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”
The American version of Temu abruptly began to show only “local” products days before the Trump administration was set to end a tariff loophole for small packages from China.
Generative AI continues to rapidly evolve, reshaping how industries create, operate, and engage with users.
Fine-tuning remains a cornerstone technique for adapting general-purpose pre-trained large language models (LLMs) models (also called foundation models) to serve more specialized, high-value downstream tasks, even as zero- and few-shot methods gain traction.
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 …
Read more “Data Streaming: Real-time data for real-time decisions”
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 …
UiPath’s agent orchestration layer Maestro moves prompts through three layers: the agent, a human and the robotic process automation system.Read More
A US judge says Apple deliberately chose not to comply with an order requiring it to loosen App Store rules—then tried to cover up its disobedience.
Fish are masters of coordinated motion. Schools of fish have no leader, yet individuals manage to stay in formation, avoid collisions, and respond with liquid flexibility to changes in their environment. Reproducing this combination of robustness and flexibility has been a long-standing challenge for human-engineered systems like robots.