Artificial intelligence (AI) is hailed as the disruptive technology that is set to revolutionize the 21st century. The function and popularity of this technology are soaring by the day. It has the potential to solve many of humanity's most pressing problems. AI is an umbrella term for technologies that can display some kind of intelligence such as machine learning computer vision, natural language processing, etc… These intelligent agents are algorithms trained using vast amounts of data to give machines some kind of reasoning ability. Instead of purely logical processing that computers usually perform, intelligent agents are designed around human thinking patterns and problem-solving skills. Artificial intelligence technologies create intelligent systems capable of self-learning and adapting to any new challenge. These specification has led to the rapid adoption of AI across different fields and industries around the world. Artificial intelligence has been around for decades, but its applications are only now opening up as more and more resources are dedicated to it. Over the last few years, AI has significantly evolved and is being extensively used in different aspects of human life and industry. Companies have begun using intelligent machines to mine data to optimize just about everything within their business operations. Artificial intelligence is a branch of computer science that aims to create intelligent machines that work and react like humans. It is the broad term for any device that is capable of performing a task normally restricted to human intelligence. AI combines several disciplines such as computer science, cognitive psychology, and neuroscience. The concept underlying AI is to get a non-human entity to make decisions just as an intelligent human would. Artificial intelligence research is about the creation of computer systems capable of visual perception, speech recognition, decision-making, and translation between languages. So many fields are using AI nowadays, from research and home automation to data processing and analysis. The technology is used to solve problems in many different ways and it is found in many types of systems such as household appliances, automobiles, financial systems, medical applications, and many other common tools. AI has undergone rapid development over the past decades, fueled by significant research and trail-blazing technological advancements. Nowadays, it is often used to make computer programs better than humans at perception and cognition tasks. AI technologies are on an exponential level of development and are becoming so advanced that it is entering nearly every field of modern life.

The adoption of artificial intelligence (AI) and machine learning (ML) has been one of the fastest growing trends across industries over the past decade. With the continuous advancements in technology, access to ever more powerful computers, increased availability of clinical and research data, and rapid development of novel algorithms that analyze and utilize that data, interest in applying AI and ML to trial design and clinical trials to improve high failure rates is increasing. Among its many potential practical applications, AI and ML can be used to minimize errors in clinical trial participant management (e.g., cohort selection, patient identification and recruiting, participant retention) and streamline data management (e.g., automate data collection, monitor data quality, analyze large data sets).1 However, realizing the potential of this technology will require overcoming a range of different issues, including problems with data quality and access, transparency of underlying development and validation processes, potential bias inherent in the source data as well as the algorithm's implementation, and the lack of definitive regulatory guidance from the relevant government agencies. Selecting and recruiting patients for clinical trials is complicated and, despite the extensive time and effort companies put into clinical trial participant management, one of the biggest factors that causes a clinical trial to fail is failure to select and recruit the most suitable subjects for a trial.2

A smartphone app that tells a defendant what to say in court using artificial intelligence has been used for the first time - and is a lot cheaper than a lawyer. It is the first time artificial intelligence (AI) has been used in a trial anywhere in the world. The neural network will listen to all speeches from witnesses, lawyers and the judge. The defendant will be told exactly what to say via an earpiece - sticking to only those words. Legal history is being made over a speeding fine.

Bluescape announced that it has achieved Federal Risk and Authorization Management Program (FedRAMP) Authorization on Amazon Web Services (AWS) at the Moderate Impact Level through sponsorship of the US Air Force. Agencies from across the public, industry, and academic sectors, can now confidently leverage Bluescape's virtual workspaces to improve their agility, coordination, and outcomes regardless of their mission. "The government is increasingly demanding access to secure visual tools to support employees and distributed teams who now need to collaborate virtually on everything from simple whiteboarding and brainstorming sessions, to more complex incidence response programs and training AI/ML models," said John Greenstein, GM, Global Public Sector at Bluescape. "We are grateful to everyone at the GSA and DoD, especially the US Air Force, not only for sponsoring us at Impact Level 4 to meet their own needs, but also going the extra step to help us achieve FedRAMP Moderate, which brings this new capability to Civilian Agencies, too." With FedRAMP authorization status, Bluescape is now certified by the federal government to satisfy cloud security requirements and help accelerate federal agencies' digital transformation.

Russian cyber-criminals have been observed on dark web forums trying to bypass OpenAI's API restrictions to gain access to the ChatGPT chatbot for nefarious purposes. Various individuals have been observed, for instance, discussing how to use stolen payment cards to pay for upgraded users on OpenAI (thus circumventing the limitations of free accounts). Others have created blog posts on how to bypass the geo controls of OpenAI, and others still have created tutorials explaining how to use semi-legal online SMS services to register to ChatGPT. "Generally, there are a lot of tutorials in Russian semi-legal online SMS services on how to use it to register to ChatGPT, and we have examples that it is already being used," wrote Check Point Research (CPR), which shared the findings with Infosecurity ahead of publication. "It is not extremely difficult to bypass OpenAI's restricting measures for specific countries to access ChatGPT," said Sergey Shykevich, threat intelligence group manager at Check Point Software Technologies.

