Text-to-image (T2I) models have seen rapid progress in recent years, allowing the generation of complex images based on natural language inputs. However, even state-of-the-art T2I models need help accurately capture and reflect all the semantics in given prompts, leading to images that may miss crucial details, such as multiple subjects or specific spatial relationships. For… →

Multi-View and Multi-Scale Alignment for Mammography Contrastive Learning:Contrastive Language-Image Pre-training (CLIP) has shown potential in medical imaging, but its application to mammography faces challenges due to limited labeled data, high-resolution images, and imbalanced datasets. This study introduces the first full adaptation of CLIP to mammography through a new framework called Multi-view and Multi-scale Alignment (MaMA).… →

AMD has recently introduced its new language model, AMD-135M or AMD-Llama-135M, which is a significant addition to the landscape of AI models. Based on the LLaMA2 model architecture, this language model boasts a robust structure with 135 million parameters and is optimized for performance on AMD’s latest GPUs, specifically the MI250. This release marks a… →

The research evaluates the reliability of large language models (LLMs) such as GPT, LLaMA, and BLOOM, extensively used across various domains, including education, medicine, science, and administration. As the usage of these models becomes more prevalent, understanding their limitations and potential pitfalls is crucial. The research highlights that as these models increase in size and… →

Integrating AI-powered code-generating technologies, such as ChatGPT and GitHub Copilot, is revolutionizing programming education. These tools, by providing real-time assistance to developers, accelerate the development process, enhance problem-solving, and make coding more accessible. Their increasing prevalence has sparked a growing interest in their influence on how students learn programming.  While these tools can speed up… →

Machine Learning ML offers significant potential for accelerating the solution of partial differential equations (PDEs), a critical area in computational physics. The aim is to generate accurate PDE solutions faster than traditional numerical methods. While ML shows promise, concerns about reproducibility in ML-based science are growing. Issues like data leakage, weak baselines, and insufficient validation… →

In the age of data-driven artificial intelligence, LLMs like GPT-3 and BERT require vast amounts of well-structured data from diverse sources to improve performance across various applications. However, manually curating these datasets from the web is labor-intensive, inefficient, and often unscalable, creating a significant hurdle for developers aiming to acquire huge data. Traditional web crawlers… →

The intersection of contract law, artificial intelligence (AI), and smart contracts tells a fascinating yet complex story. As technology takes on a more prominent role in transactions and decision-making, it raises crucial questions about how foundational legal concepts like offer, acceptance, and intent apply. With the growing use of AI, concerns regarding accountability, enforceability, and… →

PyTorch has officially launched torchao, a comprehensive native library designed to optimize PyTorch models for better performance and efficiency. The launch of this library is a milestone in deep learning model optimization, providing users with an accessible toolkit that leverages advanced techniques such as low-bit types, quantization, and sparsity. The library is predominantly written in… →

The Free Energy Principle (FEP) and its extension, Active Inference (AIF), present a unique approach to understanding self-organization in natural systems. These frameworks propose that agents use internal generative models to predict observations from unknown external processes, continuously updating their perceptive and control states to minimize prediction errors. While this unifying principle offers profound insights… →