The remarkable progress in Natural Language Processing (NLP) brought about by deep learning, particularly with the recent advent of large pre-trained neural language models, is brought into scrutiny as several studies began to discuss and report potential biases in NLP applications. Bias in NLP is found to originate from latent historical biases encoded by humans into textual data which gets perpetuated or even amplified by NLP algorithm. We present a survey to comprehend bias in large pre-trained language models, analyze the stages at which they occur in these models, and various ways in which these biases could be quantified and mitigated. Considering wide applicability of textual affective computing based downstream tasks in real-world systems such as business, healthcare, education, etc., we give a special emphasis on investigating bias in the context of affect (emotion) i.e., Affective Bias, in large pre-trained language models. We present a summary of various bias evaluation corpora that help to aid future research and discuss challenges in the research on bias in pre-trained language models. We believe that our attempt to draw a comprehensive view of bias in pre-trained language models, and especially the exploration of affective bias will be highly beneficial to researchers interested in this evolving field. The examples provided in this paper may be offensive in nature and may hurt your moral beliefs.

Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction. In large and complex domains, such as graphs, sets, or images, the choice of suitable kernel can also be non-trivial to determine, providing an additional obstacle to the learning task. Over the last decade, these challenges have resulted in significant advances being made in terms of scalability and expressivity, exemplified by, e.g., the use of inducing points and neural network kernel approximations. In this paper, we propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains while also facilitating scalable gradient-based learning methods. We consider both regression and (binary) classification tasks and report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods. We also demonstrate how IGNs can be used to effectively model complex domains using neural network architectures.

Children and young people are growing up in an increasingly digital age, where technology pervades every aspect of their lives. From robotic toys and social media to the classroom and home, artificial intelligence (AI) is a ubiquitous part of daily life. It's vital therefore that ethical guidelines protect them and ensure they get the best from this emerging technology. Generation Z, who have grown up with AI, are uniquely placed to offer an insight into the potential issues of AI targeted at children and help create governance guidelines. With that in mind the World Economic Forum has set up the AI Youth Council, a global diverse group comprising young people interested in AI.

To make decisions more quickly and accurately, enterprises in the Middle East and Africa (MEA) are increasingly turning to Machine Learning, arguably today's most practical application of Artificial Intelligence (AI). How should CIOs and IT leaders ensure success and ROI from Machine Learning deployments in their organisations? Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. In addition, Machine Learning systems apply algorithms to data to glean insights into that data without explicit programming: It's about using data to answer questions.

This article was first published in the Virginia Mercury. Last week when Virginia's new Secretary of Transportation Sheppard Miller publicly declared his belief that flying cars will be a reality within the next 50 years as a reason that leaders across the commonwealth should "reexamine transit," some might have scoffed. But just as flying cars consumed the fantasies of many mid-century Americans, today plenty of people put their faith in another utopian technology replete with endlessly elusive promises of improved safety and unbridled freedom: autonomous vehicles. As is often the case in the United States, the regulation of autonomous vehicles is largely left to the states, resulting in a patchwork of conflicting and confusing policies where some sort of national approach ought to exist. Any state has the right to craft their own legal framework for the emerging technology but few have -- our commonwealth included.

The industry may be emerging from the Covid-19 pandemic, with e-commerce still thriving on it, but the supply chain crisis isn't going away. Infrastructure designed in a predictable pre-pandemic world isn't enough to clear the backlogs, and may even be making a bad situation worse. Could artificial intelligence (AI) be the technology that gets things moving again? Organisations certainly believe so, according to a new 3Gem report for Blue Yonder, which finds that more than half (53%) of UK supply chain decision-makers believe AI advances are key to managing disruption. This confidence in AI owes much to its promise of visibility.

Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use BLE signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, we present a multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 GHz and 5 GHz) and BLE signal strength data, measured in four different environments. We have developed and evaluated a combined classification model based on BLE and IEEE 802.11 signals. Our approach significantly improves the distance classification and consequently also the contact tracing accuracy. We are able to achieve good results with our approach in everyday public transport scenarios. However, in our implementation based on IEEE 802.11 probe requests, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.

Johannesburg, the sprawling megacity once home to Nelson Mandela and Desmond Tutu, is now birthing a uniquely South African surveillance model. In the last five years, the city has become host to a centralized, coordinated, entirely privatized mass surveillance operation. Vumacam, the company building the nationwide CCTV network, already has over 6,600 cameras and counting, more than 5,000 of which are concentrated in Johannesburg. The video footage it takes feeds into security rooms around the country, which then use all manner of AI tools like license plate recognition to track population movement and trace individuals. These tools have been enthusiastically adopted by the local security industry, grappling with the pressures of a high-crime environment.

Five years ago, this wouldn't have been possible. Neither the city's infrastructure nor existing video analytics could support sending and processing footage at the necessary scale. But then fiber coverage expanded, AI capabilities advanced, and companies abroad, seeing an opportunity, began dumping the latest surveillance technologies into the country. The local security industry, forged under the pressures of a high-crime environment, embraced the menu of options. The effect has been the rapid creation of a centralized, coordinated, entirely privatized mass surveillance operation.

It has been estimated that 1.7 million people die from Tuberculosis (TB), and more than 10.4 million new cases are reported every year worldwide. The global'End TB' strategy aims to eliminate the disease by 2030. However, realizing this goal would be challenging if there were to be a gap in treatment adherence to prescribed medication. In the context of TB and HIV coinfection, non-adherence to the medication has been associated with the incidence of drug resistance, prolonged infection, unsuccessful treatments, and death. Africa experiences a severe shortage of healthcare workers, making delivering proper healthcare difficult.