Artificial Intelligence has influenced every aspect of our daily lives. Nowadays,thousands of tech companies have developed state-of-the-art AI-powered cybersecurity defense solutions specifically designed and programmed by ethical hackers and penetration testers. The Artificial Intelligence used in such solutions helps prevent cyberattacks from even happening by predicting the potential risks. Every tech app or service we use contains at least some type of Artificial Intelligence or smart learning technology, at least in most cases. It is now of a high possibility of connecting almost every electrical device to the internet to create our own personalized smart environments, all thanks to the recently announced 5th generation speed networks and rapid advancement in machine learning.

Traditional colonial power seeks unilateral power and domination over colonised people. It declares control of the social, economic, and political sphere by reordering and reinventing the social order in a manner that benefits it. In the age of algorithms, this control and domination occurs not through brute physical force but rather through invisible and nuanced mechanisms such as control of digital ecosystems and infrastructure. Common to both traditional and algorithmic colonialism is the desire to dominate, monitor, and influence the social, political, and cultural discourse through the control of core communication and infrastructure mediums. While traditional colonialism is often spearheaded by political and government forces, digital colonialism is driven by corporate tech monopolies--both of which are in search of wealth accumulation. The line between these forces is fuzzy as they intermesh and depend on one another. Political, economic, and ideological domination in the age of AI takes the form of "technological innovation", "state-of-the-art algorithms", and "AI solutions" to social problems. Algorithmic colonialism, driven by profit maximisation at any cost, assumes that the human soul, behaviour, and action is raw material free for the taking.

The University of California, Davis, has been awarded $20 million as part of a multi-institutional collaboration to establish an institute focused on enabling the next-generation food system through the integration of artificial intelligence, or AI, technologies. The award is part of a larger investment announced by the National Science Foundation, or NSF, in partnership with several federal agencies -- distributing a total of $140 million to fund seven complementary AI research institutes across the nation. The AI Institute for Next Generation Food Systems, or AIFS, aims to meet growing demands in our food supply by increasing efficiencies using AI and bioinformatics spanning the entire system -- from growing crops through consumption. This includes optimizing plant traits for yield, crop quality and disease resistance through advances in molecular breeding, in addition to minimizing resource consumption and waste through development of agriculture-specific AI applications, sensing platforms, and robotics. The team's plan also intends to benefit consumers through enhancements to food safety and development of new tools to provide real-time assessment of meals that can guide personalized health decisions.

The summer recess is over and MPs are returning to Westminster with very full inboxes. As Parliament throws its doors open for the new term, what challenges lie ahead for the government in the coming months? The challenges of posed by the coronavirus pandemic will be forefront of ministers' minds: how to encourage a return to something approaching normal while keeping the virus under control. As the holiday season winds up, many in the Conservative party want to see more done to encourage people back to offices in England - and ministers are urging people working from home to speak to their employers about returning to workplaces where it's safe to do so. Health Committee chair Jeremy Hunt has warned that the situation coming into winter is "potentially very perilous".

Being good is easy, what is difficult is being just. We need to defend the interests of those whom we've never met and never will. Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way with minimal use of mathematics, in the hope that everyone can get something out of reading this. Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories -- indeed, this is their designated purpose in life. This is reflected in the name for these algorithms which are often referred to as discriminative algorithms (splitting data into categories), in contrast to generative algorithms (generating data from a given category). When we use supervised machine learning, this "discrimination" is used as an aid to help us categorize our data into distinct categories within the data distribution, as illustrated below. Whilst this occurs when we apply discriminative algorithms -- such as support vector machines, forms of parametric regression (e.g. For example, using last week's weather data to try and predict the weather tomorrow has no moral valence attached to it.

According to the World Health Organisation (WHO) [World Health Organization, 2013], the United Nations directing and coordinating health authority, public health surveillance is: The continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice. Public health surveillance practice has evolved over time. Although it was limited to pen and paper at the beginning of 20th century, it is now facilitated by huge advances in informatics. Information technology enhancements have changed the traditional approaches of capturing, storing, sharing and analysing of data and resulted efficient and reliable health surveillance techniques [Lombardo and Buckeridge, 2007]. The main objective and challenge of a health surveillance system is the earliest possible detection of a disease outbreak within a society for the purpose of protecting community health. In the past, before the widespread deployment of computers, health surveillance was based on reports received from medical care centres and laboratories.

Test, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence (AI) is a challenge that threatens to limit the economic and societal rewards that AI researchers have devoted themselves to producing. A central task of TEVV for AI is estimating brittleness, where brittleness implies that the system functions well within some bounds and poorly outside of those bounds. This paper argues that neither of those criteria are certain of Deep Neural Networks. First, highly touted AI successes (eg. image classification and speech recognition) are orders of magnitude more failure-prone than are typically certified in critical systems even within design bounds (perfectly in-distribution sampling). Second, performance falls off only gradually as inputs become further Out-Of-Distribution (OOD). Enhanced emphasis is needed on designing systems that are resilient despite failure-prone AI components as well as on evaluating and improving OOD performance in order to get AI to where it can clear the challenging hurdles of TEVV and certification.

Emergency response to incidents such as accidents, medical calls, and fires is one of the most pressing problems faced by communities across the globe. In the last fifty years, researchers have developed statistical, analytical, and algorithmic approaches for designing emergency response management (ERM) systems. In this survey, we present models for incident prediction, resource allocation, and dispatch for emergency incidents. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. Finally, we present future research directions. To the best of our knowledge, our work is the first comprehensive survey that explores the entirety of ERM systems.

Vibraimage is a digital system that quantifies a subject's mental and emotional state by analysing video footage of the movements of their head. Vibraimage is used by police, nuclear power station operators, airport security and psychiatrists in Russia, China, Japan and South Korea, and has been deployed at an Olympic Games, FIFA World Cup, and G7 Summit. Yet there is no reliable evidence that the technology is actually effective; indeed, many claims made about its effects seem unprovable. What exactly does vibraimage measure, and how has it acquired the power to penetrate the highest profile and most sensitive security infrastructure across Russia and Asia? I first trace the development of the emotion recognition industry, before examining attempts by vibraimage's developers and affiliates scientifically to legitimate the technology, concluding that the disciplining power and corporate value of vibraimage is generated through its very opacity, in contrast to increasing demands across the social sciences for transparency. I propose the term 'suspect AI' to describe the growing number of systems like vibraimage that algorithmically classify suspects / non-suspects, yet are themselves deeply suspect. Popularising this term may help resist such technologies' reductivist approaches to 'reading' -- and exerting authority over -- emotion, intentionality and agency.

Smart grid (SG) is a complex cyber-physical system that utilizes modern cyber and physical equipment to run at an optimal operating point. Cyberattacks are the principal threats confronting the usage and advancement of the state-of-the-art systems. The advancement of SG has added a wide range of technologies, equipment, and tools to make the system more reliable, efficient, and cost-effective. Despite attaining these goals, the threat space for the adversarial attacks has also been expanded because of the extensive implementation of the cyber networks. Due to the promising computational and reasoning capability, machine learning (ML) is being used to exploit and defend the cyberattacks in SG by the attackers and system operators, respectively. In this paper, we perform a comprehensive summary of cyberattacks generation, detection, and mitigation schemes by reviewing state-of-the-art research in the SG domain. Additionally, we have summarized the current research in a structured way using tabular format. We also present the shortcomings of the existing works and possible future research direction based on our investigation.