specifically targeting this lucrative market. If you would like a deeper dive into the various semiconductor alternatives for AI, please see my companion article on this subject here.

2. Select application domains will leverage Machine Learning to improve efficiency of mission-critical processes.

If you are trying to find the killer AI app, the increasingly pervasive nature of the technology will make it difficult to identify. However, Machine Learning has begun to deliver spectacular results in very specific niches where the pattern recognition capabilities can be exploited, and this trend will continue to expand into new markets in 2017.

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LATEST SCIENCE & TECHNOLOGY NEWS **Kurzwel**

Monday | April 24, 2017
DAILY EDITION
LATEST SCIENCE & TECHNOLOGY NEWS

The first 2D microprocessor — based on a layer of just 3 atoms

April 24, 2017
Overview of the entire chip. AC=Accumulator, internal buffer; PC=Program Counter, points at the next instruction to be executed; IR=Instruction Register, used to buffer data- and instruction-bits received from the external memory; CU=Control Unit, orchestrates the other units according to the instruction to be executed; OR=Output Register, memory used to buffer output-data; ALU=Arithmetic Logic Unit, does the actual calculations. [3] (credit: TU Wien)May one day replace traditional microprocessor chips as well as open up new applications in flexible electronics

Researchers at Vienna University of Technology (known as TU Wien) in Vienna, Austria, have developed the world’s first two-dimensional microprocessor — the most complex 2D circuitry so far. Microprocessors based on atomically thin 2D materials promise to one day replace traditional microprocessors as well as open up new applications in flexible electronics. Consisting of 115 … more…

‘Negative mass’ created at Washington State University

April 21, 2017
Experimental images of an expanding spin-orbit superfluid Bose-Einstein condensate at different expansion times (credit: M. A. Khamehchi et al./Physical Review Letters)Washington State University (WSU) physicists have created a fluid with “negative mass,” which means that if you push it, it accelerates toward you instead of away, in apparent violation of Newton’s laws. The phenomenon can be used to explore some of the more challenging concepts of the cosmos, said Michael Forbes, PhD, a WSU assistant … more…

NEW EVENTS

london_futuristsWho can save Humanity from Superintelligence?

Dates: Apr 29, 2017
Location: London, UK
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ai-summit-logoThe AI Summit San Francisco

Dates: Sep 27 – 28, 2017
Location: San Francisco, California
more…

Visit KurzweilAI.net

6 Blockchain-based Digital ID Management Platforms

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