Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presents a multi-LLM communication framework designed to enhance the generation of persuasive data automatically. This framework facilitates the efficient production of high-quality, diverse linguistic content with minimal human oversight. Through extensive evaluations, we demonstrate that the generated data excels in naturalness, linguistic diversity, and the strategic use of persuasion, even in complex scenarios involving social taboos. The framework also proves adept at generalizing across novel contexts. Our results highlight the framework's potential to significantly advance research in both computational and social science domains concerning persuasive communication.

Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts. However, controlling these models to ensure consistent style remains challenging, with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper, we introduce StyleAligned, a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process, our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity, underscoring its efficacy in achieving consistent style across various inputs.

It is, to say the least, a very extensive report that raises important issues, but one can't help thinking that it might be self-serving in some cases, especially for the enormous tech companies that have already invested billions in AI and would like to control the degree of government intervention. That being said, it is well worth looking at the recommendations from the AI report and seeing whether or not they also apply to cybersecurity risk generally, as well as the cybersecurity, privacy, secrecy and safety risks of AI systems themselves. While the report is about AI, the recommendations apply equally well, if not more so, to cyberspace and cybersecurity risk. And then there is the cybersecurity of AI to consider as well as the use of AI in cybersecurity. It is, to say the least, a very extensive report that raises important issues, but one can't help thinking that it might be self-serving in some cases, especially for the enormous tech companies that have already invested billions in AI and would like to control the degree of government intervention.

They had spent many months honing an algorithm designed to steer an unmanned hot air balloon all the way from Puerto Rico to Peru. The balloon, controlled by its machine mind, kept veering off course. Salvatore Candido of Google's now-defunct Project Loon venture, which aimed to bring internet access to remote areas via the balloons, couldn't explain the craft's trajectory. His colleagues manually took control of the system and put it back on track. It was only later that they realised what was happening.