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The Spectral Neuron is a proposed ML primitive based on the largest eigenvalue of an input-conditioned matrix. The preprint develops its mathematical properties, initialization and training methods, and evaluates scalability on synthetic and real-world datasets.
Skanska has signed a $1.2 billion contract to build four data centres for an unnamed existing customer in the southeastern United States. The facilities will cover approximately 75,000 square metres, or 808,000 square feet.
ByteDance’s Seed foundation-model division has reportedly completed another internal restructuring. Its foundation-model organization now includes four first-level departments focused on pretraining data, reinforcement learning, product post-training for work, and product post-training for chat.
Stecon Group Pcl plans to bid for three data-center construction projects valued at a combined 49.8 billion baht, or about $1.5 billion. The company expects Thailand’s accelerating technology investment to drive further growth.
Apple Machine Learning presents a lexical-intervention approach for improving cross-lingual knowledge transfer when target-language data is scarce. The work targets downstream capabilities such as scientific reasoning, commonsense inference, and world knowledge without relying heavily on parallel data, translation systems, or auxiliary models.
Apple Machine Learning studies how to mix scarce target-domain data with abundant generic data during language-model pretraining. Across more than 2,000 training runs, the research examines the trade-off between insufficient target exposure, repeated examples, diminishing returns, and overfitting.
OpenAI is seeking to compete more directly with Anthropic by introducing stronger privacy protections for enterprise customers. The two companies are increasingly competing over how securely customer data is handled.
AWS demonstrates how a mid-size mortgage lender automates document intake from email through classification, extraction, validation, and final data processing. The workflow combines the AWS GAIIC IDP Accelerator with Amazon Quick Automate.
An MIT study finds that AI-generated images often cannot be traced to individual training images. As datasets grow larger, the influence of any specific example tends to fade.
NVIDIA FLARE enables organizations to coordinate training for vision-language models while keeping sensitive image and text data at local institutions. The approach supports multimodal use cases such as visual question answering, captioning, and image-text reasoning without centralizing raw records.