Software that turns eye movement into musical notes lets you compose music or play a tune on a virtual instrument with just your eyes. Called Eye Conductor, it was developed by Andreas Refsgaard at the Copenhagen Institute of Interaction Design in Denmark and his colleagues to provide a musical outlet for people with physical disabilities. Caring for a boy with muscular dystrophy, Refsgaard found that it was hard to help him express himself creatively. "I wanted to create a solution that still requires practice, like a traditional instrument," he says. The system uses an off-the-shelf eye tracker with a webcam to follow a person's gaze.

From our every day perspective, the insects and spiders that surround us usually appear to encased inside smooth and shiny exoskeletons. But a new selection of macro-images reveal that if we were to meet these miniature beasts at their own level, we would discover their bodies are actually covered in thick fur, fur like bristles and scales that look almost like feathers. The world of the small has been brought into focus in stunning detail thanks to a series of macro photographs from an amateur wildlife photographer, which reveals the creatures as very different from how we normally see them. The incredible series of images show insects and arachnids in stunning detail. Captured by Russian software architect Vasily Menshov, the series reveals the hairy antennae of mosquitoes, the feather like scales of butterflies and the alien-like appearance of hoverflies.

It is one of the biggest sites in the world with over a billion users, but YouTube began life as a dating site, one its co-founders has revealed. Steve Chen, speaking at the South by Southwest in Austin to launch his news food video site Nom, said it was conceived on Valentine's day - but five days later, had not attracted a single user. 'We always thought there was something with video there, but what would be the actual practical application?' he said. 'We thought dating would be the obvious choice.' YouTube founders Steve Chen (left) and Chad Hurley: Speaking at the South by Southwest in Austin to launch his news food video site Nom, Chen said it was conceived on Valentine's day - but five days later, had not attracted a single user.

Apple's brand may be one of the most recognisable and coveted in the world, but co-founder Steve Wozniak has hinted the firm is largely unrecognisable to the'world changing' one he helped build. Wozniak puts this down to the advent of the Apple Watch, which he claims is too complex and has taken the firm into the jewellery market. During an Ask Me Anything session on Reddit, the 65-year-old said the'only difference is the band in all those watches.' Steve Wozniak (pictured) criticised the complexity and non world changing nature of the Apple Watch line, but lavished praise on the firm's CEO, Steve Cook and its rumoured plans to make a self-driving car But the computer scientist did lavish praise on Apple's boss, Tim Cook in the same interview and has high hopes for its rumoured self-driving car. Answering questions on Reddit, Wozniak wrote: 'I love my Apple Watch, but it's taken us [Apple] into a jewellery market where you're going to buy a watch between 500 or 1100 based on how important you think you are as a person.

One of the featured speakers at the inaugural Text By The Bay conference, held in San Francisco in April 2015, drew laughter when describing a neural network question-answering model that could beat human players in a trivia game. While such performance by computers is fairly well known to the general public, thanks to IBM's Watson cognitive computer, the speaker, natural language processing (NLP) researcher Richard Socher, said, the neural network model he described "was built by one grad student using deep learning" rather than by a large team with the resources of a global corporation behind them. Socher, now CEO of machine learning developer MetaMind, did not intend his remarks to be construed as a comparison of Watson to the academic model he and his colleagues built. As an illustration of the new technical and cultural landscape around NLP, however, the laughter Socher's comment drew was an acknowledgment that basic and applied research in language processing is no longer the exclusive province of those with either deep pockets or strictly academic intentions. Indeed, new tools and new techniques--particularly open source technologies such as Google's word2vec neural text processing tool--combined with steady increases in computing power, have broadened the potential for natural language processing far beyond the research lab or supercomputer.

Based on the development of several different HRI scenarios using different robots, we have been establishing the SERA ecosystem. SERA is composed of both a model and tools for integrating an AI agent with a robotic embodiment, in humanrobot interaction scenarios. We present the model, and several of the reusable tools that were developed, namely Thalamus, Skene and Nutty Tracks. Finally we exemplify how such tools and model have been used and integrated in five different HRI scenarios using the NAO, Keepon and EMYS robots. Figure 1: Our methodology as an intersection of CGI animation, Human-robot interaction (HRI) systems are spreading as a IVA and robotics techniques.

ABSTRACT Many of the current state-of-the-art Large V ocabulary Continuous Speech Recognition Systems (L VCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Index Terms -- neural networks, L VCSR, attention, speech recognition, ASR 1. INTRODUCTION Deep neural networks have become popular acoustic models for state-of-the-art large vocabulary speech recognition systems (Hinton et al., 2012a). However, in these systems most of the other components, such as Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs) andn -gram language models, are the same as in their predecessors. These combinations of neural networks and statistical models are often referred to as hybrid systems.

A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant challenge. This dissertation explores the development of discriminative models for robust image classification that exploit underlying signal structure, via probabilistic graphical models and sparse signal representations. Probabilistic graphical models are widely used in many applications to approximate high-dimensional data in a reduced complexity set-up. Learning graphical structures to approximate probability distributions is an area of active research. Recent work has focused on learning graphs in a discriminative manner with the goal of minimizing classification error. In the first part of the dissertation, we develop a discriminative learning framework that exploits the complementary yet correlated information offered by multiple representations (or projections) of a given signal/image. Specifically, we propose a discriminative tree-based scheme for feature fusion by explicitly learning the conditional correlations among such multiple projections in an iterative manner. Experiments reveal the robustness of the resulting graphical model classifier to training insufficiency.

Messages often refer to entities such as people, places and events. Correct identification of the intended reference is an essential part of communication. Lack of shared unique names often complicates entity reference. Shared knowledge can be used to construct uniquely identifying descriptive references for entities with ambiguous names. We introduce a mathematical model for `Reference by Description', derive results on the conditions under which, with high probability, programs can construct unambiguous references to most entities in the domain of discourse and provide empirical validation of these results.

Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may be unrelated to what they will be doing in an hour from now. Mindful of these challenges we propose TribeFlow, a method designed to cope with the complex challenges of learning personalized predictive models of non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow is a general method that can perform next product recommendation, next song recommendation, next location prediction, and general arbitrary-length user trajectory prediction without domain-specific knowledge. TribeFlow is more accurate and up to 413x faster than top competitors.