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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Latest Artificial Intelligence Content News
Structured Outputs vs. Function Calling: Which Should Your Agent Use?
Language models (LMs), at their core, are text-in and text-out systems.
Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models at ICLR 2026. Large language models (LLMs) can struggle to memorize factual knowledge in their parameters, often leading to hallucinations and poor performance on knowledge-intensive tasks. In this paper, we formalize fact memorization from…
How to build effective reward functions with AWS Lambda for Amazon Nova model customization
Building effective reward functions can help you customize Amazon Nova models to your specific needs, with AWS Lambda providing the scalable, cost-effective foundation. Lambda’s serverless architecture lets you focus on defining quality criteria while it handles the computational infrastructure. Amazon Nova offers multiple customization approaches, with Reinforcement fine-tuning (RFT) standing out…
How to find the sweet spot between cost and performance
At Google Cloud, we often see customers asking themselves: “How can we manage our generative AI costs effectively without sacrificing the performance and availability our applications demand?” This is the million-dollar question — or, perhaps more accurately, the “tokens-per-minute” question. The key isn’t just about choosing the cheapest option, but…