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Reasoning and planning are the bedrock of intelligent AI systems, enabling them to plan, interact, adapt, and ultimately, operate independently. At Apple, understanding and advancing reasoning capablilities in AI systems has long been an area of active research, and has resulted in numerous publications that both explore new techniques to advance the frontier of reasoning, …
Avneesh Saluja, Santiago Castro, Bowei Yan, Ashish Rastogi Introduction Netflix’s core mission is to connect millions of members around the world with stories they’ll love. This requires not just an incredible catalog, but also a deep, machine-level understanding of every piece of content in that catalog, from the biggest blockbusters to the most niche documentaries. As …
Read more “MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix”
Critical labor shortages are constraining growth across manufacturing, logistics, construction, and agriculture. The problem is particularly acute in construction: nearly 500,000 positions remain unfilled in the United States, with 40% of the current workforce approaching retirement within the decade. These workforce limitations result in delayed projects, escalating costs, and deferred development plans. To address these …
Read more “Scaling data annotation using vision-language models to power physical AI systems”
In 2025, Amazon SageMaker AI saw dramatic improvements to core infrastructure offerings along four dimensions: capacity, price performance, observability, and usability. In this series of posts, we discuss these various improvements and their benefits. In Part 1, we discuss capacity improvements with the launch of Flexible Training Plans. We also describe improvements to price performance …
As artificial intelligence and machine learning (AI/ML) workflows grow in scale and complexity, it becomes harder for practitioners to organize and deploy their models. AI projects often struggle to move from pilot to production. AI projects often fail not because models are bad, but because infrastructure and processes are fragmented and brittle, and the original …
Read more “Build AI workflows on Amazon EKS with Union.ai and Flyte”
Nearly every snowboard trick carries a number. A 1080 means three full rotations. A 1440 means four. The convention is simple: add up every rotation around every axis and count in 180° increments. For decades it’s served as the sport’s universal shorthand for difficulty. Judges, coaches, and athletes all speak this language fluently. It’s also, …
Read more “Using Google Cloud AI to measure the physics of U.S. freestyle snowboarding and skiing”
Query Auto-Completion (QAC) is a critical feature of modern search systems that improves search efficiency by suggesting completions as users type. However, existing approaches face fundamental challenges: traditional retrieve-and-rank pipelines have poor long-tail coverage and require extensive feature engineering, while recent generative methods suffer from hallucination and safety risks. We present a unified framework that …
Building cohesive and unified customer intelligence across your organization starts with reducing the friction your sales representatives face when toggling between Salesforce, support tickets, and Amazon Redshift. A sales representative preparing for a customer meeting might spend hours clicking through several different dashboards—product recommendations, engagement metrics, revenue analytics, etc. – before developing a complete picture …
Read more “Build unified intelligence with Amazon Bedrock AgentCore”
For developers building AI applications, including custom agents and chatbots, the open-source Model Context Protocol (MCP) standard enables your innovations to access data and tools consistently and securely. At the end of 2025, we introduced managed and remote MCP support for services like Google Maps and BigQuery, establishing a standard method for AI to connect …
Read more “Powering the next generation of agents with Google Cloud databases”
How can we trust the correctness of a learned model on a particular input of interest? Model accuracy is typically measured on average over a distribution of inputs, giving no guarantee for any fixed input. This paper proposes a theoretically-founded solution to this problem: to train Self-Proving models that prove the correctness of their output …