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Under the Hood of NVIDIA and Palantir Turning Enterprise Data into Decision Intelligence On Tuesday, October 28 in Washington, DC, NVIDIA founder and CEO Jensen Huang announced our partnership and how we’ll be making NVIDIA models available through Palantir AIP — and pushing Ontology to the edge through NVIDIA’s accelerated compute. “Palantir and NVIDIA share a vision that …
Generative AI models continue to expand in scale and capability, increasing the demand for faster and more efficient inference. Applications need low latency and consistent performance without compromising output quality. Amazon SageMaker AI introduces new enhancements to its inference optimization toolkit that bring EAGLE based adaptive speculative decoding to more model architectures. These updates make …
Generative AI has created unprecedented opportunities for Canadian organizations to transform their operations and customer experiences. We are excited to announce that customers in Canada can now access advanced foundation models including Anthropic’s Claude Sonnet 4.5 and Claude Haiku 4.5 on Amazon Bedrock through cross-Region inference (CRIS). This post explores how Canadian organizations can use …
Read more “Accelerate generative AI innovation in Canada with Amazon Bedrock cross-Region inference”
Today, we’re excited to announce that Anthropic’s newest model, Claude Opus 4.5, is generally available on Vertex AI. As Anthropic’s most advanced model to date, it excels in coding, agents, vision, computer use and office tasks — at one-third the cost of its predecessor, Opus 4.1. We remain committed to providing Vertex AI customers with …
TL;DR AI is already raising unemployment in knowledge industries, and if AI continues progressing toward AGI, some knowledge-worker categories may indeed reach 100% unemployment because AI will perform these jobs better, faster, and cheaper than humans. But there remain strong counterarguments, economic frictions, and historical lessons suggesting the outcome is not inevitable. As artificial intelligence …
The canonical approach in generative modeling is to split model fitting into two blocks: define first how to sample noise (e.g. Gaussian) and choose next what to do with it (e.g. using a single map or flows). We explore in this work an alternative route that ties sampling and mapping. We find inspiration in moment …
As organizations increasingly adopt AI capabilities across their applications, the need for centralized management, security, and cost control of AI model access is a required step in scaling AI solutions. The Generative AI Gateway on AWS guidance addresses these challenges by providing guidance for a unified gateway that supports multiple AI providers while offering comprehensive …
From uncovering new insights in multimodal data to personalizing customer experiences, AI is emerging as the engine of modern innovation. The explosion in AI adoption has created a need to bring data and AI closer — not only to streamline the AI lifecycle, but also to bring AI-driven insights and workflow automation to everyone in the …
Read more “BigQuery AI: The convergence of data and AI is here”
This paper was accepted at the Learning from Time Series for Health workshop at NeurIPS 2025. Sensor data streams provide valuable information around activities and context for downstream applications, though integrating complementary information can be challenging. We show that large language models (LLMs) can be used for late fusion for activity classification from audio and …
Read more “Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition”
This post is co-written with Hossein Salami and Jwalant Vyas from MSD. In the biopharmaceutical industry, deviations in the manufacturing process are rigorously addressed. Each deviation is thoroughly documented, and its various aspects and potential impacts are closely examined to help ensure drug product quality, patient safety, and compliance. For leading pharmaceutical companies, managing these …