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].

Microsoft has appointed the co-founder of the British artificial intelligence lab DeepMind as the head of a new AI division. Mustafa Suleyman, 39, co-founded DeepMind with Demis Hassabis and Shane Legg in 2010 and the company went on to be bought by Google for 400m in 2014. It now forms the core of Google's AI efforts after merging with another unit to become Google DeepMind in 2023. The chief executive of Microsoft, Satya Nadella, announced in a blogpost that the British AI pioneer, who left DeepMind in 2019, will be chief executive of a new organisation called Microsoft AI focusing on the US company's consumer products and research. Several employees at Sulyeman's Inflection AI startup will join the division.

Using artificial intelligence to create artworks increases artists' productivity and generates more positive reactions, according to a study involving submissions to a popular art-sharing website by more than 50,000 users. However, generative AI works are more likely to display stereotypical themes and depictions, reducing the novelty of the artist's work. How this moment for AI will change society forever (and how it won't)

Microsoft has named Mustafa Suleyman head of its consumer artificial intelligence business, hiring most of the staff from his Inflection AI startup as the software giant seeks to fend off Alphabet's Google in the fiercely contested market for AI products. Suleyman, who co-founded Google's DeepMind, will report to CEO Satya Nadella and oversee a range of projects, such as integrating an AI Copilot into Windows and adding conversational elements to the Bing search engine. His hiring will put Microsoft's consumer AI work under one leader for the first time. Inflection has been a rival of Microsoft's key AI partner OpenAI. The company is shifting to selling AI software to businesses but will continue operating its Pi consumer chatbot business for now.