Curry Chandler

Curry Chandler is a writer, researcher, and independent scholar working in the field of communication and media studies. His writing on media theory and policy has been published in the popular press as well as academic journals. Curry approaches the study of communication from a distinctly critical perspective, and with a commitment to addressing inequality in power relations. The scope of his research activity includes media ecology, political economy, and the critique of ideology.

Curry is a graduate student in the Communication Department at the University of Pittsburgh, having previously earned degrees from Pepperdine University and the University of Central Florida.

Mice memory implants, augmented reality trends, predictive policing, more

Scientists have created a false memory in mice by manipulating neurons that bear the memory of a place. The work further demonstrates just how unreliable memory can be. It also lays new ground for understanding the cell behavior and circuitry that controls memory, and could one day help researchers discover new ways to treat mental illnesses influenced by memory.

Augmented reality blurs the line between the virtual and real-world environment. This capability of augmented reality often confuses users, making them unable to determine the difference between the real world experience and the computer generated experience. It creates an interactive world in real-time and using this technology, businesses can give customers the opportunity to feel their products and service as if it is real right from their current dwelling.

AR technology imposes on the real world view with the help of computer-generated sensory, changing what we see. It can use any kind of object to alter our senses. The enhancements usually include sound, video, graphics and GPS data. And its potentials are tremendous as developers have just started exploring the world of augmented reality. However, you must not confuse between virtual reality and augmented reality, as there is a stark difference between them. Virtual reality, as the name suggests, is not real. It is just a made up world. On the other hand, augmented reality is enhancing the real world, providing an augmented view of the reality. The enhancements can be minor or major, but AR technology only changes how the real world around the user looks like.

Augmentedrealitytrends.com: Why augmented reality and why your prime focus is on retail industry?

SeeMore Interactive: We recognize the importance of merging brick-and-mortar retail with cloud-based technology to create the ultimate dynamic shopping experience. It’s simply a matter of tailoring a consumer’s shopping experience based on how he or she wants to shop; the ability to research reviews, compare prices, receive new merchandise recommendations, share photos and make purchases while shopping in-store or from the comfort of their home.

Deep learning is based on neural networks, simplified models of the way clusters of neurons act within the brain that were first proposed in the 1950s. The difference now is that new programming techniques combined with the incredible computing power we have today are allowing these neural networks to learn on their own, just as humans do. The computer is given a huge pile of data and asked to sort the information into categories on its own, with no specific instruction. This is in contrast to previous systems that had to be programmed by hand. By learning incrementally, the machine can grasp the low-level stuff before the high-level stuff. For example, sorting through 10,000 handwritten letters and grouping them into like categories, the machine can then move on to entire words, sentences, signage, etc. This is called “unsupervised learning,” and deep learning systems are very good at it.

Intelligent policing can convert these modest gains into significant reductions in crime. Cops working with predictive systems respond to call-outs as usual, but when they are free they return to the spots which the computer suggests. Officers may talk to locals or report problems, like broken lights or unsecured properties, that could encourage crime. Within six months of introducing predictive techniques in the Foothill area of Los Angeles, in late 2011, property crimes had fallen 12% compared with the previous year; in neighbouring districts they rose 0.5% (see chart). Police in Trafford, a suburb of Manchester in north-west England, say relatively simple and sometimes cost-free techniques, including routing police driving instructors through high-risk areas, helped them cut burglaries 26.6% in the year to May 2011, compared with a decline of 9.8% in the rest of the city.

Although they may all look very different, the cities of the future share a new way of doing things, from sustainable buildings to walkable streets to energy-efficient infrastructure. While some are not yet complete – or even built – these five locations showcase the cutting edge of urban planning, both in developing new parts of an existing metropolitan area and building entirely new towns. By 2050, it is forecast that 70% of the world’s population will live in cities. These endeavours may help determine the way we will live then, and in decades beyond.

Mention thorium—an alternative fuel for nuclear power—to the right crowd, and faces will alight with the same look of spirited devotion you might see in, say, Twin Peaks and Chicago Cubs fans. People love thorium against the odds. And now Bill Gates has given them a new reason to keep rooting for the underdog element.

TerraPower, the Gates-chaired nuclear power company, has garnered attention for pursuing traveling wave reactor tech, which runs entirely on spent uranium and would rarely need to be refueled. But the concern just quietly announced that it's going to start seriously exploring thorium power, too.

Google might have put the kibosh on allowing x-rated apps onto Glass (for now) but that hasn't stopped the porn industry from doing what they do best: using new technology to enhance the, um, adult experience. The not yet titled film stars James Deen and Andy San Dimas.

There has always been a basic split in machine vision work. The engineering approach tries to solve the problem by treating it as a signal detection task using standard engineering techniques. The more "soft" approach has been to try to build systems that are more like the way humans do things. Recently it has been this human approach that seems to have been on top, with DNNs managing to learn to recognize important features in sample videos. This is very impressive and very important, but as is often the case the engineering approach also has a trick or two up its sleeve.

  • From Google Research:

We demonstrate the advantages of our approach by scaling object detection from the current state of the art involving several hundred or at most a few thousand of object categories to 100,000 categories requiring what would amount to more than a million convolutions. Moreover, our demonstration was carried out on a single commodity computer requiring only a few seconds for each image. The basic technology is used in several pieces of Google infrastructure and can be applied to problems outside of computer vision such as auditory signal processing.