Artificial intelligence (AI) is the concept and design of computer programs that can perform duties that initially needed human intelligence, such as critical decision-making, medical diagnosis, virtual assistance, fraud detection, and facial recognition. To most people, AI is an incomprehensible and uninterpretable innovation that collects or gathers billions of inputs and provides a response that individuals need to accept and implement in various areas of their lives. The perversity of the technology is felt in almost all areas of society, including the construction sector, e-commerce realm, and automotive industry. Companies like Tesla have developed self-driving cars that use AI to sense obstacles, make quick decisions, plan routes, and communicate with individuals through natural language processing (NLP).

In the States of Fragility Report of 2022, the Organization for Economic Co-operation and Development (OECD) seeks to incorporate the sixth component of fragility into its multidimensional fragility structure – the human dimension – to assess factors influencing the achievement of people’s potential and well-being. For the OECD, fragility is a fusion of exposure to risks and inadequate coping strategies of the government, communities, and systems to handle, mitigate, or absorb those threats (Understanding Fragility, 2022). Fragility can cause negative implications, encompassing inequality, poverty, violence, political and environmental degradation, and displacement. The human dimension addresses risks and coping mechanisms affecting people’s well-being and capacity to lead long, healthy, and prosperous lives.

FHIR (Fast Healthcare Interoperability Resources) provides a set of guidelines for the security and structure of data exchanges between healthcare providers and systems. It would do any worker in the healthcare industry well to have a comprehensive understanding of the relevance of interoperability. "Interoperability" refers to the capacity of different information systems and software programmes to collaborate and share data with one another. Interoperability has been difficult to achieve in the healthcare industry due to the large number of complex systems that are being developed, utilised, and networked together.

Envisaging and averting epidemics is paramount, given the intensifying health threats worldwide. Outbreaks of various illnesses remain one of the primary concerns regarding public health. Even with the existing modern technologies and innovative approaches, the world still has to deal with high rates of epidemics. For instance, during the COVID-19 outbreak, every nation felt the adverse impact. The coronavirus outbreak struck when the world was unprepared to stop its widespread. In this regard, it had an unforgettable impact as many people lost their lives, and the global economy remained crippled. The existing mechanisms aimed at identifying such threats proved ineffective as there was a delay in sharing accurate information to curb the virus before it spread to almost every part of the world.

The evolution of artificial intelligence (AI) has prompted genuine societal, legal, and ethical concerns over its mainstream deployment and adoption. Among the possible angles of contention, AI’s fairness and representation are conspicuous in many discourses. As technology permeates various sectors, ensuring that it is understandable and transparent is a technical challenge and a moral imperative. The development and use of AI can cause unintentional harm through a flawed data modeling algorithmic structure. Several case scenarios illustrate the value of inclusivity in technology development teams. The research shows that training and educating employees on the ethical necessity of fair models is also essential in building useful models. 

Data deserts are among the most impactful issues affecting modern healthcare, which is overly reliant on data and artificial intelligence (AI). Data deserts and the underlying disparities are observable in electronic health records (EHRs), patient-generated health data (PGHD), prescription drug monitoring, public health, and health research. This report examines these data deserts and the underlying implications on affected communities to frame potential solutions. The compiled information suggests that information technology (IT) can be leveraged to solve the data divide.

Artificial Intelligence has a limitless potential that can be utilized in diagnosis, patient care, and treatments. The changes brought by this artificial technology have already been termed unheralded, with some people even suggesting the need for legal interventions to regulate the advancements and a society ill-prepared to deal with overwhelming changes. For gains to be realized in healthcare through AI, community organizations have to be involved.

Innovative digital solutions have transformed the healthcare sector by supporting provider collaboration, reducing medical errors, enabling telemedicine, and facilitating informed decision-making. Currently, interconnected health information management systems help streamline care coordination and ease communication across multi-disciplinary teams. Technology empowers patients and primary caregivers through timely access to crucial health information and educational programs promoting treatment or medication adherence. Despite these advances, human health issues remain complex and multifaceted with rapidly evolving needs. Health facilities collect and process pertinent data from diverse sources, including electronic health records (EHR), wearable devices, payment processors, public exchange marketplaces, laboratory results, clinical observations, and medical imaging.

The AI systems gather farmers and produce data to streamline precision agriculture and resource distribution. However, the biased systems aggravate the lack of trust between the farmers and the other actors in the food system, mainly because the farmers have little to no control over the usage of collected data. Therefore, this ambiguity in the supply chain results in disruptions, inequity in resource distribution, and preference for specific regions, exacerbating economic disparities.

