Structured data extraction, or data tagging, is one of the easiest and impactful applications of modern AI. Before the wide availability of pre-trained AI models, the process of “understanding” unstructured data either required constructing complex heuristic logic or investing in a machine learning team who could train models in-house. Now, cheap and powerful tagging machines are an API call away, redefining what is possible for how we can understand our data. In this talk, I'll share how we’ve used AI at Yahoo News to improve our content understanding pipelines. Yahoo was a pioneer in...
AI Con USA 2025 - Keynotes
Keynotes are included in all passes! These industry visionaries will take the stage for in-depth discussions on trending topics.
Wednesday, June 11
Best Practices for Using AI for Structured Data Extraction
The Impact of AI on Developer Productivity
Generative AI tools hold promise to increase human productivity. In the world of software development, GitHub Copilot was one of the first practical applications of the use of generative AI to support developer productivity. However, measuring software productivity is non-trivial. For example, developer productivity gains is more than just producing code faster. If these artifacts don’t meet quality standards or themselves bring cost efficiency challenges then there may not be much overall improvement. To truly understand the benefits and challenges of AI-powered copilots requires real-...
AI at Scale: Balancing Innovation, Governance, and Risk in Large Organizations
As AI reshapes industries, large organizations must scale innovation while upholding governance, security, and ethical responsibility. Deploying AI at scale isn’t just a technical challenge—it’s a strategic balancing act between agility and compliance, risk and reward. Andreas Bohman, CIO at the University of Washington, will discuss strategies to drive AI-powered innovation without compromising regulatory obligations, operational effectiveness, or public trust. He will share governance strategies that enable innovation rather than restrict it. He’ll also talk about addressing critical...
AI for Real People—Panel
We have heard a LOT of hype about AI and AGI and ASI and all of the nonsense. But in year 3 of Generative AI, people are actually moving beyond talking and finding real value in AI. Come and hear from REAL AI implementers about how AI is having an impact on businesses across the board including small businesses, non-profits, big companies, and more.
Thursday, June 12
AWS Public Health Modernization: Leveraging GenAI for Government Innovation
Join Venkata Kampana, Senior Solutions Architect from the AWS Health and Human Services team, and Tim Collinson, the CTO of 11:59, an AWS consulting partner, for an insightful discussion on transforming public health systems across federal, state, and local governments. This session will showcase real-world implementations of GenAI and AWS technologies that are revolutionizing public health operations. They will demonstrate innovative solutions, including their IDP implementation utilizing Bedrock's Data Automation (BDA) feature with confidence scoring and bounding box capabilities,...
Five Ways to Operationalize AI at Scale
Enterprises often struggle with how to incorporate AI and machine learning in a repeatable, sustainable manner. In today’s competitive landscape, AI is no longer just a trend but a necessity. Generative AI has quickly become an essential tool for businesses—and with companies expected to explain its usage and justify any lack thereof—organizations are looking for ways to leverage AI at scale. This keynote provides a strategic roadmap to unlock the full potential of AI within organizations, focusing on cultural readiness, enablement, ethical considerations, and addressing biases. Attendees...
The Future of AI Agents
The future of AI agents is rapidly evolving, with increasing importance across diverse sectors. AI agents, defined by their current capabilities, are poised for significant transformation. Several key trends are shaping this future. Advancements in models are enhancing AI agent communication. Agent frameworks are evolving to make AI Agents easier to create and manage. Personalization and adaptability are driving the development of AI agents that learn user preferences and adapt accordingly. Simultaneously, ethical guidelines and responsible AI development are gaining prominence....