UK Spaceport, Faster Broadband And Driverless Electric Cars Mentioned In Queen’s Speech 2016.
Everything you need to know about the government’s futuristic vision for 2016.
Privacy is practically a joke anymore.
A hacker known as “Peace” is selling what is reportedly account information from 117 million LinkedIn users. The stolen data is said to include email addresses and passwords, which a malicious party could use to gain access to other websites and accounts for which people used the same password.
LinkedIn says it has about 433 million members worldwide, so this data could represent 27% of its user base.
The hacker says the credentials were obtained during a LinkedIn data breach in 2012 that saw 6.5 million encrypted passwords posted online, according to Motherboard. But the leak now appears to be much larger than was thought at the time. Peace is selling the data for about $2,200 (5 bitcoin) on the Dark Web, the part of the internet accessible only with a special browser that masks user identities.
Given the fact that Los Alamos Labs have been and continue to advance cyber security work on the Quantum Internet as well as work in partnerships with other labs and universities; so, why isn’t Mason not collaborating with Los Alamos on developing an improved hacker proof net? Doesn’t look like the most effective and cost efficient approach.
Imagine burglars have targeted your home, but before they break in, you’ve already moved and are safe from harm.
Now apply that premise to protecting a computer network from attack. Hackers try to bring down a network, but critical tasks are a step ahead of them, thanks to complex algorithms. The dreaded “network down” or denial of service message never flashes on your screen.
That’s the basic idea behind new research by George Mason University researchers, who recently landed some $4 million in grants from the Defense Advanced Research Projects Agency (DARPA). George Mason’s researchers are leading an effort that includes Columbia University, Penn State University and BAE Systems.
WEST LAFAYETTE, Ind. – A new highly efficient power amplifier for electronics could help make possible next-generation cell phones, low-cost collision-avoidance radar for cars and lightweight microsatellites for communications.
Fifth-generation, or 5G, mobile devices expected around 2019 will require improved power amplifiers operating at very high frequencies. The new phones will be designed to download and transmit data and videos faster than today’s phones, provide better coverage, consume less power and meet the needs of an emerging “Internet of things” in which everyday objects have network connectivity, allowing them to send and receive data.
Power amplifiers are needed to transmit signals. Because today’s cell phone amplifiers are made of gallium arsenide, they cannot be integrated into the phone’s silicon-based technology, called complementary metal-oxide-semiconductor (CMOS). The new amplifier design is CMOS-based, meaning it could allow researchers to integrate the power amplifier with the phone’s electronic chip, reducing manufacturing costs and power consumption while boosting performance.
A new form of light which makes fiber optics more secure. Los Alamos has been key player in this space due to their work on the Quantum Internet.
In a breakthrough that has the potential to alter our understanding of the fundamental nature of light, scientists from the Trinity College Dublin School of Physics and the CRANN Institute in Ireland have discovered a never before seen new form of luminescence.
Lead author Paul Eastham attests to how exciting this finding is, saying in a statement that this very fundamental property of light that has always been thought to be constant can, in fact, change.
One of the measurable characteristics of a beam of light is known as angular momentum.
For the first time, scientists at IBM Research have demonstrated reliably storing 3 bits of data per cell using a relatively new memory technology known as phase-change memory (PCM).
The current memory landscape spans from venerable DRAM to hard disk drives to ubiquitous flash. But in the last several years PCM has attracted the industry’s attention as a potential universal memory technology based on its combination of read/write speed, endurance, non-volatility and density. For example, PCM doesn’t lose data when powered off, unlike DRAM, and the technology can endure at least 10 million write cycles, compared to an average flash USB stick, which tops out at 3,000 write cycles.
This research breakthrough provides fast and easy storage to capture the exponential growth of data from mobile devices and the Internet of Things.
Good article and perfect timing for me too because I plan to see what “good” bots are available and how I can use it to eradicate troll activity around my online content.
To some unfortunate users, the internet is a minefield of harassment and hatred. But there are steps we can take to make it a lot friendlier.
