Episode 41: Beyond Prompt Engineering: Can AI Learn to Set Its Own Goals?

Vanishing Gradients

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Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field.

The panel features:

Ben Taylor (Jepson) – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning.
Joe Reis – Co-founder of Ternary Data and author of Fundamentals of Data Engineering.
Juan Sequeda – Principal Scientist and Head of AI Lab at Data.World, known for his expertise in knowledge graphs and the semantic web.

The discussion unpacks essential topics such as:

The shift from prompt engineering to goal engineering—letting AI iterate toward well-defined objectives.
Whether generative AI is having an electricity moment or more of a blockchain trajectory.
The combinatorial power of AI to explore new solutions, drawing parallels to AlphaZero redefining strategy games.
The POC-to-production gap and why AI projects stall.
Failure modes, hallucinations, and governance risks—and how to mitigate them.
The disconnect between executive optimism and employee workload.

Hugo also mentions his upcoming workshop on escaping Proof-of-Concept Purgatory, which has evolved into a Maven course "Building LLM Applications for Data Scientists and Software Engineers" launching in January. Vanishing Gradient listeners can get 25% off the course (use the code VG25), with $1,000 in Modal compute credits included.

A huge thanks to Dave Scharbach and the Toronto Machine Learning Society for organizing the conference and to the audience for their thoughtful questions.

As we head into the new year, this conversation offers a reality check amidst the growing AI agent hype.

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