Artificial Intelligence (AI) has the potential to transform the world as we know it, but it's a hotly debated topic. However, new research commissioned by UC EXPO, Europe's largest unified communications and collaboration (UC&C) event, has uncovered that 85% of UK residents want AI to support at least part of their current job. Almost half of these individuals (44%) want AI technology to do all of their job, leaving them to simply manage the AI system. Despite 20% of Brits associating AI with the end of the world, it's a technology which is increasingly involved in our working lives. This uptake of AI technology is likely to be in the form of virtual assistants – 33% of the UK public believe that this will be the main use for AI in the workplace.

Precision typing on a smartwatch with finger gestures If you wear a smartwatch, you know how limiting it is to type it on or otherwise operate it. Now European researchers have developed an input method that uses a depth camera (similar to the Kinect game controller) to track fingertip touch and location on the back of the hand or in mid-air, allowing for precision control. The researchers have created a prototype called "WatchSense," worn on the user's arm. It would also work with smartphones, smart TVs, and virtual-reality or augmented reality devices, explains Srinath Sridhar, a researcher in the Graphics, Vision and Video group at the Max Planck Institute for Informatics. KurzweilAI has covered a variety of attempts to use depth cameras for controlling devices, but developers have been plagued with the lack of precise control with current camera devices and software. The new software, based on machine learning, recognizes the exact positions of the thumb and index finger in the 3D image from the depth sensor, says Sridhar, identifying specific fingers and dealing with the unevenness of the back of the hand and the fact that fingers can occlude each other when they are moved.

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.

Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback-loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.

In this paper, we study the prediction of a circularly symmetric zero-mean stationary Gaussian process from a window of observations consisting of finitely many samples. This is a prevalent problem in a wide range of applications in communication theory and signal processing. Due to stationarity, when the autocorrelation function or equivalently the power spectral density (PSD) of the process is available, the Minimum Mean Squared Error (MMSE) predictor is readily obtained. In particular, it is given by a linear operator that depends on autocorrelation of the process as well as the noise power in the observed samples. The prediction becomes, however, quite challenging when the PSD of the process is unknown. In this paper, we propose a blind predictor that does not require the a priori knowledge of the PSD of the process and compare its performance with that of an MMSE predictor that has a full knowledge of the PSD. To design such a blind predictor, we use the random spectral representation of a stationary Gaussian process. We apply the well-known atomic-norm minimization technique to the observed samples to obtain a discrete quantization of the underlying random spectrum, which we use to predict the process. Our simulation results show that this estimator has a good performance comparable with that of the MMSE estimator.

Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which to base the predictions remains a challenge, especially when the sample size is small. Incorporating expert knowledge offers a promising alternative to improve a prediction model, but collecting such knowledge is laborious to the expert if the number of candidate features is very large. We introduce a probabilistic model that can incorporate expert feedback about the impact of genomic measurements on the sensitivity of a cancer cell for a given drug. We also present two methods to intelligently collect this feedback from the expert, using experimental design and multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51), expert knowledge decreased the prediction error by 8%. Furthermore, the intelligent approaches can be used to reduce the workload of feedback collection to less than 30% on average compared to a naive approach.

Executives at Burger King are convinced playing video games makes people really, really hungry. So hungry, in fact, that they can't take a few minutes to grab a snack, order a pizza or even look away from the screen. Thankfully for starved players in Madrid, Spain, Burger King and Sony have rolled out a solution to this dining dilemma: Burger Clan. Burger Clan allows PlayStation Network members in Spain to jump into a game with an eSports professional -- folks like FIFA champion Alfonso Ramos Cuevas or Call of Duty player Roberto Abreu -- and between rounds of owning n00bs, they can order Burger King for home delivery directly from these pros. Think of it as a drive-thru system for the living room.

In 1844, a laborer from a small town in southern Italy was put on trial for stealing "five ricottas, a hard cheese, two loaves of bread […] and two kid goats". The laborer, Giuseppe Villella, was reportedly convicted of being a brigante (bandit), at a time when brigandage -- banditry and state insurrection -- was seen as endemic. Villella died in prison in Pavia, northern Italy, in 1864. Villella's death led to the birth of modern criminology. Nearby lived a scientist and surgeon named Cesare Lombroso, who believed that brigantes were a primitive type of people, prone to crime.

The UK's National Health Service (NHS) and Intel are working together to make cancer detection more efficient through artificial intelligence. Last week, the University of Warwick, University Hospitals Coventry & Warwickshire NHS Trust (UHCW) alongside Intel said a new collaboration between the groups will push forward the classification of cancer cells "more efficiently and accurately through ground-breaking artificial intelligence." A team of scientists, hosted by the University of Warwick's Tissue Image Analytics (TIA) laboratory and led by Professor Nasir Rajpoot are currently creating a digital repository of known tumor and immune cells based on thousands of human tissue cells. This database of cancer information will then be used by algorithms to recognize these cells automatically. While some types of cancer are more aggressive than others, time is almost always an issue.

How To Create a Website in a Weekend! (Project-Centered Course) State University of New York via Coursera (3 ratings) 8th May, 2017