Adobe's Firefly Video Model is in public beta as of today, meaning the days of praying you had a chance to test it are over. Previously, it was only available in the Adobe Premiere Pro video editor with Generative Extend, but you can also access a standalone Firefly web app now. For those unaware, Adobe's Firefly Video Model powers the Generate Video feature, which can generate video clips from a text prompt or image. It can also edit images, turn them into videos, create 3D worlds and more. The content is "safe" for commercial use too, since the AI applies watermarks signifying which parts have AI assistance, and that can be checked with the Adobe Content Authenticity web app's Inspect tool.

This image shows the three-dimensional genome structures of several chromosomes reported in a Dip-C study, which were used to train the new ChromoGen model. Every cell in your body contains the same genetic sequence, yet each cell expresses only a subset of those genes. These cell-specific gene expression patterns, which ensure that a brain cell is different from a skin cell, are partly determined by the three-dimensional structure of the genetic material, which controls the accessibility of each gene. MIT chemists have now come up with a new way to determine those 3D genome structures, using generative artificial intelligence. Their technique can predict thousands of structures in just minutes, making it much speedier than existing experimental methods for analyzing the structures.

When people talk about the "size" of an AI model, they're referring to the number of "parameters" it contains. A parameter is one variable in the AI model that determines how it generates output, and any given AI model can have billions of these parameters. Also referred to as model weights, these parameters occupy storage space to operate properly -- and when an AI model has billions of parameters, storage requirements can quickly balloon. As you can see, the storage space consumed by an LLM increases with the size of its parameters. The same is true for other types of generative AI models, too.

The two had a blowout argument over the future direction of OpenAI – the company they came together to found in 2015 – with Altman seemingly content to pursue a for-profit approach and Musk feeling that was forswearing the founding principles of the firm as well as its name. OpenAI couldn't be open, he reckoned, if it was closed off and trying to make money rather than better humanity. So it's no surprise that Musk, who lodged an audacious bid to take over Twitter a little more than two years ago, which ended up with his ownership of the platform now called X, has sought to put a spoiler in two years of near-untrammelled growth for OpenAI. Musk – who is currently overhauling (to his supporters; "tearing down" to his opponents) the US government to be, as he would describe it, leaner and more efficient while also devastating important programmes such as international aid and cutting-edge scientific research – has lodged a near 100bn bid for OpenAI's non-profit arm. "It's time for OpenAI to return to the open-source, safety-focused force for good it once was," Musk said in a statement supplied by the lawyer shepherding his bid.

The feud between Elon Musk and Sam Altman has become one of the bitterest rivalries in business history, with the Tesla tycoon bidding to buy Altman's OpenAI in an apparent attempt to derail the ChatGPT maker's ascent to becoming one of the world's most important companies. Musk and Altman were among the 11-person team that founded OpenAI in 2015. Created as a counterweight to Google's dominance in artificial intelligence, the project got its initial funding from Musk, who invested 45 million to get it started. Three years later, Musk departed OpenAI. The company initially cited "a potential future conflict for Elon ... as Tesla continues to become more focused on AI," noting the electric vehicle company's ambitions in autonomous driving.

Developing web-based GIS applications, commonly known as CyberGIS dashboards, for querying and visualizing GIS data in environmental research often demands repetitive and resource-intensive efforts. While Generative AI offers automation potential for code generation, it struggles with complex scientific applications due to challenges in integrating domain knowledge, software engineering principles, and UI design best practices. This paper introduces a knowledge-augmented code generation framework that retrieves software engineering best practices, domain expertise, and advanced technology stacks from a specialized knowledge base to enhance Generative Pre-trained Transformers (GPT) for front-end development. The framework automates the creation of GIS-based web applications (e.g., dashboards, interfaces) from user-defined UI wireframes sketched in tools like PowerPoint or Adobe Illustrator. A novel Context-Aware Visual Prompting method, implemented in Python, extracts layouts and interface features from these wireframes to guide code generation. Our approach leverages Large Language Models (LLMs) to generate front-end code by integrating structured reasoning, software engineering principles, and domain knowledge, drawing inspiration from Chain-of-Thought (CoT) prompting and Retrieval-Augmented Generation (RAG). A case study demonstrates the framework's capability to generate a modular, maintainable web platform hosting multiple dashboards for visualizing environmental and energy data (e.g., time-series, shapefiles, rasters) from user-sketched wireframes. By employing a knowledge-driven approach, the framework produces scalable, industry-standard front-end code using design patterns such as Model-View-ViewModel (MVVM) and frameworks like React. This significantly reduces manual effort in design and coding, pioneering an automated and efficient method for developing smart city software.

