Predicting material failure: Machine learning spots early abnormal grain growth signs for safer designs

by AI Generated Robotic ContentAI/ML NewsPosted on Comments are Disabled

A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of stronger, more reliable materials for high-stress environments, such as combustion engines. A paper describing their novel machine learning method was recently published in Nature Computational Materials.

FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations

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This paper was accepted at the Workshop on Foundation Models in the Wild at ICLR 2025. Visual understanding is inherently contextual – what we focus on in an image depends on the task at hand. For instance, given an image of a person holding a bouquet of flowers, we may focus on either the person …

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Clario enhances the quality of the clinical trial documentation process with Amazon Bedrock

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This post is co-written with Kim Nguyen and Shyam Banuprakash from Clario. Clario is a leading provider of endpoint data solutions to the clinical trials industry, generating high-quality clinical evidence for life sciences companies seeking to bring new therapies to patients. Since Clario’s founding more than 50 years ago, the company’s endpoint data solutions have …

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Generating and Visualizing Context Vectors in Transformers

by AI Generated Robotic ContentAI/ML ResearchPosted on Comments are Disabled

This post is divided into three parts; they are: • Understanding Context Vectors • Visualizing Context Vectors from Different Layers • Visualizing Attention Patterns Unlike traditional word embeddings (such as Word2Vec or GloVe), which assign a fixed vector to each word regardless of context, transformer models generate dynamic representations that depend on surrounding words.