A BSTRACT Since deep neural networks are over-parametrized, they may memorize noisy examples. We address such memorizing issue under the existence of annotation noise. From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we find that noisy examples do not consistently incur small losses on the network in the presence of perturbation. Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) whose goal is to prevent overfitting noisy examples by eliminating them identified via consensus of an ensemble of perturbed networks. One of the proposed LECs, L TEC outperforms the current state-of-the-art methods on MNIST, CIFAR-10, and CIFAR-100 despite its efficient memory usage. 1 I NTRODUCTION Deep neural networks (DNNs) have shown excellent performance (Krizhevsky et al., 2012; He et al., 2016) on visual recognition datasets (Deng et al., 2009). However, it is difficult to obtain annotated datasets of such high quality in practice (Wang et al., 2018a). Even worse, DNNs may not generalize training data in the presence of noisy examples (Zhang et al., 2016). Therefore, there is an increasing demand for robust training methods. In general, DNNs trained on noisy datasets first generalize clean examples (Arpit et al., 2017).

The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. This study aimed to explore psychiatrists' opinions about the potential impact of innovations in artificial intelligence and machine learning on psychiatric practice. In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written response to three open-ended questions in the survey. Comments were classified into four major categories in relation to the impact of future technology on patient-psychiatric interactions, the quality of patient medical care, the profession of psychiatry, and health systems. Overwhelmingly, psychiatrists were skeptical that technology could fully replace human empathy. Many predicted that 'man and machine' would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. This study presents timely information of psychiatrists' view about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

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.