AI content generation is about feeding your AI models with semantic and contextual information. The result is a platform that can ‘understand’ what an item is, and how it should be used. AI creates content using semantic knowledge in any form of content including video, 3d, VR and more.
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Weight-Only Quantization
Overview This post is divided into five parts; they are: • Asymmetric Affine Quantization • Post-Training Quantization with PyTorch • GPTQ and AWQ • Quantizing a Model to GGUF • Evaluating Weight-Only Quantization Note: The code examples in this post use TorchAO and require a CUDA-enabled PyTorch installation.
Beware the New AI-Fueled Ransomware Boom
Fully automated ransomware hasn’t arrived—yet. But seemingly unsophisticated attackers are using AI tools to wreak havoc.
Robots recognize the shape of an arena by messaging each other
Humans can create loose maps of environments they are exploring in their minds, using their senses and navigation skills. Many robots, on the other hand, rely on set maps of known environments or new maps produced using data collected by their sensors.
The Complete Guide to LLM Evaluation
In this article, you will learn how to evaluate LLM applications systematically, from choosing scorers and designing reliable LLM judges to evaluating RAG systems and…