With artificial intelligence (AI) being well established within the daily lives of research communities, we turn our gaze toward an application area that appears intuitively unsuited for probabilistic decision-making: the area of formal methods (FM). FM aim to provide sound and understandable reasoning about problems in computer science, which seemingly collides with the black-box nature that inhibits many AI approaches. However, many researchers have crossed this gap and applied AI techniques to enhance FM approaches. As this dichotomy of FM and AI sparked our interest, we conducted a systematic mapping study to map the current landscape of research publications. In this study, we investigate the previous five years of applied AI to FM (2019-2023), as these correspond to periods of high activity. This investigation results in 189 entries, which we explore in more detail to find current trends, highlight research gaps, and give suggestions for future research.

The benefits and capabilities of pre-trained language models (LLMs) in current and future innovations are vital to any society. However, introducing and using LLMs comes with biases and discrimination, resulting in concerns about equality, diversity and fairness, and must be addressed. While understanding and acknowledging bias in LLMs and developing mitigation strategies are crucial, the generalised assumptions towards societal needs can result in disadvantages towards under-represented societies and indigenous populations. Furthermore, the ongoing changes to actual and proposed amendments to regulations and laws worldwide also impact research capabilities in tackling the bias problem. This research presents a comprehensive survey synthesising the current trends and limitations in techniques used for identifying and mitigating bias in LLMs, where the overview of methods for tackling bias are grouped into metrics, benchmark datasets, and mitigation strategies. The importance and novelty of this survey are that it explores the perspective of under-represented societies. We argue that current practices tackling the bias problem cannot simply be 'plugged in' to address the needs of under-represented societies. We use examples from New Zealand to present requirements for adopting existing techniques to under-represented societies.

People have been afraid that AI will make humans obsolete since its introduction to the workforce. We began to see AI take over jobs and cause layoffs in certain industries like the automotive industry. Although this only fuelled the anti-AI firestorm, it may have been a mistake. According to current trends, AI seems more likely to create jobs than take over. We keep you informed about current trends in the job marketplace.

Despite the ongoing disruption from COVID-19, many impressive breakthroughs in science and technology occurred this year. Below we have listed our top 20 most viewed blogs of 2021, in reverse order. In June, researchers from Google reported a new machine learning technique for microchip floorplanning that can outperform human experts. In November, the world's first electric and self-piloting container ship – Yara Birkeland – undertook its maiden voyage in the Oslo Fjord. This will cut 1,000 tonnes of CO2 and replace 40,000 trips by diesel-powered trucks a year.

AI technology is radically changing the outlook for the ecommerce sector in a number of very promising ways.

Ontology, as a discipline of philosophy, explains the nature of existence and has its roots in Aristotle and Plato studies on "metaphysics" (Welty and Guarino, 2001). However, the word ontology originated from two Greek words: ontos (being) and logos (word), and conceived for the first time during the Sixteen century by German philosophers (Welty and Guarino, 2001). From then till the mid-twentieth, ontology evolved mainly as a branch of philosophy. However, with the advent of Artificial Intelligence since the 1950s, researchers perceived the necessity of ontology to describe a new world of intelligent systems (Welty and Guarino, 2001). Moreover, with the development of the World Wide Web in the 1990s, ontology development got to be common among different domain specialists to define and share the concepts and entities in their fields on the Internet (Noy et al., 2001). During the last three decades, ontology development studies have evolved and shifted from theoretical issues of ontology to practical implications of the use of ontology in real-world, large-scale applications (Noy et al., 2001). Nowadays, ontology development focuses mainly on defining machine interpretable concepts and their relationships in a domain. However, ontology development also pursues other goals, such as providing a common conceptualization of the domain on which different experts agree, (Métral and Cutting-Decelle, 2011) and enable them to reuse the domain knowledge (Noy et al., 2001). It also enables researchers to easily analyze the domain knowledge and eloquently express the domain assumptions.

Indeed, most people working in healthcare believe it is a sector that will benefit most from AI technologies. In addition to innovations such as AI-assisted robotic surgery, cloud technology is already being used to back up documents and photos. However, AI data analysis comes with its own challenges, due to ethical concerns regarding a huge number of legacy systems containing highly sensitive data. Despite this, healthcare industry trends prove that AI has a valuable role to play. It can improve quality of care, reduce costs and speed up procedures.

Given that we are currently in the age of digital transformation, technological trends stand out among all others. The digital mesh will consist of automated devices, robots, humans, services and content, all driven by disruptive technological trends. We are here to discuss the top trends of IT in 2019 that are going to shape the future of business operations for the rest of the year. Artificial intelligence (AI) essentially involves harnessing the power of algorithms and machine learning to teach machines to identify, comprehend and mimic human behavior. Though it has been around for quite some time, this year we have witnessed AI combined with machine learning (ML) entering into the business platform to enable smart business operations.

The global insurance market is in the midst of a game-changing course correction that will re-define'business as usual.' A'digital first' urgency is sweeping across the landscape, driven by a new generation of consumers, data, automation and Artificial Intelligence (AI). Let's take a look at the current trends that are shaping the insurance industry and how digital technologies are driving irreversible change. The digital economy will make usage-based, on-demand and'all-in-one' insurance lifestyle products more relevant. Customers will prefer personalized insurance covers instead of the one-size-fits-all products currently available.

Artificial intelligence (AI) in the fintech industry is not about replacing live employees with robots. Instead, it's about using automation to carry out basic or routine tasks in order to let employees handle more complex issues. It's a way of giving employees more responsibility and the chance to work closer with the customers who truly need live help. AI is also ensuring that each transaction is accurate and it's making online transactions safer by automating regulatory compliance. When basic customer tasks are automated, such as simplistic banking transactions like depositing money, checking account balances or cashing checks, employees can have the time and mental energy to handle high-value tasks and troubleshoot difficult problems.