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There is a growing concern that AI is likely to replace the work done face to face in the classroom by teachers. The concerns also extend to the students use of AI to complete assignments which could impact on their grades either positive or negative and in some cases, when a student work is detected with high AI the work could be classified as plagiarism if AI usage is not declared. On another note, there are increasing debates about the use of AI as a valid tool to support work completed by human beings. Whatever maybe the growing concerns, many researchers have argued that AI is not likely to replace education jobs such as teaching and learning done by teachers and education administrators. The purpose of this paper is to explore debates around the use of AI in education, mostly in teaching and learning and assessment of students university misconduct policy. Teachers work and the link to the opinions on integrating AI in the classroom are illuminated by empirical evidence gathered via interviews. A lot of educators respond to AI in different ways. Some of the debates falls around AI as God of the admin work and assessment of students s sent tools that can help reduce some work such as helping with multiple choice questions, on the other hand, some students have been penalised and in some cases failed their work due to use of AI in completing their assignments without acknowledging the use. In addition, others have argued that AI has come to replace the work done by teachers and are anxious about AI in education jobs done by teachers, hence would not bear the idea for classroom teaching and learning. [For the complete Volume 22 proceedings, see ED656158.]

A common and growing issue in scientific research and industry is that of storing and sharing ever-increasing datasets. In this paper we document the development and applications of Baler - a Machine Learning based tool for tailored compression of data across multiple disciplines. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data quality. This in turn will lead to significant benefits for the CLAS12 physics program. [ABSTRACT FROM AUTHOR]

Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future. [ABSTRACT FROM AUTHOR]

This paper investigates the implications of the European Union's Artificial Intelligence Act (AI Act), on conversational AI technologies. As the EU institutes a groundbreaking framework for AI regulation, this study assesses how the AI Act's risk-oriented approach impacts the crafting, deployment, and oversight of conversational AI. The analysis explores the Act's system classification, high-risk AI categorization, and delineation of duties for AI developers and deployers, examining effects on innovation, privacy, and ethical considerations within conversational AI. The significance of this research lies in its exploration of the EU AI Act's effort to balance technological progression with the safeguarding of fundamental rights and user privacy. By examining the AI Act provisions specific to conversational AI technologies like ChatGPT, this paper highlights the challenges and opportunities within the legislative framework. It addresses key regulatory concerns including data protection, algorithmic transparency, and accountability, evaluating the Act's role as a potential standard for AI legislation globally. Situated within the extensive debate on AI regulation and ethics, this contribution is timely, offering insights into how legislative bodies can adapt to and influence the rapid development of AI technologies. This analysis seeks to guide policymakers, developers, and the academic sphere in navigating the complexities of conversational AI regulation, proposing strategies to align AI technology's growth with societal values and legal frameworks. [ABSTRACT FROM AUTHOR]

The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine learning models—algorithms that learn from data without specific coding—and, more generally, AI models, due to AI's swiftly growing impact on scientific and engineering sectors. In this paper, we propose a practical definition of the FAIR principles for AI models and provide a template program for their adoption. We exemplify this strategy with an implementation from high-energy physics, where a graph neural network is employed to detect Higgs bosons decaying into two bottom quarks. [ABSTRACT FROM AUTHOR]

This paper explores the complex interaction between AI and customs procedures, aiming to uncover the legal ramifications associated with the integration of AI technologies in this field. With the rapid advancement of AI, customs authorities worldwide are increasingly adopting automated systems to improve the efficiency, accuracy, and security of cross-border trade operations. However, as AI systems take on significant roles in decision-making processes traditionally overseen by humans, concerns regarding legality, accountability, and transparency become prominent. Through a thorough examination of existing legal frameworks, international agreements, and relevant case law related to AI implementation in customs procedures, this research conducts a comprehensive analysis. It seeks to shed light on the evolving legal landscape surrounding AI in customs operations through a comparative study of various regulatory approaches and interpretations. [ABSTRACT FROM AUTHOR]

Technological progress has had both benefits and negative effects over time. The road to civilization was paved by scientists through hard work. Today we enjoy the results produced over a long period of time when civilizations and empires succeeded each other. Man has always sought to lead a better life, to explore territories and seek new opportunities. The institutionalisation of people's power in the form of the state led to some limitations on the freedom of free movement. The establishment of states led to the emergence of borders. This paper seeks to analyse how today's most advanced advancement, AI, can have both beneficial and negative contributions to society. In a world where basic human rights are no longer a novelty, regulation of new technologies has become a necessity. The lack of clear regulations leads to abuses by the state or its members. Ethics is a basic pillar in any field of activity. Scientific discoveries led to economic progress. Competitiveness has bred efficiency. Even if in a first stage there are systemic changes in certain economic branches, with the possibility of technology replacing certain activities carried out until then by man, the economic prosperity of the last two centuries was only possible through a mass development of production. The political factor, through its representative bodies, has the mission of ensuring a balance between technological development and society as a whole. The balance is ensured through regulation, legislation. Technologies must have a positive impact on the democratic world. This article examines new technologies in the context of refugees. Displacement of populations is not new, but in correlation with new technologies and fundamental human rights it acquires a new dimension. [ABSTRACT FROM AUTHOR]

