Lessons from Two Decades of AI
Micheal Lanham has been building intelligent systems since the early 2000s, starting with neural networks and evolutionary algorithms in games and moving through enterprise software, geoscience, AR/VR, and now AI agents. Over the years, he has written more than ten technical books and worked across industries as an architect, manager, and hands-on AI engineer.
In this conversation, Micheal shares hard-earned lessons from two decades at the intersection of data, software, and AI. We’ll explore what games can teach us about intelligence, why evolutionary methods are resurfacing, and how to think about AI agents beyond the hype.
We plan to cover:
What games can teach us about AI and data products
The promise (and limits) of evolutionary deep learning
AI agents in practice: beyond LLMs and prompts
How XR and intelligent systems are converging
What it takes to productionize AI across industries
What has really changed in AI over the last 20 years
About the speaker
Micheal Lanham is a best-selling author, innovator, and AI engineer based in Calgary, Canada. His work spans games, graphics, GIS, enterprise software, and machine learning. He has published over ten technical books, including Evolutionary Deep Learning, Hands-On Reinforcement Learning for Games, and AI Agents in Action. Micheal has worked as a lead AI developer, architect, and manager across industries from oil and gas to fintech, and today focuses on building intelligent systems with deep reinforcement learning, evolutionary methods, and generative AI.
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