​[[{“value”:” Classical randomness has emerged as an important tool in addressing the challenge of designing quantum protocols and algorithms. Current methods for calibrating and evaluating quantum gates, like randomized benchmarking, depend heavily on classical randomness. Many researchers are exploring ways to incorporate classical randomness to… Read More »MIT Researchers Introduce Stochastic Quantum Signal Processing (QSP) as a Randomly-Compiled Version of QSP, and Reduce the Cost of QSP-based Algorithms by a Factor of 1/2 Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In this post, we discuss how generative artificial intelligence (AI) can help health insurance plan members get the information they need. Many health insurance plan beneficiaries find it challenging to navigate through the complex member portals provided by their insurance plans. These portals often… Read More »How healthcare payers and plans can empower members with generative AI Sachin Jain AWS Machine Learning Blog

​[[{“value”:” Constructing Knowledge Graphs (KGs) from unstructured data is a complex task due to the difficulties of extracting and structuring meaningful information from raw text. Unstructured data often contains unresolved or duplicated entities and inconsistent relationships, which complicates its transformation into a coherent knowledge graph.… Read More »How Can We Convert Unstructured Text into Actionable Knowledge? This AI Paper Unveils iText2KG for Incremental Knowledge Graphs Construction Using Large Language Models Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” As generative AI moves from proofs of concept (POCs) to production, we’re seeing a massive shift in how businesses and consumers interact with data, information—and each other. In what we consider “Act 1” of the generative AI story, we saw previously unimaginable amounts of… Read More »Enabling production-grade generative AI: New capabilities lower costs, streamline production, and boost security Baskar Sridharan AWS Machine Learning Blog

​[[{“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