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.

We present Hookpad Aria, a generative AI system designed to assist musicians in writing Western pop songs. Our system is seamlessly integrated into Hookpad, a web-based editor designed for the composition of lead sheets: symbolic music scores that describe melody and harmony. Hookpad Aria has numerous generation capabilities designed to assist users in non-sequential composition workflows, including: (1) generating left-to-right continuations of existing material, (2) filling in missing spans in the middle of existing material, and (3) generating harmony from melody and vice versa. Hookpad Aria is also a scalable data flywheel for music co-creation -- since its release in March 2024, Aria has generated 318k suggestions for 3k users who have accepted 74k into their songs. More information about Hookpad Aria is available at https://www.hooktheory.com/hookpad/aria

As Generative AI (GenAI) continues to gain prominence and utility across various sectors, their integration into the realm of Internet of Things (IoT) security evolves rapidly. This work delves into an examination of the state-of-the-art literature and practical applications on how GenAI could improve and be applied in the security landscape of IoT. Our investigation aims to map the current state of GenAI implementation within IoT security, exploring their potential to fortify security measures further. Through the compilation, synthesis, and analysis of the latest advancements in GenAI technologies applied to IoT, this paper not only introduces fresh insights into the field, but also lays the groundwork for future research directions. It explains the prevailing challenges within IoT security, discusses the effectiveness of GenAI in addressing these issues, and identifies significant research gaps through MITRE Mitigations. Accompanied with three case studies, we provide a comprehensive overview of the progress and future prospects of GenAI applications in IoT security. This study serves as a foundational resource to improve IoT security through the innovative application of GenAI, thus contributing to the broader discourse on IoT security and technology integration.

Causal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach for model compression. A significant remaining issue lies in the major differences between teacher and student models, namely the substantial capacity gap, mode averaging, and mode collapse, which pose barriers during distillation.s To address these issues, we introduce Temporally Adaptive Interpolated Distillation (TAID), a novel knowledge distillation approach that dynamically interpolates student and teacher distributions through an adaptive intermediate distribution, gradually shifting from the student's initial distribution towards the teacher's distribution. We provide a theoretical analysis demonstrating TAID's ability to prevent mode collapse and empirically show its effectiveness in addressing the capacity gap while balancing mode averaging and mode collapse. Our comprehensive experiments demonstrate TAID's superior performance across various model sizes and architectures in both instruction tuning and pre-training scenarios. These results demonstrate TAID's effectiveness in creating high-performing and efficient models, advancing the development of more accessible AI technologies. Large language models are too large. Causal language models (LMs) are increasingly becoming essential tools across various sectors (Malinka et al., 2023; Wu et al., 2023; Zhang et al., 2023a; He et al., 2024). Scaling data size, model size, and training steps has been the primary approach to improve LM performance (Kaplan et al., 2020; Hoffmann et al., 2022; OpenAI et al., 2024), leading to rapid advancements in both proprietary and open-source LMs (Touvron et al., 2023; Abdin et al., 2024; Yang et al., 2024). This paradox of scale hinders the widespread deployment and use of LMs despite their potential and high demand. Knowledge distillation offers a promising prescription. One promising approach to developing compact yet high-performing models is knowledge distillation (KD) (Hinton et al., 2015).