As it becomes easier for humans to do the mundane with the advent of artificial intelligence (AI), the ability of human kind to process complex emotions will become imperative. While the evolution of AI and machine learning--and how they will change our lifestyle, the markets and workforce in many sectors--has been staring in our face, it is important to know the following facts. In the last decade, data and analytics have turned baseball into a bonanza of home runs and strikeouts, basketball became a game won or lost behind the arc and even prompted football coaches to start going for it on fourth down. Microsoft's belief that artificial intelligence (AI) will continue to become relevant in the coming years has often been indicated through moves the tech giant has made in the recent past. Earlier this month, the firm partnered up with Novartis to transform the field of medicine using AI.

Every year, municipalities across South Africa lose millions of Rands from electricity theft. My work as an electrical engineer at Aurecon has led me to think deeply about coming up with ways to not only help solve this problem but consider possible preventative measures that could be put into place. Municipalities generate an enormous amount of data related to electricity distribution and consumption. When combined with real-time data analysis and machine learning algorithms, this information can be used to pick up on electricity theft at any node in the grid. As part of my Research interests, I started to create an algorithm that uses machine learning and artificial neural intelligence to detect electricity theft as well as cable theft, together with one of the Junior Electrical Engineers Tendai Matiza.

Trilobites of the species Ampx priscus were caught in an avalanche of sediment 480 million years ago as they marched in a single-file line on the seafloor of what is now Morocco. The trilobites go marching one by one, hurrah, hurrah … well, at least they did, some 480 million years ago. New fossils from Morocco show lines of trilobites in orderly queues, likely buried by a storm as they trekked from one place to another under the Ordovician seas in an ancient game of "follow the leader." "I think people think that collective behavior is something new in the course of evolution, but actually sophisticated behavior started very, very early," said study leader Jean Vannier, a paleontologist at the University of Lyon in France. Vannier and colleagues from Marrakech, Morocco, discovered the trilobites in the southern part of Morocco in an area known for well-preserved fossils of animals from the early Ordovician, a geologic period that began about 485 million years ago and is one of six periods that make up the Paleozoic era.

If you look at the past four years we've been enjoying substantial economic growth, in order for the economy to be resilient to external shocks we have to continue to diversify and explore new niches to sustain our economic growth. So for this reason we have delved into niche economic areas. We started 2 years ago with Blockchain and we've been attracting significant investment to our island, not only in terms of crypto but also in other areas of technological developments. Now we're seeing new development in companies that are investing in technology and coming here to work and operate from Malta. We're also seeing a spill-over effect, such as companies from the iGaming industry who are producing new products supported by blockchain technology.

Abu Dhabi on Wednesday announced the establishment of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the first graduate level, research-based AI university in the world. MBZUAI will enable graduate students, businesses, and governments to advance artificial intelligence, a statement said. The University is named after Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, who has long advocated for the UAE's development of human capital through knowledge and scientific thinking to take the nation into the future, it added. MBZUAI will provide all admitted students with a full scholarship, plus benefits such as a monthly allowance, health insurance, and accommodation. The university will also work with leading local and global companies to secure internships, and will also assist students in finding employment opportunities.

ABSTRACT Google's multilingual speech recognition system combines low-level acoustic signals with language-specific recognizer signals to better predict the language of an utterance. This paper presents our experience with different signal combination methods to improve overall language identification accuracy. We compare the performance of a lattice-based ensemble model and a deep neural network model to combine signals from recognizers with that of a baseline that only uses low-level acoustic signals. Experimental results show that the deep neural network model outperforms the lattice-based ensemble model, and it reduced the error rate from 5 .5% in the baseline to 4 .3%, Index T erms-- Signal combination, language identification, lattice regression, deep neural network 1. INTRODUCTION Multilingual speech recognition is an important feature for modern speech recognition systems allowing users to speak in more than a single, preset language.

We consider the problem of tensor-response regression given covariates on multiple modes. Such data problems arise frequently in applications such as neuroimaging, network analysis, and spatial-temporal modeling. We propose a new family of tensor response regression models that incorporate covariates, and establish the theoretical accuracy guarantees. Unlike earlier methods, our estimation allows high-dimensionality in both the tensor response and the covariate matrices on multiple modes. An efficient alternating updating algorithm is further developed. Our proposal handles a broad range of data types, including continuous, count, and binary observations. Through simulation and applications to two real datasets, we demonstrate the outperformance of our approach over the state-of-art.

Deep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensionality reduction scale. According to the constant-Q transform, good features can be extracted from audio signals if the low frequency bands are processed with high frequency resolution filters and the high frequency bands with high time resolution filters. In the spectrogram of a mixture of singing voices and music signals, there is usually more information about the voice in the low frequency bands than the high frequency bands. These raise the need for processing each part of the spectrogram differently. In this paper, we propose a multi-band multi-resolution fully convolutional neural network (MBR-FCN) for singing voice separation. The MBR-FCN processes the frequency bands that have more information about the target signals with more filters and smaller dimentionality reduction scale than the bands with less information. Furthermore, the MBR-FCN processes the low frequency bands with high frequency resolution filters and the high frequency bands with high time resolution filters. Our experimental results show that the proposed MBR-FCN with very few parameters achieves better singing voice separation performance than other deep neural networks.

Global Artificial Intelligence (AI) in Automotive Market has valued 566.80 Mn in 2016 and is estimated to reach US$ 10,600.3 Global Artificial Intelligence (AI) in Automotive Market is segmented by technology, offering, process, application, and geography. By technology, Global Artificial Intelligence (AI) in the automotive market is divided into Computer Vision, Machine Learning, Context Awareness, natural language processing. Based on the offering, Artificial Intelligence (AI) in Automotive Market is categorized hardware and software. By process, the market is fragmented into Data Mining, Signal Recognition, and Image Recognition.

Armed violence is on the rise and we don't know how to stop it1. Since 2011, conflicts worldwide have killed up to 100,000 people a year, three-quarters of whom were in Afghanistan, Iraq and Syria. The rate of major wars has decreased over the past few decades. But the number of civil conflicts has doubled since the 1960s, and terrorist attacks have become more frequent in the past ten years. The nature of conflict is changing.