Generative AI (GenAI) models have demonstrated remarkable capabilities in a wide variety of medical tasks. However, as these models are trained using generalist datasets with very limited human oversight, they can learn uses of medical products that have not been adequately evaluated for safety and efficacy, nor approved by regulatory agencies. Given the scale at which GenAI may reach users, unvetted recommendations pose a public health risk. In this work, we propose an approach to identify potentially harmful product recommendations, and demonstrate it using a recent multimodal large language model.

As telecommunications networks become increasingly complex, the integration of advanced technologies such as network digital twins and generative artificial intelligence (AI) emerges as a pivotal solution to enhance network operations and resilience. This paper explores the synergy between network digital twins, which provide a dynamic virtual representation of physical networks, and generative AI, particularly focusing on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We propose a novel architectural framework that incorporates these technologies to significantly improve predictive maintenance, network scenario simulation, and real-time data-driven decision-making. Through extensive simulations, we demonstrate how generative AI can enhance the accuracy and operational efficiency of network digital twins, effectively handling real-world complexities such as unpredictable traffic loads and network failures. The findings suggest that this integration not only boosts the capability of digital twins in scenario forecasting and anomaly detection but also facilitates a more adaptive and intelligent network management system.

Foundation model-enabled generative artificial intelligence facilitates the development and implementation of agents, which can leverage distinguished reasoning and language processing capabilities to takes a proactive, autonomous role to pursue users' goals. Nevertheless, there is a lack of systematic knowledge to guide practitioners in designing the agents considering challenges of goal-seeking (including generating instrumental goals and plans), such as hallucinations inherent in foundation models, explainability of reasoning process, complex accountability, etc. To address this issue, we have performed a systematic literature review to understand the state-of-the-art foundation model-based agents and the broader ecosystem. In this paper, we present a pattern catalogue consisting of 17 architectural patterns with analyses of the context, forces, and trade-offs as the outcomes from the previous literature review. The proposed catalogue can provide holistic guidance for the effective use of patterns, and support the architecture design of foundation model-based agents by facilitating goal-seeking and plan generation.

This paper tackles the emerging challenge of training generative models within a self-consuming loop, wherein successive generations of models are recursively trained on mixtures of real and synthetic data from previous generations. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models, including parametric and non-parametric models. Specifically, we derive bounds on the total variation (TV) distance between the synthetic data distributions produced by future models and the original real data distribution under various mixed training scenarios for diffusion models with a one-hidden-layer neural network score function. Our analysis demonstrates that this distance can be effectively controlled under the condition that mixed training dataset sizes or proportions of real data are large enough. Interestingly, we further unveil a phase transition induced by expanding synthetic data amounts, proving theoretically that while the TV distance exhibits an initial ascent, it declines beyond a threshold point. Finally, we present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.

Apple is apparently looking to take all the help it can get to integrate generative AI into its recently announced Apple Intelligence. According to a report by the Wall Street Journal, citing sources with knowledge of the discussions, Apple has held talks with Meta about the possibility of using the company's generative AI model. It also reportedly had similar discussions with startups Anthropic and Perplexity. As of now, though, nothing has been finalized, WSJ reports. At WWDC earlier this month, Apple officially announced its much-rumored partnership with OpenAI that will bring ChatGPT to newer iPhones, iPads and Macs with the upcoming generation of the devices' OS.

ChatGPT can now easily pass any Turing test, a measure of successful A.I. proposed by a founder of computer science, Alan Turing. But contemporary Turing tests leave out the most interesting part of Turing's original test: the gender-bending. I can usually spot A.I. writing in my students' work by the overuse of words like "delve," but the accuracy of artificial intelligence is impossible to deny. A.I. is being integrated into every aspect of our written culture, from news sources to classrooms to medicine. But in 1950, Turing's ideas about A.I. were prescient, creative, and, when I read them, surprisingly queer.

For a few days, AI chip juggernaut Nvidia sat on the throne as the world's biggest company, but behind the its staggering success are questions on whether new entrants can stake a claim to the artificial intelligence bonanza. Nvidia, which makes the processors that are the only option to train generative AI's large language models, is now Big Tech's newest member and its stock market takeoff has lifted the whole sector. Even tech's second rung on Wall Street has ridden on Nvidia's coattails with Oracle, Broadcom, HP and a spate of others seeing their stock valuations surge, despite sometimes shaky earnings.

Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. Powered by foundation models that have been pretrained and can generate complex content, GenAI represents a paradigm shift away from the more traditional focus on task-specific classifiers that have dominated the AI landscape thus far. We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications that automate healthcare workflows at the point of care using smaller foundation models. These models will be finetuned for different capabilities and application specific scenarios and will have the ability to provide medical explanations, reference evidence within a retrieval augmented framework and utilizing external tools. We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance, including safety-critical diagnostic tasks, which will require greater research prior to implementation. We consider areas where 'human in the loop' Generative AI can improve healthcare quality and safety by automating mundane tasks. Using the principles of implementation science will be critical for integrating 'end to end' GenAI systems that will be accepted by healthcare teams.

In an opulent ballroom on a Saturday night, the classic pump-up anthem "Eye of the Tiger" blared as artificial intelligence enthusiasts tapped away on their keyboards. This was a hackathon -- an event where participants have a set amount of time to collaborate on a project they present to the crowd -- at a sprawling mansion about 30 minutes south of San Francisco. As a professional freelance photographer, I've spent the past decade documenting the people and culture of Silicon Valley. Ever since OpenAI's ChatGPT debuted in November 2022, countless entrepreneurs have been inspired to make their own generative AI tools. Now, nearly every new start-up has an AI element -- technology that automates simple tasks, for example, or a chatbot that provides mental health tips.

This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore, examining GenAI familiarity, perceptions of its use in assessment marking and feedback, knowledge checking and participation, and experiences of GenAI text detection. Descriptive statistics and reflexive thematic analysis revealed a generally low familiarity with GenAI among both groups. GenAI feedback was viewed negatively; however, it was viewed more positively when combined with instructor feedback. Academic staff were more accepting of GenAI text detection tools and grade adjustments based on detection results compared to students. Qualitative analysis identified three themes: unclear understanding of text detection tools, variability in experiences with GenAI detectors, and mixed feelings about GenAI's future impact on educational assessment. These findings have major implications regarding the development of policies and practices for GenAI-enabled assessment and feedback in higher education.