How We Stay Relevant in the Age of AI

AI is changing how people think about work, learning, and human value. Many professionals, parents, students, and community members are asking a very real question: if AI can generate answers, summaries, explanations, drafts, code, and ideas so quickly, where do humans remain relevant?

This session argues that humans should not try to compete with AI at speed, recall, or routine output. That is the wrong competition. As AI makes answers easier to produce, human value moves toward deeper understanding, disciplined curiosity, judgment, and the ability to question, verify, and ground what AI produces.

For most of us, learning has often been treated as natural and implicit. We were taught subjects in school, but not always taught how learning itself works. Machine learning gives us a useful mirror. In order to teach machines how to learn, humans had to make parts of learning explicit: objectives, data, patterns, prediction, feedback, error correction, validation, calibration, and updating.

This session uses those machine learning principles not as a technical lesson, but as a way to think about how humans can learn more deeply in the age of AI. Participants will explore why superficial knowledge is becoming less survivable, why AI still needs to be grounded in human reality, and how disciplined curiosity can help people remain valuable as AI becomes more capable.

The session will include a short lecture-style presentation followed by a guided audience discussion. The discussion can be shaped around a small number of reflection questions, with flexibility to shift into Q&A or a brief example/demo depending on the audience engagement and MPL’s preference. No technical background is required.

By the end of this session, participants will be able to:

1. Explain why shallow learning and routine knowledge work become more vulnerable when AI can generate fluent answers quickly.
2. Apply key machine learning principles, such as objective-setting, feedback, error correction, validation, calibration, and updating, as a practical framework for deeper human learning, better questioning, and stronger judgment.

Bio:
Darren Soo is a Canadian technology founder and creator of PI, an individualized AI reasoning framework designed to help people reason through complex, changing information over time. His background spans more than two decades of practical technology work across enterprise and operational software environments. His current work centres on artificial intelligence, machine learning, and explainable reasoning systems, while his broader interests include how technology can support curiosity, better questions, learning and decision-making.