The researchers developed a technique to coordinate multiple wireless transmitters by synchronizing their wave phases, according to a statement from MIT on Tuesday. Multiple independent transmitters will be able to send data over the same wireless channel to multiple independent receivers without interfering with each other.

Since wireless spectrum is scarce, and network congestion is only expected to grow, the technology could have important implications.

The researchers called the approach MegaMIMO 2.0 (Multiple Input, Multiple Output) .

For their experiments, the researchers set up four laptops in a conference room setting, allowing signals to roam over 802.11 a/g/n Wi-Fi. The speed and distance improvement is expected to also apply to cellular networks. A video describes the technology as well as a technical paper (registration required), which was presented this week to the Association for Computing Machinery’s Special Interest Group on Data Communications (SIGCOMM 16).

The researchers, from MIT’s Computer Science and Artificial Intelligence Lab, are: Ezzeldin Hamed, Hariharan Rahul, Mohammed Abdelghany and Dina Katabi.

Courtesy-http://www.thegurureview.net/mobile-category/mit-researchers-develop-technique-to-triple-wireless-speeds.html

Courtesy-http://www.thegurureview.net/mobile-category/mit-researchers-develop-technique-to-triple-wireless-speeds.html

The researchers have worked out that slowing the speed of light to the extent that it moves slower than flowing electrons can create an “optical boom”, the optical equivalent of a sonic boom.

Slowing the speed of light is no mean feat, but the clever folks at MIT managed it by using the honeycomb shape of carbon to slow photons to slow photons to several hundredths of their normal speed in a free space, explained researcher Ido Kaminer.

Meanwhile, the characteristics of graphene speed up electrons to a million metres a second, or around 1/300 of the speed of light in a vacuum.

The optical boom is caused when the electrons passing though the graphene reach the speed of light, effectively breaking its barrier in the carbon honeycomb and causing a shockwave of light.

As electrons move faster than the trapped light, they bleed plasmons, a form of virtual particle that represents the oscillation of electrons on the graphene’s surface.

Effectively, it is the equivalent of turning electricity into light. This is nothing new – Thomas Edison did it a century ago with fluorescent tubes – but it can efficiently and controllably generate plasmons at a scale that works with microchip technology.

The discovery could allow chip components to be made from graphene to enable the creation of light-based circuits. These circuits could be the next step in the evolution of chip and computing technology, as the transfer of data through light is far faster than using electrons in today’s chips, even the fast pixel-pushing ones.

So much faster that it’s “six orders of magnitude higher than what is used in electronics”, according to Kaminer. That’s up to a million times faster in plain English.

“There’s a lot of excitement about graphene because it could be easily integrated with other electronics,” said physics professor Marin Soljačić, a researcher on the project, who is confident that MIT can turn this theoretical experiment into a working system. “I have confidence that it should be doable within one to two years.”

This is a pretty big concept and almost sci-fi stuff, but we’re always keen to see smaller and faster chips. It also shows that the future tech envisioned by the world of sci-fi may not be that far away.

Courtesy-TheInq

Knowledge about threats is often hidden in unstructured sources such as blogs, research reports and documentation, said Kevin Skapinetz, director of strategy for IBM Security.

“Let’s say tomorrow there’s an article about a new type of malware, then a bunch of follow-up blogs,” Skapinetz explained. “Essentially what we’re doing is training Watson not just to understand that those documents exist, but to add context and make connections between them.”

Over the past year, IBM Security’s own experts have been working to teach Watson the “language of cybersecurity,” he said. That’s been accomplished largely by feeding it thousands of documents annotated to help the system understand what a threat is, what it does and what indicators are related, for example.

“You go through the process of annotating documents not just for nouns and verbs, but also what it all means together,” Skapinetz said. “Then Watson can start making associations.”

Now IBM aims to accelerate the training process. This fall, it will begin working with students at universities including California State Polytechnic University at Pomona, Penn State, MIT, New York University and the University of Maryland at Baltimore County along with Canada’s universities of New Brunswick, Ottawa and Waterloo.

Over the course of a year, the program aims to feed up to 15,000 new documents into Watson every month, including threat intelligence reports, cybercrime strategies, threat databases and materials from IBM’s own X-Force research library. X-Force represents 20 years of security research, including details on 8 million spam and phishing attacks and more than 100,000 documented vulnerabilities.

Watson’s natural language processing capabilities will help it make sense of those reams of unstructured data. Its data-mining techniques will help detect outliers, and its graphical presentation tools will help find connections among related data points in different documents, IBM said.

