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
[MiniMax-H3] Subtle expressions and natural pauses without any prompting
There are plenty of videos around with characters that look like AI or have plastic skin or talk like a robot. What about emotions, or subtle facial expressions, or natural pauses? The solutions and advice usually offered include complex prompt guides or access to paid platforms. I don’t like that.…
Evaluating Graph-RAG vs. Standard RAG: A Hallucination Benchmark on Fact-Dense Queries
In this article, you will learn how to benchmark a deterministic 3-Tiered Graph-RAG system against a standard vector RAG pipeline on fact-dense queries, and what…
Normalizing Trajectory Models
Diffusion-based models decompose sampling into many small Gaussian denoising steps, an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood framework in the process. We introduce Normalizing Trajectory Models (NTM),…
Pay-per-inference for AI agents: How BlockRun and Incarna use Amazon Bedrock AgentCore payments
When an AI agent runs, it often needs to buy something to finish a task: a model inference, an API response, access web content, or a call to another agent. These purchases are small and frequent, sometimes a fraction of a cent each, and they happen inside the agent’s loop…