Generative AI is rapidly transforming medical imaging and text analysis, offering immense potential for enhanced diagnosis and personalized care. However, this transformative technology raises crucial ethical, societal, and legal questions. This paper delves into these complexities, examining issues of accuracy, informed consent, data privacy, and algorithmic limitations in the context of generative AI's application to medical imaging and text. We explore the legal landscape surrounding liability and accountability, emphasizing the need for robust regulatory frameworks. Furthermore, we dissect the algorithmic challenges, including data biases, model limitations, and workflow integration. By critically analyzing these challenges and proposing responsible solutions, we aim to foster a roadmap for ethical and responsible implementation of generative AI in healthcare, ensuring its transformative potential serves humanity with utmost care and precision.

Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of data, and generating human-preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We provide a discussion of the progress and advantages of LLMs in financial contexts, analyzing their advanced technologies as well as prospective capabilities in contextual understanding, transfer learning flexibility, complex emotion detection, etc. We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications. For each application area, we delve into specific methodologies, such as textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations. Furthermore, a comprehensive collection of datasets, model assets, and useful codes associated with mainstream applications are presented as resources for the researchers and practitioners. Finally, we outline the challenges and opportunities for future research, particularly emphasizing a number of distinctive aspects in this field. We hope our work can help facilitate the adoption and further development of LLMs in the financial sector.

Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current, existing approaches typically involve continued pre-training on new documents. However, they frequently face difficulties in extracting stored knowledge. Motivated by the remarkable success of the Feynman Technique in efficient human learning, we introduce Self-Tuning, a learning framework aimed at improving an LLM's ability to effectively acquire new knowledge from raw documents through self-teaching. Specifically, we develop a Self-Teaching strategy that augments the documents with a set of knowledge-intensive tasks created in a self-supervised manner, focusing on three crucial aspects: memorization, comprehension, and self-reflection. In addition, we introduce three Wiki-Newpages-2023-QA datasets to facilitate an in-depth analysis of an LLM's knowledge acquisition ability concerning memorization, extraction, and reasoning. Extensive experimental results on Llama2 family models reveal that Self-Tuning consistently exhibits superior performance across all knowledge acquisition tasks and excels in preserving previous knowledge.

In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, making in-depth literature analysis increasingly challenging and time-consuming. The emergence of Large Language Models (LLMs) has offered a new way to address this challenge. Known for their strong abilities in summarizing texts, LLMs are seen as a potential tool to improve the analysis of scientific literature. However, existing LLMs have their own limits. Scientific literature often includes a wide range of multimodal elements, such as tables, charts, and molecule, which are hard for text-focused LLMs to understand and analyze. This issue points to the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. To answer this demand, we present \textbf{Uni-SMART} (Universal Science Multimodal Analysis and Research Transformer), an innovative model designed for in-depth understanding of multimodal scientific literature. Through rigorous quantitative evaluation across several domains, Uni-SMART demonstrates superior performance over other text-focused LLMs. Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts. These applications not only highlight Uni-SMART's adaptability but also its potential to revolutionize how we interact with scientific literature.

Previous studies on framing have relied on manual analysis or fine-tuning models with limited annotated datasets. However, pre-trained models, with their diverse training backgrounds, offer a promising alternative. This paper presents a comprehensive analysis of GPT-4, GPT-3.5 Turbo, and FLAN-T5 models in detecting framing in news headlines. We evaluated these models in various scenarios: zero-shot, few-shot with in-domain examples, cross-domain examples, and settings where models explain their predictions. Our results show that explainable predictions lead to more reliable outcomes. GPT-4 performed exceptionally well in few-shot settings but often misinterpreted emotional language as framing, highlighting a significant challenge. Additionally, the results suggest that consistent predictions across multiple models could help identify potential annotation inaccuracies in datasets. Finally, we propose a new small dataset for real-world evaluation on headlines from a diverse set of topics.

Group of Seven (G7) leaders agreed Friday on establishing a common code of conduct for organizations engaging with artificial intelligence (AI), while striking deals on a string of joint initiatives to support the development of clean energy resources in Africa and boost the resilience of global food supply chains. In a joint communique, the bloc also agreed to build a common framework to combat illegal migration, with the aim of cracking down on human trafficking while bolstering the investigation capabilities of authorities on the African continent. In line with the agenda set by Italian Prime Minister Giorgia Meloni, the group reiterated the need to build equal partnerships with developing and emerging countries across the so-called Global South.

Police seized ketamine hidden inside life-size Transformer robots in Thailand. A woman who was previously caught trying to ship meth hidden in a food processing machine was trying to send the robots to Taiwan. Cambodian authorities on Friday destroyed more than seven tons of illicit drugs and the ingredients for them, as a drug-fighting official said educating people about their danger is the best way of combating the illegal trade. Some 4.1 tons of the destroyed items were drugs including heroin, marijuana, methamphetamine, ecstasy and ketamine that had been confiscated from traffickers across the country, the National Authority for Combating Drugs said. The remaining 3.2 tons were various chemicals and other ingredients used to produce illegal drugs, it said.

When Christi Angel first talked to a chatbot impersonating her deceased partner, Cameroun, she found the encounter surreal and "very weird". "Yes, I knew it was an AI system but, once I started chatting, my feeling was I was talking to Cameroun. That's how real it felt to me," she says. Angel's conversation with "Cameroun" took a more sinister turn when the persona assumed by the chatbot said he was "in hell". Angel, a practising Christian, found the exchange upsetting and returned a second time seeking a form of closure, which the chatbot provided.

Heritage Foundation tech policy director Kara Frederick joins'America's Newsroom' to discuss pornographic AI photos of Taylor Swift sparking conversations about deepfake regulation. A bill aimed at outlawing "revenge porn" has been approved by lawmakers in the Massachusetts House and Senate and shipped to Democratic Gov. Maura Healey, a move advocates say was long overdue. If signed by Healey, the bill -- which bars the sharing of explicit images or videos without the consent of those depicted in the videos -- would leave South Carolina as the only state not to have a law specifically banning revenge porn. Supports say the bill, which landed on Healey's desk Thursday, would align Massachusetts with the other 48 states that have clear prohibitions on disseminating sexually explicit images and videos without the subject's consent. It is a form of abuse that advocates say has grown increasingly common in the digital age, subjecting people to social and emotional harm often inflicted by former romantic partners.

Controversial facial recognition company Clearview AI has agreed to an unusual settlement to a class action lawsuit, The New York Times reports. Rather than paying cash, the company would provide a 23 percent stake in its company to any Americans in its database. Without the settlement, Clearview could go bankrupt, according to court documents. If you live in the US and have ever posted a photo of yourself publicly online, you may be part of the class action. The settlement could amount to at least 50 million according to court documents, It still must be approved by a federal judge.