The concept of „automated decision making" (ADM) is currently a key vector reshaping industries in the era of technology-based decision making, and the use of artificial intelligence (AI) in this process is a hot topic worldwide with the adoption of generative AI tools like ChatGPT. ADM enables companies to use technology for tasks that were not long ago the domain of human judgment. AI is essential in financial services, such as credit worthiness assessment, automated investment advice (often called robo-advice) or insurance. The regulation of activities related to ADM is contained in various acts and regulations, including the Sale of Goods Directive 2019/771, the Digital Contracts Directives 2019/770 and the EU General Regulation on Data Protection. However, these directives do not contain special rules for new technologies such as Artificial Intelligence (AI). Although recently, the EU has moved forward with regulations on product liability laws affecting digital products and services, with the EU Data Act, the EU Cyber Resilience Act and the highly publicized EU AI Act - regulations with a transition period of two years for compliance, the speed with which in practice the ADM tools are developed and trained make the legislative efforts slow and incomprehensible. In light of this issue, this article explores the relationship between AI, ADM and discusses whether existing EU consumer law is equipped to deal with situations where AI systems are either used for internal purposes by companies or offered to consumers as the main subject of the contract. The paper will reveal a number of gaps in current EU consumer law and briefly discuss future legislation. This article also reviews the efficiency of key EU consumer law directives for the conclusion of contracts with consumers through the use of AI, with recommendations for clarifications and additions to improve the use of ADM. [ABSTRACT FROM AUTHOR]

We present different elements of the 2021–2022 Computational Frontier (CompF) effort within the Snowmass process, including organization, events, and highlights of the final report [1]. With focus in the next 10–15 years, we analyze the impact of newly established and emerging technologies, identify challenges within theoretical, experimental and observational research programs, and present the report recommendations. We emphasize the main recommendation, which is the formation of a Coordinating Panel for Software and Computing (CPSC). [ABSTRACT FROM AUTHOR]

The evolution of modern heterogeneous accelerators, such as GPUs, has significantly advanced the landscape of artificial intelligence (AI). There is a notable surge to adopt AI within the nuclear physics domain (AI4NP). While most AI4NP studies focus on feasibility analysis, our attention is directed towards evaluating their performance on contemporary GPUs that integrate tensor cores. We first benchmark the throughput of hyperparameterized multi-layer perceptron (MLP) models. We then examine the performance of an AI4NP application: Hydra. We assess the performance gain and accuracy loss caused by the tensor cores for low-precision floating-point operations. Our experiments encompass the PyTorch and TensorFlow Keras frameworks on NVIDIA's T4 and A100 GPUs. We explore the behavior of different GPU hardware platforms and AI software tools. This study can be a valuable resource for guiding the performance optimization of larger-scale deployments of AI4NP applications. [ABSTRACT FROM AUTHOR]

The continuous growth in model complexity in high-energy physics (HEP) collider experiments demands increasingly time-consuming model fits. We show first results on the application of conditional invertible networks (cINNs) to this challenge. Specifically, we construct and train a cINN to learn the mapping from signal strength modifiers to observables and its inverse. The resulting network infers the posterior distribution of the signal strength modifiers rapidly and for low computational cost. We present performance indicators of such a setup including the treatment of systematic uncertainties. Additionally, we highlight the features of cINNs estimating the signal strength for a vector boson associated Higgs production analysis of simulated samples of events, which include a simulation of the CMS detector. [ABSTRACT FROM AUTHOR]

Recent developments of HEP software allow novel approaches to physics analysis workflows. The novel data delivery system, ServiceX, can be very effective when accessing a fraction of large datasets at remote grid sites. ServiceX can deliver user-selected columns with filtering and run at scale. We introduce the ServiceX data management package, ServiceX DataBinder, for easy manipulations of ServiceX delivery requests and delivered data using a single configuration file. We show various practical use cases within analysis pipelines that range from a data delivery of a few columns for machine learning study to a data delivery for full-scale physics analysis. [ABSTRACT FROM AUTHOR]