​[[{“value”:” The strong generalization abilities of large-scale vision foundation models have contributed to their amazing performance in various computer vision tasks. These models are quite adaptable since they can handle a number of jobs without requiring a lot of task-specific training. Two-view correspondence, the act… Read More »Evaluating Geometric Awareness in Large-Scale Vision Models for Long-Term Point Tracking Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Artificial intelligence (AI) has advanced rapidly, especially in multi-modal large language models (MLLMs), which integrate visual and textual data for diverse applications. These models are increasingly applied in video analysis, high-resolution image processing, and multi-modal agents. Their capacity to process and understand vast amounts… Read More »LongLLaVA: A Breakthrough Hybrid Architecture Combining Mamba and Transformer Layers to Efficiently Process Large-Scale Multi-Modal Data with Unmatched Accuracy and Performance Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Supervised learning in medical image classification faces challenges due to the scarcity of labeled data, as expert annotations are difficult to obtain. Vision-Language Models (VLMs) address this issue by leveraging visual-text alignment, allowing unsupervised learning, and reducing reliance on labeled data. Pre-training on large… Read More »MedUnA: Efficient Medical Image Classification through Unsupervised Adaptation of Vision-Language Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Thomson Reuters, a global content and technology-driven company, has been using artificial intelligence and machine learning (AI/ML) in its professional information products for decades. The introduction of generative AI provides another opportunity for Thomson Reuters to work with customers and advance how they do… Read More »Scaling Thomson Reuters’ language model research with Amazon SageMaker HyperPod John Duprey AWS Machine Learning Blog

​[[{“value”:” Graph-based methods have become increasingly important in data retrieval and machine learning, particularly in nearest neighbor (NN) search. NN search helps identify data points closest to a given query, which becomes critical with high-dimensional data such as text, images, or audio. Approximate nearest neighbor… Read More »iRangeGraph: A Dynamic Approach for Enhancing Range-Filtering Nearest Neighbor Search Performance Through Efficient Graph Construction and Reduced Memory Footprint in Large-Scale Data Systems Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The release of Reader-LM-0.5B and Reader-LM-1.5B by Jina AI marks a significant milestone in small language model (SLM) technology. These models are designed to solve a unique and specific challenge: converting raw, noisy HTML from the open web into clean markdown format. While seemingly… Read More »Jina AI Released Reader-LM-0.5B and Reader-LM-1.5B: Revolutionizing HTML-to-Markdown Conversion with Multilingual, Long-Context, and Highly Efficient Small Language Models for Web Data Processing Asif Razzaq Artificial Intelligence Category – MarkTechPost

​Two new AI systems, ALOHA Unleashed and DemoStart, help robots learn to perform complex tasks that require dexterous movement Two new AI systems, ALOHA Unleashed and DemoStart, help robots learn to perform complex tasks that require dexterous movement  Read More  

​[[{“value”:” OpenBMB recently released the MiniCPM3-4B, the third-generation model in the MiniCPM series. This model marks a great step forward in the capabilities of smaller-scale language models. Designed to deliver powerful performance with relatively modest resources, the MiniCPM3-4B model demonstrates a range of enhancements over… Read More »MiniCPM3-4B Released by OpenBMB: A Versatile and Efficient Language Model with Advanced Functionality, Extended Context Handling, and Code Generation Capabilities Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” One important tactic for improving large language models’ (LLMs’) capacity for reasoning is the Chain-of-Thought (CoT) paradigm. By encouraging models to divide tasks into intermediate steps, much like humans methodically approach complex problems, CoT improves the problem-solving process. This method has proven to be… Read More »Strategic Chain-of-Thought (SCoT): An Unique AI Method Designed to Refine Large Language Model (LLM) Performance and Reasoning Through Strategy Elicitation Tanya Malhotra Artificial Intelligence Category – MarkTechPost