Speed, cost-efficiency, and agility are central to the effective operation of an information technology (IT) infrastructure. The emergence of cloud computing has created new opportunities for enhancing the operational efficiency of IT infrastructure. Firms specialized in using Microsoft technologies to develop powerful solutions in the Azure cloud or on-premises are focusing more on capitalizing microservices by deploying the Azure Service Fabric. Microsoft Azure Service Fabric was invented to simplify the development, deployment, and management of containers and microservices. The Azure ecosystem helps IT managers create and manage applications that can be quickly deployed in the cloud to achieve various purposes. As such, an understanding of Azure Service Fabric is essential for IT managers considering shifting into microservices architecture or seeking to capitalize on Azure’s modern application features.

The world is in the information age, where sensors dominate life, industrial, and business activities. The sensors collect large volumes of data that can be analyzed for various benefits at different levels. For example, businesses can harness Big Data to enable relevant digital transformations and gain a competitive advantage. Big Data-based analytics is becoming increasingly dominant with the rapid progress and proliferating uptake of artificial intelligence (AI) technologies. AI systems, such as language models and generative AI, are particularly important for businesses, helping reinvent how workers spend their time and the amount of insights that can be obtained from data.

 When a patient has a complicated case, a physician may seek another or even a third opinion. You are a doctor and you want to get AI advice for a patient's medical record. You use more than one AI model to help you. This helps you avoid mistakes or biases that one LLM /model might have. You are using the combined intelligence of many models. This is like machine learning "Ensemble" models. Each model gives you their result/opinion like a doctor asking other doctors for their opinions and then making a final determination. Autogen allows the leveraging of multiple agents (LLMs, APIs or even a human) working together for a common outcome.

AI’s prevalent and profound benefits are evident from its continued adoption into society and everyday lives. Individuals and organizations increasingly leverage AI technologies and AI-powered systems for tailored recommendations of products and services, content creation, prediction of future purchase habits, and informed decision-making. One of the most common adoptions of AI systems is Netflix’s recommendations on shows and movies to watch based on the audience’s preferences. Besides recommendations, scientists have employed the technologies to create groundbreaking and simultaneously controversial synthetic realities.

In recent years, artificial intelligence (AI) has rapidly evolved from a futuristic concept to one of the most transformative technologies. Today, AI is a core technology in various applications such as navigation systems, recommendation algorithms, text generation, image recognition, and others. Regardless of these developments, the widespread adoption of AI with limited regulations has raised concerns regarding their ethical implications and societal impacts. Issues ranging from algorithm bias, social injustices, privacy, and discrimination have been identified in AI systems. Furthermore, AI still remains a black box whereby there is limited understanding of the internal workings of AI systems. Considering these issues, there is a growing effort to prevent AI systems from reinforcing or exacerbating unethical vices. Therefore, the goal of this paper is to explore the key ethical issues of AI and their impacts on society. Furthermore, the paper examines the existing AI guidelines before making recommendations to mitigate the ethical risks of AI.

Microsoft announced today that it will deepen its partnership with OpenAI, the firm behind artificial intelligence systems like ChatGPT, DALL-E 2, and GPT-3 that produce art and text, by investing "several billions of dollars" over the course of "many years." According to OpenAI, it would use the freshly acquired funding to carry out independent research and create "powerful, useful, and safe" artificial intelligence.

Continuous technological advancements are affecting the design and development of automation tools to lower the burden on software engineers and developers. Automation makes the software development process more efficient in terms of cost and time because resource allocation depends on technical consideration, creative thinking, and effort. Partial or full-scale automation adoption decreases the time dedicated to repeated, menial tasks, ultimately reducing operational costs.

There are many benefits to having policies and procedures when using computers and information systems. Having rules and procedures to guide the use of computers ensure all state are aware of their obligations about choosing what type of computer systems to use or safety mechanisms to embrace. Information technology rules and procedures have proven to be critical in enhancing the performance of computers and protecting users from malicious internet users.

The current paper presents profound research on the possible ways of improving IT governance in an organization setting. It introduces the concepts behind the application of IT governance practices and its contributions to the achievement of a company’s shared objectives. It reviews the existing research on the different approaches that can be used to improve IT governance. This material touches on the five IT governance domains, namely, strategic alignment; value delivery; risk management; resource management; and performance management.

The modern business environment depends significantly on technological innovation to create a competitive advantage and exploit novel opportunities. Emerging artificial intelligence (AI) and machine learning (ML) solutions have rapidly revolutionized and advanced critical industrial sectors such as healthcare, education, manufacturing, transportation, and agriculture. AI offers diverse tools that solve problems, streamline processes, create value for organizations, and automate routine tasks.

AI is driving the growth of the NLP market. ChatGPT and Mistral are some NLP models available in the market. Microsoft joined forces with Mistral LLM to enhance its AI research and innovation. Mistral has NLP and machine learning that will support Microsoft in this market. But it has to compete with ChatGPT's performance. These two NLP models have similar goals and target the same market, but they differ in language models, strengths, weaknesses, and prices