Scientists at Glasgow University have established a world first by cracking the communication code of our brains. Pioneering research in the field of cognitive neuroimaging has revealed how brains process what we see. The work has been led by Prof Philippe Schyns, the head of Glasgow's school of psychology, with more than a little help from Voltaire and Salvador Dali. How Dali's mind worked is a matter of continuing conjecture. But one of his works has helped unlock how our minds work.

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words. While unsupervised techniques have been proposed to overcome this data sparsity problem, such techniques have not outperformed supervised methods. In this paper, we propose a new approach to building semi-supervised WSD systems that combines a small amount of sense-annotated data with information from Word Sense Induction, a fully-unsupervised technique that automatically learns the different senses of a word based on how it is used. In three experiments, we show how sense induction models may be effectively combined to ultimately produce high-performance semi-supervised WSD systems that exceed the performance of state-of-the-art supervised WSD techniques trained on the same sense-annotated data. We anticipate that our results and released software will also benefit evaluation practices for sense induction systems and those working in low-resource languages by demonstrating how to quickly produce accurate WSD systems with minimal annotation effort.

A century ago, more than 60,000 tigers roamed the wild. Today, the worldwide estimate has dwindled to around 3,200. Poaching is one of the main drivers of this precipitous drop. Whether killed for skins, medicine or trophy hunting, humans have pushed tigers to near-extinction. The same applies to other large animal species like elephants and rhinoceros that play unique and crucial roles in the ecosystems where they live.

See if your favorite'Game of Thrones' character will survive or die with this machine learning algorithm. You don't have to be a diehard Game of Thrones fan to know that characters are killed off left and right in the HBO series. What real fans don't know is the fate of their favorite characters in season 6, especially since war is coming. If you can't bear to wait one more second wondering what happened to Jon Snow out in the cold, you can check out this site that uses a machine learning algorithm to reveal which GoT characters will probably kick the bucket next. The algorithm was developed as part of a project called "A Song of Ice and Data" by students in a JavaScript Course at the Technical University of Munich.

IMAGE: Researchers collect information for the design of PAWS in a protected area for a trial patrol. A century ago, more than 60,000 tigers roamed the wild. Today, the worldwide estimate has dwindled to around 3,200. Poaching is one of the main drivers of this precipitous drop. Whether killed for skins, medicine or trophy hunting, humans have pushed tigers to near-extinction.

When we think of automation in the workplace, the first jobs that come to mind of being most at threat are low paying, low-skilled jobs. While this is definitely true, advances in technology are starting to threaten high paying, higher skilled jobs. Due to the advancements in robotics, artificial intelligence and machine learning, there seem to be very few jobs, if any, that will be completely immune to, if not replacement, then some sort of alteration. This market snapshot looks at how the progression of machine learning could impact on jobs we thought immune to automation. We look at the impact of automation on the middle class and what support would need to be available to people losing their jobs.

Tesla founder, Elon Musk, is working on a self-driving vehicle that could replace buses. The billionaire says the mystery vehicle will reduce traffic in cities, but declined to discuss any more details. 'We have an idea for something which is not exactly a bus but would solve the density problem for inner city situations,' Musk said at a transport conference in Norway. Tesla founder, Elon Musk, is working on a self-driving vehicle that could replace buses. 'We have an idea for something which is not exactly a bus but would solve the density problem for inner city situations,' Musk said at a transport conference in Norway'Autonomous vehicles are key,' he said of the project, declining to reveal more. 'I don't want to talk too much about it.

If you haven't been eagerly awaiting Game of Thrones season six, then you're probably no friend of mine. I'm also going to hazard a guess that you haven't quite hit it off with the cultural touchstone that is George R R Martin's fantasy epic, or its HBO adaptation. Spoiler alert: a lot of people die in Game of Thrones, generally in a grisly way and usually unexpectedly. That's why any news around who might die next is always welcome. Thanks to some researchers at the Technical University of Munich, we may know the next Game of Thrones star to shuffle off this mortal coil. Using the power of "Big Data", the research team put together a set of machine-learning algorithms to trawl through data from both the book and the TV show to predict who will get the axe next.

Earlier this month, György Buzsáki of New York University in New York City showed a slide that sent a murmur through an audience in the Grand Ballroom of New York's Midtown Hilton during the annual meeting of the Cognitive Neuroscience Society. It wasn't just the grisly image of a human cadaver with more than 200 electrodes inserted into its brain that set people whispering; it was what those electrodes detected--or rather, what they failed to detect. When Buzsáki and his colleague, Antal Berényi of the University of Szeged in Hungary, mimicked an increasingly popular form of brain stimulation by applying alternating electrical current to the outside of the cadaver's skull, the electrodes inside registered little. Hardly any current entered the brain. On closer study, the pair discovered that up to 90% of the current had been redirected by the skin covering the skull, which acted as a "shunt," Buzsáki said. For many meeting attendees, the unusual study heightened serious doubts about the mechanism and effectiveness of transcranial direct current stimulation, an experimental, noninvasive treatment that uses electrodes to deliver weak current to a person's scalp or forehead.

Every once in a while a new algorithms comes and makes all others (in the same domain) seems kind of obsolete when it comes to the same domain. Will deep learning make that related algorithms (backpropagation NN, GMM, HMM, ...)? There are several reasons why there will always be a place for other algorithms to be better suited than deep learning in some applications. There are many cases where you need to have an understanding of the domain in order to have optimal results. While some proponents of Deep Learning describe their approach as being general-purpose, I don't think that will ever be true.