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.

The global artificial intelligence and education Market is significantly driven by the integration of intelligent algorithms as well as Advanced Technologies in to e-learning platforms. Education software, machine learning, and artificial intelligence are some of the Innovative learning models and Technologies change the rules and creating tremendous shift from the teaching methods. These technologies have completely transformed with a classroom. The sophistication level has increased tremendously with the increasing adoption of artificial intelligence and machine learning algorithms. These Technologies are becoming extremely useful for developing user-friendly decision support systems and used in knowledge acquisition applications, language translation, and information retrieval.

Fintech startups and banks have always been at the forefront of tech adoption, and they've been curiously following the growth and development of AI for many years. And there's a good reason for it -- we, the consumers of their services, want to have access to cutting-edge technology while dealing with our finances, as well as making sure that the companies dealing with our savings be equipped with the best of what tech can offer. AI and ML have recently moved from the realm of futurism to the very crux of the conversation in the Fintech sector, and many aspiring businesses have started integrating it into their services. In this article, we wanted to touch on the ways various Fintech businesses and startups implement this technology in the services they provide their customers with and how it benefits their users. Let's dive right in, shall we?

The latest market analysis report on the Deep Learning market performs industry diagnostic as a way to accumulate valuable data into the business environment of the Deep Learning market for the forecast period 2019 – 2026. The subject matter experts behind the research have collected vital statistics on the market share, size and growth as a way to help stakeholders, business owners and field marketing personnel identify the areas to reduce costs, improve sales, explore new opportunities and streamline their processes. Unbiased perspective on intangible aspects such as key challenges, threats, new entrants as well as strengths and weaknesses of the prominent vendors too are discussed in this market intelligence report. By offering expert assistance, it would be able to assist humans in extending their capabilities. Organizations are using deep learning networks to get valuable insights from huge amount of data.

Sequential decision making is a typical problem in reinforcement learning with plenty of algorithms to solve it. However, only a few of them can work effectively with a very small number of observations. In this report, we introduce the progress to learn the policy for Malaria Control as a Reinforcement Learning problem in the KDD Cup Challenge 2019 and propose diverse solutions to deal with the limited observations problem. We apply the Genetic Algorithm, Bayesian Optimization, Q-learning with sequence breaking to find the optimal policy for five years in a row with only 20 episodes/100 evaluations. We evaluate those algorithms and compare their performance with Random Search as a baseline. Among these algorithms, Q-Learning with sequence breaking has been submitted to the challenge and got ranked 7th in KDD Cup.