We investigate the theoretical foundations of classifier-free guidance (CFG). CFG is the dominant method of conditional sampling for text-to-image diffusion models, yet unlike other aspects of diffusion, it remains on shaky theoretical footing. In this paper, we disprove common misconceptions, by showing that CFG interacts differently with DDPM (Ho et al., 2020) and DDIM (Song …
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With Amazon Bedrock Evaluations, you can evaluate foundation models (FMs) and Retrieval Augmented Generation (RAG) systems, whether hosted on Amazon Bedrock or another model or RAG system hosted elsewhere, including Amazon Bedrock Knowledge Bases or multi-cloud and on-premises deployments. We recently announced the general availability of the large language model (LLM)-as-a-judge technique in model evaluation …
Read more “Use custom metrics to evaluate your generative AI application with Amazon Bedrock”
Across industries, enterprises need efficient and proactive solutions. Imagine frontline professionals using voice commands and visual input to diagnose issues, access vital information, and initiate processes in real-time. The Gemini 2.0 Flash Live API empowers developers to create next-generation, agentic industry applications. This API extends these capabilities to complex industrial operations. Unlike solutions relying on …
Read more “Build live voice-driven agentic applications with Vertex AI Gemini Live API”
When’s the last time you watched a race for the braking? It’s the heart-pounding acceleration and death-defying maneuvers that keep most motorsport fans on the edge of their seats. Especially when it comes to Formula E — and really all EVs — the explosive, near-instantaneous acceleration of an electric motor is part of the appeal. …
Read more “Pushing the limits of electric mobility: Formula E’s Mountain Recharge”
The Amazon Bedrock multi-agent collaboration feature gives developers the flexibility to create and coordinate multiple AI agents, each specialized for specific tasks, to work together efficiently on complex business processes. This enables seamless handling of sophisticated workflows through agent cooperation. This post aims to demonstrate the application of multiple specialized agents within the Amazon Bedrock …
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Your customers might not all speak the same language. If you operate internationally or serve a diverse customer base, you need your chatbot to meet them where they are – whether they’re searching for something in Spanish or Japanese. If you want to give your customers multilingual support with chatbots, you’ll need to orchestrate multiple …
Pan-privacy was proposed by Dwork et al. (2010) as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system’s internal state. Motivated by federated telemetry applications, we study local pan-privacy, where privacy should be retained under repeated unannounced intrusions on the local state. …
Multimodal fine-tuning represents a powerful approach for customizing foundation models (FMs) to excel at specific tasks that involve both visual and textual information. Although base multimodal models offer impressive general capabilities, they often fall short when faced with specialized visual tasks, domain-specific content, or particular output formatting requirements. Fine-tuning addresses these limitations by adapting models …
Read more “Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock”
Data Streaming: Real-time data for real-time decisions (Palantir RFx Blog Series, #8) Often the most business critical decisions are also the most time-sensitive. Data streaming technologies let organizations act on information (almost) as quickly as it comes in. Editor’s note: This is the eighth post in the Palantir RFx Blog Series, which explores how organizations can better …
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Amazon Bedrock Model Distillation is generally available, and it addresses the fundamental challenge many organizations face when deploying generative AI: how to maintain high performance while reducing costs and latency. This technique transfers knowledge from larger, more capable foundation models (FMs) that act as teachers to smaller, more efficient models (students), creating specialized models that …