We present KNOW--the Knowledge Navigator Ontology for the World--the first ontology designed to capture everyday knowledge to augment large language models (LLMs) in real-world generative AI use cases such as personal AI assistants. Our domain is human life, both its everyday concerns and its major milestones. We have limited the initial scope of the modeled concepts to only established human universals: spacetime (places, events) plus social (people, groups, organizations). The inclusion criteria for modeled concepts are pragmatic, beginning with universality and utility. We compare and contrast previous work such as Schema.org and Cyc--as well as attempts at a synthesis of knowledge graphs and language models--noting how LLMs already encode internally much of the commonsense tacit knowledge that took decades to capture in the Cyc project. We also make available code-generated software libraries for the 12 most popular programming languages, enabling the direct use of ontology concepts in software engineering. We emphasize simplicity and developer experience in promoting AI interoperability.

But true data intelligence is about more than establishing the right data foundation. Organizations are also wrestling with how to overcome dependence on highly technical staff and create frameworks for data privacy and organizational control when using generative AI. Specifically, they are looking to enable all employees to use natural language to glean actionable insight from the company's own data; to leverage that data at scale to train, build, deploy, and tune their own secure large language models (LLMs); and to infuse intelligence about the company's data into every business process. In this next frontier of data intelligence, organizations will maximize value by democratizing AI while differentiating through their people, processes, and technology within their industry context. Based on a global, cross-industry survey of 600 technology leaders as well as in-depth interviews with technology leaders, this report explores the foundations being built and leveraged across industries to democratize data and AI.

Talk #0: Introductions & Meetup Announcements By Chris Fregly, Principal Solution Architect & Antje Barth, Principal Developer Advocates, AI & machine learning @ AWS Talk #1: Amazon DataZone & Data Mesh By Joel Farvault, Principal Solutions Architect @ AWS Data & Analytics Use Amazon DataZone to share, search, & discover data at scale across organizational boundaries. Collaborate on data projects through a unified data analytics portal that gives you a personalized view of all your data while enforcing your governance & compliance policies. Talk #2: Generative AI use cases & deep-dive By Arun Shankar, Sr. Solution Architect @ AWS AI/ML I will provide guidance from how to best showcase our SageMaker Generative AI playground or try-out experience (e.g., prompt engineering guidance), training/ fine tuning capabilities available, selecting instances & real-world deployment considerations in production. We will cover: Guided Generative AI demos, git-hub examples & technical Q&A for public & proprietary models in SageMaker. Differences between various types of learning with these large language models, natural language understanding & generation tasks they can solve & what are the some of the common use cases that are aligned with these tasks.