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.

Neural Architecture Search has attracted increasing attention in recent years. Among them, differential NAS approaches such as DARTS, have gained popularity for the search efficiency. However, they still suffer from three main issues, that are, the weak stability due to the performance collapse, the poor generalization ability of the searched architectures, and the inferior robustness to different kinds of proxies. To solve the stability and generalization problems, a simple-but-effective regularization method, termed as Beta-Decay, is proposed to regularize the DARTS-based NAS searching process (i.e., $\beta$-DARTS). Specifically, Beta-Decay regularization can impose constraints to keep the value and variance of activated architecture parameters from being too large, thereby ensuring fair competition among architecture parameters and making the supernet less sensitive to the impact of input on the operation set. In-depth theoretical analyses on how it works and why it works are provided. Comprehensive experiments validate that Beta-Decay regularization can help to stabilize the searching process and makes the searched network more transferable across different datasets. To address the robustness problem, we first benchmark different NAS methods under a wide range of proxy data, proxy channels, proxy layers and proxy epochs, since the robustness of NAS under different kinds of proxies has not been explored before. We then conclude some interesting findings and find that $\beta$-DARTS always achieves the best result among all compared NAS methods under almost all proxies. We further introduce the novel flooding regularization to the weight optimization of $\beta$-DARTS (i.e., Bi-level regularization), and experimentally and theoretically verify its effectiveness for improving the proxy robustness of differentiable NAS.

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].

During the 1950s, Alan Turing proposed an experiment called the imitation game (now called the Turing test). In it, he posited a situation where someone--the interrogator--was in a room, separated from another room that had a computer and a second person. The goal of the test was for the interrogator to ask questions of both the person and the computer; the goal of the computer was to make the interrogator believe it was a human. Turing predicted that, eventually, computers would be able to mimic human behavior successfully and fool interrogators a high percentage of the time. Turing's prediction has yet to come to pass, and there's a fair question of whether computers will ever be able to truly complete the test.

Next month, AI will enter the courtroom, and the US legal system may never be the same. An artificial intelligence chatbot, technology programmed to respond to questions and hold a conversation, is expected to advise two individuals fighting speeding tickets in courtrooms in undisclosed cities. The two will wear a wireless headphone, which will relay what the judge says to the chatbot being run by DoNotPay, a company that typically helps people fight traffic tickets through the mail. The headphone will then play the chatbot's suggested responses to the judge's questions, which the individuals can then choose to repeat in court. But it also has the potential to change how people interact with the law, and to bring many more changes over time.

SAN FRANCISCO – Next month, AI will enter the courtroom, and the US legal system may never be the same. An artificial intelligence chatbot, technology programmed to respond to questions and hold a conversation, is expected to advise two individuals fighting speeding tickets in courtrooms in undisclosed cities. The two will wear a wireless headphone, which will relay what the judge says to the chatbot being run by DoNotPay, a company that typically helps people fight traffic tickets through the mail. The headphone will then play the chatbot's suggested responses to the judge's questions, which the individuals can then choose to repeat in court. But it also has the potential to change how people interact with the law, and to bring many more changes over time. DoNotPay CEO Josh Browder says expensive legal fees have historically kept people from hiring traditional lawyers to fight for them in traffic court, which typically involves fines that can reach into the hundreds of dollars.

Next month, AI will enter the courtroom, and the US legal system may never be the same. An artificial intelligence chatbot, technology programmed to respond to questions and hold a conversation, is expected to advise two individuals fighting speeding tickets in courtrooms in undisclosed cities. The two will wear a wireless headphone, which will relay what the judge says to the chatbot being run by DoNotPay, a company that typically helps people fight traffic tickets through the mail. The headphone will then play the chatbot's suggested responses to the judge's questions, which the individuals can then choose to repeat in court. But it also has the potential to change how people interact with the law, and to bring many more changes over time.

Academic Earth More than 1,500 video lectures by professors from Harvard, Yale, broken down into single classes on topics like art, architecture, and astronomy.

Increasing the number of cyclists, whether for general transport or recreation, can provide health improvements and reduce the environmental impact of vehicular transportation. However, the public's perception of cycling may be driven by the ideologies and reporting standards of news agencies. For instance, people may identify cyclists on the road as "dangerous" if news agencies overly report cycling accidents, limiting the number of people that cycle for transportation. Moreover, if fewer people cycle, there may be less funding from the government to invest in safe infrastructure. In this paper, we explore the perceived perception of cyclists within news headlines. To accomplish this, we introduce a new dataset, "Bike Frames", that can help provide insight into how headlines portray cyclists and help detect accident-related headlines. Next, we introduce a multi-task (MT) regularization approach that increases the detection accuracy of accident-related posts, demonstrating improvements over traditional MT frameworks. Finally, we compare and contrast the perceptions of cyclists with motorcyclist-related headlines to ground the findings with another related activity for both male- and female-related posts. Our findings show that general news websites are more likely to report accidents about cyclists than other events. Moreover, cyclist-specific websites are more likely to report about accidents than motorcycling-specific websites, even though there is more potential danger for motorcyclists. Finally, we show substantial differences in the reporting about male vs. female-related persons, e.g., more male-related cyclists headlines are related to accidents, but more female-related motorcycling headlines about accidents. WARNING: This paper contains descriptions of accidents and death.