If you’ve ever seen a “recommended item” on eBay or Amazon that was just what you were looking for (or maybe didn’t know you were looking for), it’s likely the suggestion was powered by a recommendation engine. In a recent interview, Co-founder of machine learning startup Delvv, Inc., Raefer Gabriel, said these applications for recommendation engines and collaborative filtering algorithms are just the beginning of a powerful and broad-reaching technology.
Gabriel noted that content discovery on services like Netflix, Pandora, and Spotify are most familiar to people because of the way they seem to “speak” to one’s preferences in movies, games, and music. Their relatively narrow focus of entertainment is a common thread that has made them successful as constrained domains. The challenge lies in developing recommendation engines for unbounded domains, like the internet, where there is more or less unlimited information.
“Some of the more unbounded domains, like web content, have struggled a little bit more to make good use of the technology that’s out there. Because there is so much unbounded information, it is hard to represent well, and to match well with other kinds of things people are considering,” Gabriel said. “Most of the collaborative filtering algorithms are built around some kind of matrix factorization technique and they definitely tend to work better if you bound the domain.”
Of all the recommendation engines and collaborative filters on the web, Gabriel cites Amazon as the most ambitious. The eCommerce giant utilizes a number of strategies to make item-to-item recommendations, complementary purchases, user preferences, and more. The key to developing those recommendations is more about the value of the data that Amazon is able to feed into the algorithm initially, hence reaching a critical mass of data on user preferences, which makes it much easier to create recommendations for new users.
“In order to handle those fresh users coming into the system, you need to have some way of modeling what their interest may be based on that first click that you’re able to extract out of them,” Gabriel said. “I think that intersection point between data warehousing and machine learning problems is actually a pretty critical intersection point, because machine learning doesn’t do much without data. So, you definitely need good systems to collect the data, good systems to manage the flow of data, and then good systems to apply models that you’ve built.”
Beyond consumer-oriented uses, Gabriel has seen recommendation engines and collaborative filter systems used in a narrow scope for medical applications and in manufacturing. In healthcare for example, he cited recommendations based on treatment preferences, doctor specialties, and other relevant decision-based suggestions; however, anything you can transform into a “model of relationships between items and item preferences” can map directly onto some form of recommendation engine or collaborative filter.
One of the most important elements that has driven the development of recommendation engines and collaborative filtering algorithms is the Netflix Prize, Gabriel said. The competition, which offered a $1 million prize to anyone who could design an algorithm to improve upon the proprietary Netflix’s recommendation engine, allowed entrants to use pieces of the company’s own user data to develop a better algorithm. The competition spurred a great deal of interest in the potential applications of collaborative filtering and recommendation engines, he said.
In addition, relative ease of access to an abundant amount of cheap memory is another driving force behind the development of recommendation engines. An eCommerce company like Amazon with millions of items needs plenty of memory to store millions of different of pieces of item and correlation data while also storing user data in potentially large blocks.
“You have to think about a lot of matrix data in memory. And it’s a matrix, because you’re looking at relationships between items and other items and, obviously, the problems that get interesting are ones where you have lots and lots of different items,” Gabriel said. “All of the fitting and the data storage does need quite a bit of memory to work with. Cheap and plentiful memory has been very helpful in the development of these things at the commercial scale.”
Looking forward, Gabriel sees recommendation engines and collaborative filtering systems evolving more toward predictive analytics and getting a handle on the unbounded domain of the internet. While those efforts may ultimately be driven by the Google Now platform, he foresees a time when recommendation-driven data will merge with search data to provide search results before you even search for them.
“I think there will be a lot more going on at that intersection between the search and recommendation space over the next couple years. It’s sort of inevitable,” Gabriel said. “You can look ahead to what someone is going to be searching for next, and you can certainly help refine and tune into the right information with less effort.”
While “mind-reading” search engines may still seem a bit like science fiction at present, the capabilities are evolving at a rapid pace, with predictive analytics at the bow.