Illinois advertising professor Michelle Nelson says voters should expect to see a lot more generative AI in political ads during the 2024 election cycle, warning that it might be difficult to impossible to tell what's real and what's fake. It's estimated that 12 billion will be spent on political ads this [USA] election cycle – 30% more than in 2020. The sheer volume of ads is remarkable, and there is vast potential to use this political information to contribute to democracy: to reach more potential voters and provide accurate information. There's also more potential than ever for generative artificial intelligence to misrepresent candidates and policies, leading to confusion in the voting booth. News Bureau editor Lois Yoksoulian spoke with advertising professor and department head Michelle Nelson about the topic.

The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education. This study delves into university instructors' experiences and attitudes toward AI language models, filling a gap in the literature by analyzing educators' perspectives on AI's role in the classroom and its potential impacts on teaching and learning. The objective of this research is to investigate the level of awareness, overall sentiment towardsadoption, and the factors influencing these attitudes for LLMs and generative AI-based tools in higher education. Data was collected through a survey using a Likert scale, which was complemented by follow-up interviews to gain a more nuanced understanding of the instructors' viewpoints. The collected data was processed using statistical and thematic analysis techniques. Our findings reveal that educators are increasingly aware of and generally positive towards these tools. We find no correlation between teaching style and attitude toward generative AI. Finally, while CS educators show far more confidence in their technical understanding of generative AI tools and more positivity towards them than educators in other fields, they show no more confidence in their ability to detect AI-generated work.

In recent years, the emergence of Large Language Models (LLMs) represent a critical turning point in Generative AI and its ability to expedite productivity across a variety domains. However, the capabilities of these models, while impressive, are limited in a number of ways that have hindered certain industries from being able to take full advantage of the potential of this technology. A key disadvantage is the tendency for LLMs to hallucinate information and its lack of knowledge in domain specific areas. The knowledge of LLMs are limited by their training data, and without the use of additional techniques, these models have very poor performance of very domain specific tasks. In order to develop a large language model, the first step is the pre-training process where a transformer is trained on a very large corpus of text data. This data is very general and not specific to a certain domain or field, as well as unchanging with time. This is a reason why LLMs like ChatGPT might perform well for general queries but fail on questions on more specific and higher-level topics. Additionally, a model's performance about a certain topic is highly dependent on how often that information appears in the training data, meaning that LLMs struggle with information that does not appear frequently.

Sonatype's 2023 report found that 97% of developers and security leads integrate generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), into their development process. Concerns about the security implications of this trend have been raised. Developers are now weighing the benefits and risks of LLMs against other relied-upon information sources, such as StackOverflow (SO), requiring empirical data to inform their choice. In this work, our goal is to raise software developers awareness of the security implications when selecting code snippets by empirically comparing the vulnerabilities of ChatGPT and StackOverflow. To achieve this, we used an existing Java dataset from SO with security-related questions and answers. Then, we asked ChatGPT the same SO questions, gathering the generated code for comparison. After curating the dataset, we analyzed the number and types of Common Weakness Enumeration (CWE) vulnerabilities of 108 snippets from each platform using CodeQL. ChatGPT-generated code contained 248 vulnerabilities compared to the 302 vulnerabilities found in SO snippets, producing 20% fewer vulnerabilities with a statistically significant difference. Additionally, ChatGPT generated 19 types of CWE, fewer than the 22 found in SO. Our findings suggest developers are under-educated on insecure code propagation from both platforms, as we found 274 unique vulnerabilities and 25 types of CWE. Any code copied and pasted, created by AI or humans, cannot be trusted blindly, requiring good software engineering practices to reduce risk. Future work can help minimize insecure code propagation from any platform.

Emerging technologies like generative AI tools, including ChatGPT, are increasingly utilized in educational settings, offering innovative approaches to learning while simultaneously posing new challenges. This study employs a survey methodology to examine the policy landscape concerning these technologies, drawing insights from 102 high school principals and higher education provosts. Our results reveal a prominent policy gap: the majority of institutions lack specialized guide-lines for the ethical deployment of AI tools such as ChatGPT. Moreover,we observed that high schools are less inclined to work on policies than higher educational institutions. Where such policies do exist, they often overlook crucial issues, including student privacy and algorithmic transparency. Administrators overwhelmingly recognize the necessity of these policies, primarily to safeguard student safety and mitigate plagiarism risks. Our findings underscore the urgent need for flexible and iterative policy frameworks in educational contexts.

