This literature review studies the field of automated process extraction, i.e., transforming textual descriptions into structured processes using Natural Language Processing (NLP). We found that Machine Learning (ML) / Deep Learning (DL) methods are being increasingly used for the NLP component. In some cases, they were chosen for their suitability towards process extraction, and results show that they can outperform classic rule-based methods. We also found a paucity of gold-standard, scalable annotated datasets, which currently hinders objective evaluations as well as the training or fine-tuning of ML / DL methods. Finally, we discuss preliminary work on the application of LLMs for automated process extraction, as well as promising developments in this field.

Artificial Intelligence is revolutionizing the way military INCE the early days of cyber space technology strides in enhancing its strategic capabilities. Today, we and government organizations operate. These advanced find ourselves at the precipice of a new technological technologies enable machines to learn and reason revolution: Artificial Intelligence (AI). As a strategic autonomously, with applications ranging from situational imperative for national security, AI presents unparalleled awareness to decision-making support. In particular, the opportunities for strengthening our defense capabilities, advent of Large Language Models (LLMs) has significantly similar to how space and cyberspace technology transformed impacted the field of natural language processing, providing our approach to warfare and reconnaissance.

We still do not live in an Age where Equality, Diversity and Inclusion (EDI) is by default. Instead, bias and discrimination are part of the everyday. Moreover, inequality in income and wealth, which transfers into inequality in opportunity, is rising according to the 2019 UN Global Sustainable Development Report. Sadly, EDI manifests not just in what we see, hear and experience, but also in the judgments made against us. Moreover, this is not confined to the people who are responsible for creating negative experiences.

Approximately two weeks ago, Google engineer Blake Lemoine, claimed that reverberated throughout the global AI community: Google's chatbot, LaMDA, had achieved a degree of sentience akin to that of a human child. Google responded by promptly suspending the engineer, leading many members of the public to speculate as to whether the claim was true. Unfortunately, to refer to any entity as sentient requires an operationalized definition of the term that is applicable universally. Moreover, we would also need to generate a discrete, empirically motivated, theoretical framework that adequately disseminates the "Hard Problem" of consciousness (i.e., the idea that there is a set of fundamental attributes that give rise to our capacity for lived experience), which philosophers, psychologists, and neuroscientists have yet to answer. On the other hand, throughout the history of AI, the Turing Test has been popularized as the method of choice for the ascription of sentience to computational agents.

We all started to realize that the rapid development of AI was really going to change the world we live in. AI is no longer just a branch of computer science, it has escaped from research labs with the development of "AI systems", "software that, for human-defined purposes, generates content, predictions, recommendations or decisions influencing the environments with which they interact" (european union definition). The issues of governance of these AI systems – with all the nuances of ethics, control, regulation and regulation – have become crucial, as their development today is in the hands of a few digital empires like them Gafa-Natu-Batx… who have become the masters of real societal choices on automation and on the "rationalization" of the world. The complex fabric intersecting AI, ethics and law is then built in power relations – and connivance – between states and tech giants. But the commitment of citizens becomes necessary, to assert other imperatives than a solutionism technology where "everything that can be connected will be connected and streamlined".

"I'm running out of time, but I'm also not gonna be like, 'I'm having a baby for the sake of having a baby,'" said the younger of the two. "One thing I would recommend," replied the older woman, "if it's an option: freeze your eggs." As a woman, you get to a certain age and babies – hypothetical, expected, realised – suddenly seem ubiquitous: in friendship circles, on social media, in targeted advertising for pregnancy tests and public health messages. But for women of my generation, the decision whether to have children feels more existentially fraught and morally complex than ever before. I have always wanted kids. I have always felt an uncomplicated joy at the chubbiness of babies' limbs and the infectiousness of a child's laughter.

Come 2022, Intelligent Automation will continue to unlock opportunities and improve business operations, according to Blue Prism. Blue Prism has released its annual list of trends to watch nwxt year in the field of Intelligent Automation. Experts at Blue Prism believe that many firms are continuing to adapt and change to new ways of working because of the pandemic and that IA maximises the workforces potential if positioned as a strategic imperative, not a tactical fix. "2022 will see further convergence between previously disparate areas of automation including basic and advanced forms of process automation (RPA, iBPMS, Low Code Workflow tools), Intelligent Document Processing, Rules-based and Artificial Intelligence (AI)- based Decision engines and others," says Dan Ternes, chief technology officer, APJ, Blue Prism. "Organisations will continue to look for easier integration while finding the sweet spot across these technologies to support greater efficiencies and improve business operations," he says.

Edge computing is a networked information technology (IT) design in which customer data is processed as near to the original source as feasible at the network's edge. Modern businesses rely on data to provide significant business insight and real-time management over essential business operations and processes. Large volumes of data may be routinely acquired from sensors and IoT devices running in real-time from remote places and hostile working environments virtually anywhere in the globe, and today's organisations are drowned in a sea of information. It's all about the location when it comes to edge computing. Data is created at a client terminal, including a user's computer, in traditional corporate computing.

As international AI regulation starts to take shape, organisations must educate themselves on lessons from the past. One thing is clear: implementing AI and automated decision-making should not mean imposing biased decisions on the public. While the ethical imperative for this principle is (hopefully) obvious, the legal and regulatory imperative is gathering pace. In April 2021, the EU put forward "the first ever legal framework on AI". It warns against bias in AI systems, and argues that firms must do their bit to monitor and prevent it.

As I've written in my "On AI Ethics," series, machine learning models that aim to mirror and predict real-life as closely as possible are not without their challenges. Household name brands like Amazon, Apple, Facebook, Google have been accused of algorithmic bias that have negatively affected society at large. While some organizations are investing in teams to ensure algorithmic accountability and ethics, Reid Blackman, CEO of Virtue and former professor of philosophy at Colgate University and the University of North Carolina, Chapel Hill, says most are still falling short in ensuring their products perform ethically in the real world. "Despite reputational, regulatory, and legal risks, it's surprising how many companies that rely on AI/ML still lack the ability to identify, evaluate, and mitigate the associated ethical risks," says Blackman. "Teams end up either overlooking risks, scrambling to solve issues as they come up, or crossing their fingers in the hope that the problem will resolve itself."