ITRS Group help enterprises run their IT estates efficiently, prevent outages and optimise costs. Since its inception, the Internet of Things (IoT) has grown at a steady pace – but, finally, it is positioned to break into the mainstream. Demonstrating this growth, a quarter of businesses now use IoT technology, compared to just 13% in 2014. And this expansion is only set to continue, with IoT underpinning an increasing host of new technologies, including driverless cars and smart homes. However, as IoT continues to proliferate, security becomes a crucial concern – with a number of high-profile cyberattacks demonstrating the vulnerability of IoT.

Google's claim to "organize the world's information and make it universally accessible and useful" has earned it an aura of objectivity. Its dominance in search, and the disappearance of most competitors, make its lists of links appear still more canonical. An experimental new interface for Google Search aims to remove that mantle of neutrality. Search Atlas makes it easy to see how Google offers different responses to the same query on versions of its search engine offered in different parts of the world. The research project reveals how Google's service can reflect or amplify cultural differences or government preferences--such as whether Beijing's Tiananmen Square should be seen first as a sunny tourist attraction or the site of a lethal military crackdown on protesters.

The Pentagon yesterday announced it was scuttling its long-doomed "Project JEDI," a cloud-services AI contract that was awarded to Microsoft in 2019. Up front: Project JEDI is a big deal. The US military needs a reliable cloud-service platform from which to operate its massive AI infrastructure. Unfortunately the project was mishandled from the very beginning. Today, the Department of Defense (DoD) canceled the Joint Enterprise Defense Infrastructure (JEDI) Cloud solicitation and initiated contract termination procedures.

Machine Learning is everywhere these days. We live in a world where Machine Learning and Artificial Intelligence is not obscure mathematical and science fiction anymore they have become crucial part of our lives. Netflix, Amazon, Siri, Pandora, Google, Prisma the list goes on and on and it's not just entertainment and media, It's even the post office to healthcare and traffic to security. Close analysis suggests that virtually every moment of our lives we are touched by Machine Learning at some point.

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

The improvement of computers' capacities, advancements in algorithmic techniques, and the significant increase of available data have enabled the recent developments of Artificial Intelligence (AI) technology. One of its branches, called Machine Learning (ML), has shown strong capacities in mimicking characteristics attributed to human intelligence, such as vision, speech, and problem-solving. However, as previous technological revolutions suggest, their most significant impacts could be mostly expected on other sectors that were not traditional users of that technology. The agricultural sector is vital for African economies; improving yields, mitigating losses, and effective management of natural resources are crucial in a climate change era. Machine Learning is a technology with an added value in making predictions, hence the potential to reduce uncertainties and risk across sectors, in this case, the agricultural sector. The purpose of this paper is to contextualize and discuss barriers to ML-based solutions for African agriculture. In the second section, we provided an overview of ML technology from a historical and technical perspective and its main driving force. In the third section, we provided a brief review of the current use of ML in agriculture. Finally, in section 4, we discuss ML growing interest in Africa and the potential barriers to creating and using ML-based solutions in the agricultural sector.

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

The cybersecurity industry is rapidly embracing the notion of "zero trust", where architectures, policies, and processes are guided by the principle that no one and nothing should be trusted. However, in the same breath, the cybersecurity industry is incorporating a growing number of AI-driven security solutions that rely on some type of trusted "ground truth" as reference point. This is not a hypothetical discussion. Organizations are introducing AI models into their security practices that impact almost every aspect of their business, and one of the most urgent questions remains whether regulators, compliance officers, security professionals, and employees will be able to trust these security models at all. Because AI models are sophisticated, obscure, automated, and oftentimes evolving, it is difficult to establish trust in an AI-dominant environment.

After dabbling in machine learning during my undergrad, I scored a job as a Business Analyst catering to the Public Sector and Education business of one of the leading AI service providers in India. Having landed the job in the pandemic -- which I must say, I consider myself fortunate to -- and onboarded onto the role fully online, I have been at the forefront of the transition of organizations from real-world to digital world. And I say this not only for the company I work for, but for the numerous governmental agencies and educational institutions that have approached us seeking to undergo a digital transformation in order to enhance their processes in the current situation as well as for the future. I genuinely believe that these past one-and-a-half years have been a blessing for the entire IT industry and in association to the AI industry -- since AI is now nearly ubiquitous when it comes to IT -- as evidenced by the incredulous growth the Big Tech has achieved by a two-prong strategy of undergoing digital transformation themselves (thereby cutting expenditures) and providing the same services to other industries (and generating immense revenue). As previously mentioned, Big Tech companies, while initially apprehensive of the impact of the'new normal', soon realized its potential and took full advantage of it. Everything increased -- from ad revenues due to digital ads becoming the major avenue to reach consumers, online shopping, sales of laptops and mobiles as everything went online, social media engagement to increased cloud consumption as businesses went digital.

Ghebreab has spent the last decade imparting that emphasis on interdisciplinarity to his students. 'Sometime around 2010, I decided to stop putting my energy into my own research and to invest it in the next generation instead. Questions I have dealt with in my teaching include: how does the brain process information? Where do we see pattern recognition reflected? And how does AI cope with pattern recognition and bias?