​[[{“value”:” Continual learning is a rapidly evolving area of research that focuses on developing models capable of learning from sequentially arriving data streams, similar to human learning. It addresses the challenges of adapting to new information while retaining previously acquired knowledge. This field is particularly… Read More »RanDumb: A Simple Yet Powerful AI Approach to Exemplar-Free Continual Learning Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” One of the central challenges in spatiotemporal prediction is efficiently handling the vast and complex datasets produced in diverse domains such as environmental monitoring, epidemiology, and cloud computing. Spatiotemporal datasets consist of time-evolving data observed at different spatial locations, making their analysis critical for… Read More »This AI Paper from Google Unveils How Bayesian Neural Fields Revolutionize Spatiotemporal Forecasting for Large Datasets Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Recent advances in multimodal foundation models like GPT-4V have shown strong performance in general visual and textual data tasks. However, adapting these models to specialized domains like biomedicine requires large, domain-specific instruction datasets. While automatic dataset generation has been explored, these datasets often need… Read More »BioMed-VITAL: A Clinician-Aligned AI Framework for Biomedical Visual Instruction Tuning Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Natural language processing (NLP) has experienced a surge in progress with the emergence of large language models (LLMs), which are utilized in various applications such as text generation, translation, and conversational agents. These models can process and understand human languages at an unprecedented level,… Read More »This AI Paper from KAIST AI Introduces a Novel Approach to Improving LLM Inference Efficiency in Multilingual Settings Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) and image generators face a critical challenge known as model collapse. This phenomenon occurs when the performance of these AI systems deteriorates due to the increasing presence of AI-generated data in their training datasets. As generative AI evolves, evidence suggests… Read More »Model Collapse in the Synthetic Data Era: Analytical Insights and Mitigation Strategies Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Table of contents Introduction to Chunking in RAGOverview of Chunking in RAGDetailed Analysis of Each Chunking MethodChoosing the Right Chunking TechniqueConclusion Introduction to Chunking in RAG In natural language processing (NLP), Retrieval-Augmented Generation (RAG) is emerging as a powerful tool for information retrieval and… Read More »Chunking Techniques for Retrieval-Augmented Generation (RAG): A Comprehensive Guide to Optimizing Text Segmentation Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) evaluate and interpret links between words or tokens in a sequence primarily through the self-attention mechanism. However, this module’s time and memory complexity rises quadratically with sequence length, which is a disadvantage. Longer sequences demand exponentially more memory and processing,… Read More »Researchers from China Introduce INT-FlashAttention: INT8 Quantization Architecture Compatible with FlashAttention Improving the Inference Speed of FlashAttention on Ampere GPUs Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Human-sensing applications such as activity recognition, fall detection, and health monitoring have been revolutionized by advancements in artificial intelligence (AI) and machine learning technologies. These applications can significantly impact health management by monitoring human behavior and providing critical data for health assessments. However, due… Read More »CRoP: A Context-wise Static Personalization Method for Robust and Scalable Human-Sensing AI Models in Healthcare and Real-World Scenarios Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Protein language models (pLMs), trained on protein sequence databases, aim to capture the fitness landscape for property prediction and design tasks. While scaling these models has become common, it assumes that the source databases accurately reflect the fitness landscape, which may not be true.… Read More »AMPLIFY: Leveraging Data Quality Over Scale for Efficient Protein Language Model Development Sana Hassan Artificial Intelligence Category – MarkTechPost