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
Tool Calling vs. Code Execution for AI Agents: Choosing the Right Action Primitive
Theory is easier to trust once it’s running against a real API, so both examples in this article use the same tool — a get_weather function backed by
Trading a Cloud Identity for Your Own: Workload Attestation on Managed Compute
By Dhruv Pratap Introduction Organizations that have been around for a while usually run two identity systems side by side. One belongs to the cloud provider: IAM roles, instance profiles, execution roles. The other is your own, and it is the one your internal services actually check when they decide whether…
Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput
When you post-train a Mixture-of-Experts (MoE) model with Reinforcement Learning from Human Feedback (RLHF) or Group Relative Policy Optimization (GRPO) at scale, three simultaneous challenges emerge. The first requires coordinating heterogeneous compute for rollout generation and policy training. Second, sustaining high-throughput communication across hundreds of accelerators. And third, dynamically orchestrating…
Best practices guide for customizing Gemini models via Reinforcement Learning (RL)
Reinforcement learning (RL) has been a keystone of modern LLM post-training, but it demands large training clusters and access to model internals that external customers can’t have with proprietary models like Gemini. So here at Google Cloud, we packaged it into a managed RL fine-tuning service (RLFT service) — you…