Performance attribution analysis, defined as the process of explaining the drivers of the excess performance of an investment portfolio against a benchmark, stands as a significant feature of portfolio management and plays a crucial role in the investment decision-making process, particularly within the fund management industry. Rooted in a solid financial and mathematical framework, the importance and methodologies of this analytical technique are extensively documented across numerous academic research papers and books. The integration of large language models (LLMs) and AI agents marks a groundbreaking development in this field. These agents are designed to automate and enhance the performance attribution analysis by accurately calculating and analyzing portfolio performances against benchmarks. In this study, we introduce the application of an AI Agent for a variety of essential performance attribution tasks, including the analysis of performance drivers and utilizing LLMs as calculation engine for multi-level attribution analysis and question-answering (QA) tasks. Leveraging advanced prompt engineering techniques such as Chain-of-Thought (CoT) and Plan and Solve (PS), and employing a standard agent framework from LangChain, the research achieves promising results: it achieves accuracy rates exceeding 93% in analyzing performance drivers, attains 100% in multi-level attribution calculations, and surpasses 84% accuracy in QA exercises that simulate official examination standards. These findings affirm the impactful role of AI agents, prompt engineering and evaluation in advancing portfolio management processes, highlighting a significant development in the practical application and evaluation of Generative AI technologies within the domain.

Rep. Jay Obernolte was selected to lead the House task force on AI. Fox News Digital speaks with the California Republican about his goals for the panel and his own thoughts about the rapidly advancing technology. AI fakery is quickly becoming one of the biggest problems confronting us online. Deceptive pictures, videos and audio are proliferating as a result of the rise and misuse of generative artificial intelligence tools. With AI deepfakes cropping up almost every day, depicting everyone from Taylor Swift to Donald Trump, it's getting harder to tell what's real from what's not.

In recent times, the grandeur of Large Language Models (LLMs) has not only shone in the realm of natural language processing but has also cast its brilliance across a vast array of applications. This remarkable display of LLM capabilities has ignited a surge in research contributions within this domain, spanning a diverse spectrum of topics. These contributions encompass advancements in neural network architecture, context length enhancements, model alignment, training datasets, benchmarking, efficiency improvements, and more. Recent years have witnessed a dynamic synergy between academia and industry, propelling the field of LLM research to new heights. A notable milestone in this journey is the introduction of ChatGPT, a powerful AI chatbot grounded in LLMs, which has garnered widespread societal attention. The evolving technology of LLMs has begun to reshape the landscape of the entire AI community, promising a revolutionary shift in the way we create and employ AI algorithms. Given this swift-paced technical evolution, our survey embarks on a journey to encapsulate the recent strides made in the world of LLMs. Through an exploration of the background, key discoveries, and prevailing methodologies, we offer an up-to-the-minute review of the literature. By examining multiple LLM models, our paper not only presents a comprehensive overview but also charts a course that identifies existing challenges and points toward potential future research trajectories.

General purpose AI, such as ChatGPT, seems to have lowered the barriers for the public to use AI and harness its power. However, the governance and development of AI still remain in the hands of a few, and the pace of development is accelerating without proper assessment of risks. As a first step towards democratic governance and risk assessment of AI, we introduce Particip-AI, a framework to gather current and future AI use cases and their harms and benefits from non-expert public. Our framework allows us to study more nuanced and detailed public opinions on AI through collecting use cases, surfacing diverse harms through risk assessment under alternate scenarios (i.e., developing and not developing a use case), and illuminating tensions over AI development through making a concluding choice on its development. To showcase the promise of our framework towards guiding democratic AI, we gather responses from 295 demographically diverse participants. We find that participants' responses emphasize applications for personal life and society, contrasting with most current AI development's business focus. This shows the value of surfacing diverse harms that are complementary to expert assessments. Furthermore, we found that perceived impact of not developing use cases predicted participants' judgements of whether AI use cases should be developed, and highlighted lay users' concerns of techno-solutionism. We conclude with a discussion on how frameworks like Particip-AI can further guide democratic AI governance and regulation.

Net and a fine-tuned SD model exhibits acceptable performance, as shown in the upper part of Figure 1. The Webtoon phenomenon has evolved beyond traditional paperbased Despite the breadth of existing studies, designing a portrait comics. It uses information technology to present content that stylization framework at the business level remains challenging, as is both produced and consumed in a digital format, and it is rapidly shown in the bottom part of Figure 1. First, concerns exist over skintone gaining global popularity. Webtoon is thus well positioned as an optimal expression, in which a model uniformly alters users' actual skin environment for integration with generative AI. In this regard, tones to match those of a specific trained style, possibly leading to portrait stylization has been an active research area, in which given ethical issues. Second, malicious users could generate sexual content individual photographs are translated into specific art styles to enhance with a specific style. In IP-based businesses, safeguarding the the value of intellectual property (IP) by delivering a distinct IP is crucial; unfortunately, the neglect of this issue in existing studies sense of enjoyment to users [1].