A new SwiftKey keyboard hopes to serve you better typing suggestions by utilizing a miniaturized neural network. SwiftKey Neural does away with the company's tried-and-tested prediction engine in favor of a method that mimics the way the brain processes information. It's a model that's typically deployed on a grand scale for things like spam and phishing prevention in Gmail or image recognition, but very recent advancements have seen neural networks creep into phones through Google Translate, which uses one for offline text recognition. According to SwiftKey, this is the first time it's been used on a phone keyboard. To grasp how the new system works, we need to understand the old one.

Pattern Recognition is the official journal of the Pattern Recognition Society. The Society was formed to fill a need for information exchange among research workers in the pattern recognition field. Up to now, we "pattern-recognitionophiles" have been tagging along in computer science, information theory, optical processing techniques, and other miscellaneous fields. Because this work in pattern recognition presently appears in widely spread articles and as isolated lectures in conferences in many diverse areas, the purpose of the journal Pattern Recognition is to give all of us an opportunity to get together in one place to publish our work. The journal will thereby expedite communication among research scientists interested in pattern recognition.

As humans, we can distinguish between different objects easily - such as dogs wearing hats, or between oranges and bananas in a bag - but for computers this has been typically much more difficult. A team of Google researchers has developed an advanced image classification and detection algorithm called GoogLeNet, which is twice as effective than previous programs. It is so accurate it can locate and distinguish between a range of object sizes within a single image, and it can also determine an object within, or on top of, an object, within the photo. A team of California-based Google researchers developed GoogLeNet, that uses an advanced classification and detection algorithm to identify object. The software recently placed first in the ImageNet large-scale visual recognition challenge (ILSVRC).

For some birds, recognising their own eggs can be a matter of life or death. Scientists have used imafe recognition technology to show that birds defending their nests against the Common Cuckoo - which lays its lethal offspring in other birds' nests - have evolved distinctive patterns on their eggs in order to distinguish them from those laid by a cuckoo cheat. These patterns provide a defence against the cuckoo's trickery by helping birds reject the cuckoo eggs before they hatch and destroy the host's own brood. Scientists have shown that birds defending their nests against the Common Cuckoo - which lays its lethal offspring in other birds' nests - have evolved distinctive patterns on their eggs in order to distinguish them from those laid by a cuckoo cheat. Scientists from the University of Cambridge and Harvard University, in Cambridge, Massachusetts, have developed a new computer vision tool to unravel how a host bird may perceive and recognise such complex pattern information.

For years scientists have worked to find a way to make it easier for deaf and hearing impaired people to communicate. And now it is hoped that a new intelligent system could be about to transform their lives. Researchers have used image recognition to translate sign language into'readable language' and while it is early days, the tool could one day be used on smartphones. Researchers have used image recognition to translate sign language (pictured) into'readable language' and while it is early days, the tool could one day be used on smartphones Scientists from Malaysia and New Zealand came up with the Automatic Sign Language Translator (ASLT), which can capture, interpret and translate sign language. It has been tested on gestures and signs representing both isolated words and continuous sentences in Malaysian sign language, with what they claim is a high degree of recognition accuracy and speed.

Dr. Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the founding director of the Center for Unified Biometrics and Sensors. He received his Bachelor's degree with honors from the Indian Institute of Technology (IIT) in 1986, and his Ph.D. from UB in 1992. His research focus is on machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics. Dr. Govindaraju has co-authored about 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service.

Google's Hartmut Neven demonstrates his visual-search app by snapping a picture of a Salvador Dali clock in his office building. Google and other tech companies are racing to improve image-recognition software Computers can recognize some objects in images, but not all Google's engineering director predicts the technology will fully mature in 10 years Google's engineering director predicts the technology will fully mature in 10 years Santa Monica, California (CNN) -- Computers used to be blind, and now they can see. Thanks to increasingly sophisticated algorithms, computers today can recognize and identify the Eiffel Tower, the Mona Lisa or a can of Budweiser. Still, despite huge technological strides in the last decade or so, visual search has plenty more hurdles to clear. At this point, it would be quicker to describe the types of things an image-search engine can interpret instead of what it can't.

Scientists at Google have created artificial intelligence software that can describe the contents of photographs far more accurately than ever before. The software's description of pictures was similar to that written by a human. As well as making it easier to search for images, the software could be used to help blind people understand pictures better, Google said. Stanford University has also announced a breakthrough in the same field. The machine-learning software developed by Google used two neural networks - one which deals with image recognition, the other with natural language processing.

A new computer model can now mimic the human ability to learn new concepts from a single example instead of the hundreds or thousands of examples it takes other machine learning techniques, researchers say. The new model learned how to write invented symbols from the animated show Futurama as well as dozens of alphabets from across the world. It also showed it could invent symbols of its own in the style of a given language. The researchers suggest their model could also learn other kinds of concepts, such as speech and gestures. Although scientists have made great advances in machine learning in recent years, people remain much better at learning new concepts than machines.

A typical human ability is the recognition of patterns in the world around us. It constitutes the basis of each natural science: the laws of physics, the description of species in biology, or the analysis of human behavior; they are all based on seeing patterns. Also in daily life pattern recognition plays an important role: reading texts, identifying people, retrieving objects, or finding the way in a city. Once patterns are established, learned from some examples or from a teacher, we are able to classify new objects or phenomena into a class of known patterns. The study of automatic pattern recognition has two sides, one purely fundamentally scientific and one applied.