AEM presented 10 top trends for the future of building construction, among them alternative power, the electrification of compact equipment, autonomous machinery and sensors for increased safety. Referencing recent aviation fuel regulations plans, the California Air Resources Board's (CARB) ban on small engines on new equipment starting in 2024, the Environmental Protection Agency's (EPA) new greenhouse gas emissions rules for 2023–2026 passenger vehicles and light-duty trucks and the EPA's plan to reduce greenhouse gas emissions from heavy-duty trucks starting with 2027 models, the AEM whitepaper asserts that construction companies will see their fleets change over the next decade, as well. Major corporations continue to invest in renewable energy like biofuels, solar and wind power, as construction companies and large contractors commit to net-zero impact pledges for new buildings and infrastructure. The United States' commitment to cutting carbon emissions by 50% by 2030 will spur "the electrification of many segments of the compact construction equipment market" over the next 10 years, according to AEM. Thanks to the advanced 5G network and cloud systems, equipment tracking will allow real-time visibility into productivity and maintenance on a Jobsite, so operators and contractors can make sure they queue properly and have the most efficient job flow they can.

Objective: Text mining of clinical notes embedded in electronic medical records is increasingly used to extract patient characteristics otherwise not or only partly available, to assess their association with relevant health outcomes. As manual data labeling needed to develop text mining models is resource intensive, we investigated whether off-the-shelf text mining models developed at external institutions, together with limited within-institution labeled data, could be used to reliably extract study variables to conduct association studies. Materials and Methods: We developed multiple text mining models on different combinations of within-institution and external-institution data to extract social factors from discharge reports of intensive care patients. Subsequently, we assessed the associations between social factors and having a do-not-resuscitate/intubate code. Results: Important differences were found between associations based on manually labeled data compared to text-mined social factors in three out of five cases. Adopting external-institution text mining models using manually labeled within-institution data resulted in models with higher F1-scores, but not in meaningfully different associations. Discussion: While text mining facilitated scaling analyses to larger samples leading to discovering a larger number of associations, the estimates may be unreliable. Confirmation is needed with better text mining models, ideally on a larger manually labeled dataset. Conclusion: The currently used text mining models were not sufficiently accurate to be used reliably in an association study. Model adaptation using within-institution data did not improve the estimates. Further research is needed to set conditions for reliable use of text mining in medical research.

We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection with 2,200 manually labeled and 1.8 million machine-labeled titles that were posted from 2014 to the present by nine representative media organizations across three media bias groups - Left, Central, and Right in an active learning manner. The fine-tuned transformer-based language model achieves an overall accuracy of 0.84 and an F1 score of 0.78 on an external validation set. Next, we conduct a computational analysis to quantify the extent and dynamics of partisanship in news titles. While some aspects are as expected, our study reveals new or nuanced differences between the three media groups. We find that overall the Right media tends to use proportionally more hyperpartisan titles. Roughly around the 2016 Presidential Election, the proportions of hyperpartisan titles increased in all media bias groups where the relative increase in the proportion of hyperpartisan titles of the Left media was the most. We identify three major topics including foreign issues, political systems, and societal issues that are suggestive of hyperpartisanship in news titles using logistic regression models and the Shapley values. Through an analysis of the topic distribution, we find that societal issues gradually receive more attention from all media groups. We further apply a lexicon-based language analysis tool to the titles of each topic and quantify the linguistic distance between any pairs of the three media groups. Three distinct patterns are discovered. The Left media is linguistically more different from Central and Right in terms of foreign issues. The linguistic distance between the three media groups becomes smaller over recent years. In addition, a seasonal pattern where linguistic difference is associated with elections is observed for societal issues.

Machine learning (ML) and Artificial Intelligence (AI) are increasingly used in energy and engineering systems, but these models must be fair, unbiased, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful for making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of Explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL) in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using AI that is explainable, interpretable, and trustworthy, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. The objective of this paper is to understand the idea of XAI and IML and justify the important role of ML/AI in the Digital Twin framework and components, which requires XAI to understand the prediction better. This paper explains the importance of XAI and IML in both local and global aspects to ensure the use of trustworthy ML/AI applications for RUL prediction. This paper used the RUL prediction for the XAI and IML studies and leveraged the integrated python toolbox for interpretable machine learning (PiML).

The life cycle of a legislative or regulatory text begins with the publication of its complete version in the JORF and continues with the possible publication of texts amending it. The full amended text, called its consolidated version, is never published in the JORFandhasno legalvalue: only the initial version andthe suite ofthe ordered modifications of the text are authentic [8]. Since 2008, the French Légifrance [4] website presents most of the legal texts in their original versions as well as in their successive versions, consequences of the modifications brought to these texts over time. The operator of the Légifrance website, the Direction of Legal and Administrative Information(DILA), manually reports the modifications described in natural language in the texts in order to obtain, at each modification date, the complete consolidated version of the text. This convenience of access to the texts in an easier-to-read and easier-to-use version has de facto changed the status of these consolidated versions: they are seen by most users, including legal professionals, as the reflection of the applicable law [7].