Researchers have found that learning sign language can be beneficial for hearing adults too, giving them faster reaction times in their peripheral vision. Improved peripheral vision is useful in many sports and for driving, making you more alert to changes in your peripheral field of vision. The research also found that deaf adults have far better peripheral vision and reaction times than both hearing adults and hearing adults who use sign language. Researchers at the University of Sheffield have found that learning sign language can be beneficial for hearing adults too, giving them faster reaction times in their peripheral vision. The research, conducted at the University of Sheffield's Academic Unit of Opthalmology, found that adults learning a visual-spatial language such as British sign language (BSL) had a positive impact on their visual field response.

Attend Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS and learn how to identify complex and dynamic patterns within your multilevel data. This advanced class provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM). Meet the Presenters Catherine Truxillo and Chris Daman discuss what you can expect to learn in this class. Attend a public course or enjoy the classroom experience right at your desktop, the choice is yours!

Every year some 35,000 people from around 140 countries working in the education sector gather to experience and observe ideas, practices and technologies that allow educators and learners to fulfil their potential. This year, as to be expected, it did not disappoint, with some very exciting talks and lots of great new products and technologies. Where else can you listen to Heston Blumenthal discuss how food and cooking can unleash creativity in the classroom, and see Sir Tony Robinson share stories about his love of history and his personal quest for learning, not before enjoying a talk by Sir Ken Robinson about his views on the necessity for new approaches in the education system. Where else can you listen to Heston Blumenthal discuss how food and cooking can unleash creativity in the classroom? At this year's event, Microsoft vice-president of worldwide education said: 'We've got to make technology available, but to bring it all together we have to raise the bar for how we can drive innovation and transformation', a statement that we at Jisc fully support.

Artificial General Intelligence (AGI) is an emerging field aiming at the building of "thinking machines"; that is, general-purpose systems with intelligence comparable to that of the human mind. What is currently labeled'artificial intelligence' is largely narrow automated knowledge work, lacking the flexibility and adaptability seen in animal intelligence. The pursuit of AGI begins at a foundational level, asking fundamental questions about models of cognition, knowledge acquisition, making choices through reason, thinking and conceiving the world in adaptive and intuitive ways. You emphasize the importance and value of "artificial understanding" of human language. What are the current "natural language processing" systems (Siri, Alexa, chat-bots, etc.) doing and how does this differ from what AGI is striving for w/regards to working with language? None of the language understanding systems go beyond identifying words correctly in context; this is a major step forward, but not enough.

The patter of automated machinery fills the air inside wire-basket manufacturer Marlin Steel's bustling factory in a rugged industrial section of this city. Maxi Cifarelli, 25, of Baltimore, peers through safety goggles at a flat screen, her left knee bent and heel resting on her chair. Two years after earning a fine arts degree from Towson University with a specialty in interdisciplinary object design, she now spends her work days working with a personality-free machine with a name to match: a computer numerical control, or CNC, router. With automation poised to sweep through the economy, some fear that it will kill more jobs than it creates. But Cifarelli's experience is the opposite. She befriended automation, instead of fighting it, and she has a job because of it.

One of the most significant threats faced by computer networks is from "bots." A bot is simply a program that runs on a computer without the owner's knowledge and carries out any of a number of tasks over the network and the wider internet. It can run the same tasks, such as sending emails or accessing a specific page on the internet, at a much higher rate than would be possible if a person were to carry out the task. A collection of bots in a network, used for malicious purposes, is a botnet and while they are often organized and run by a so-called botmaster there are bots that are available for hire for malicious and criminal activity. Bots might be illicitly installed on computers in the home, schools, businesses, government buildings and other installations.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Many dimensionality reduction or manifold learning algorithms optimize for retaining the pairwise similarities, distances, or local neighborhoods of data points. Spectral methods like Kernel PCA (kPCA) or isomap achieve this by computing the singular value decomposition (SVD) of some similarity matrix to obtain a low dimensional representation of the original data. However, this is computationally expensive if a lot of training examples are available and, additionally, representations for new (out-of-sample) data points can only be created when the similarities to the original training examples can be computed. We introduce similarity encoders (SimEc), which learn similarity preserving representations by using a feed-forward neural network to map data into an embedding space where the original similarities can be approximated linearly. The model optimizes the same objective as kPCA but in the process it learns a linear or non-linear embedding function (in the form of the tuned neural network), with which the representations of novel data points can be computed - even if the original pairwise similarities of the training set were generated by an unknown process such as human ratings. By creating embeddings for both image and text datasets, we demonstrate that SimEc can, on the one hand, reach the same solution as spectral methods, and, on the other hand, obtain meaningful embeddings from similarities based on human labels.

Seiya Tokui is a researcher at Preferred Networks, Inc. and a Ph.D. student at the University of Tokyo, from which he received the master's degree in mathematical informatics in 2012. He is the lead developer of Chainer, a deep learning framework. His research interests include deep learning and generative models.