Ultimately, the result will be a cloud service called Watson for Cyber Security that’s designed to provide insights into emerging threats as well as recommendations on how to stop them.

Source-http://www.thegurureview.net/computing-category/ibms-watson-to-get-schooled-on-cybersecurity.html

It’s an interesting move for a man who in 2014 said artificial intelligence, or A.I., will pose a threat to the human race.

“I think we should be very careful about artificial intelligence,” Musk said about a year and a half ago during an MIT symposium. “If I were to guess at what our biggest existential threat is, it’s probably that… with artificial intelligence, we are summoning the demon. In all those stories with the guy with the pentagram and the holy water, and he’s sure he can control the demon. It doesn’t work out.”

Today, Musk is moving to help programmers use A.I. and machine learning to build smart robots and smart devices.

“We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms,” wrote Greg Brockman, OpenAI’s CTO, and John Schulman, a scientist working with OpenAI, in a blog post . “We originally built OpenAI Gym as a tool to accelerate our own RL research. We hope it will be just as useful for the broader community.”

The OpenAI Gym is meant as a tool for programmers to use to teach their intelligent systems better ways to learn and develop more complex reasoning. In short, it’s meant to make smart systems smarter.

Musk is a co-chair of OpenAI, a $1 billion organization that was unveiled last December as an effort focused on advancing artificial intelligence that will benefit humanity.

While Musk has warned of what he sees as the perils of A.I., it’s also a technology that he needs for his businesses.

The OpenAI Gym is made up of a suite of environments, including simulated robots and Atari games, as well as a site for comparing and reproducing results.

It’s focused on reinforcement learning, a field of machine learning that involves decision-making and motor control.

According to OpenAI, reinforcement learning is an important aspect of building intelligent systems because it encompasses any problem that involves making a sequence of decisions. For instance, it could focus on controlling a robot’s motors so it’s able to run and jump, or enabling a system to make business decisions regarding pricing and inventory management.

Two major challenges for developers working with reinforcement learning are the lack of standard environments and the need for better benchmarks.

Musk’s group is hoping that the OpenAI Gym addresses both of those issues.

Source- http://www.thegurureview.net/aroundnet-category/elon-musk-opens-training-gym-for-ai-programmers.html

The architecture they have devised ought to be relatively easy to scale, and could therefore form the basis of workable quantum computers in the future – with a bit of “engineering effort” and “an enormous amount of money”, according to Isaac Chuang, professor of physics, electrical engineering and computer science at MIT.

Chuang’s team has put together a prototype comprising the first five quantum bits (or qubits) of a quantum computer. This is being tested on mathematical factoring problems, which could have implications for applications that use factoring as the basis for encryption to keep information, including credit card details, secure.

The proof-of-concept has been applied only to the number 15, but the researchers claim that this is the “first scalable implementation” of quantum computing to solve Shor’s algorithm, a quantum algorithm that can quickly calculate the prime factors of large numbers.

“The team was able to keep the quantum system stable by holding the atoms in an ion trap, where they removed an electron from each atom, thereby charging it. They then held each atom in place with an electric field,” explained MIT.

Chuang added: “That way, we know exactly where that atom is in space. Then we do that with another atom, a few microns away – [a distance] about 100th the width of a human hair.

“By having a number of these atoms together, they can still interact with each other because they’re charged. That interaction lets us perform logic gates, which allow us to realise the primitives of the Shor factoring algorithm. The gates we perform can work on any of these kinds of atoms, no matter how large we make the system.”

Chuang is a pioneer in the field of quantum computing. He designed a quantum computer in 2001 based on one molecule that could be held in ‘superposition’ and manipulated with nuclear magnetic resonance to factor the number 15.

The results represented the first experimental realisation of Shor’s algorithm. But the system wasn’t scalable as it became more difficult to control as more atoms were added.

However, the architecture that Chuang and his team have put together is, he believes, highly scalable and will enable the team to build quantum computing devices capable of solving much bigger mathematical factors.

“It might still cost an enormous amount of money to build, [and] you won’t be building a quantum computer and putting it on your desktop anytime soon, but now it’s much more an engineering effort and not a basic physics question,” said Chuang.

In other quantum computing news this week, the UK government has promised £200m to support engineering and physical sciences PhD students and fuel UK research into quantum technologies, although most of the cash will be spent on Doctoral Training Partnerships rather than trying to build workable quantum computing prototypes.

Courtesy-TheInq