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For 2026, Machine Learning Week returns to San Francisco, as HYBRID AI 2026. This is MLW’s 18th year, and perhaps its most important.
AI is on the cusp of greatness. The bad news is that positive returns are still few and far between – begging the question, when will AI finally achieve its greatness? The good news is that the final mile to more universally realized value is in sight.
THE PROBLEM: How can practitioners get genAI pilots to production – and get predictive AI from development to deployment – considering that the success rates are still extremely low?
THE SOLUTION:
1) Hybrid AI. GenAI and Predictive AI are destined to marry because each is suited to address the other’s greatest limitations: GenAI is often unreliable, while predictive AI is hard to use.
2) A reliability layer to tame LLMs. This layer must feature:
- i) Continually expanding guardrails
- ii) Strategically embedded humans in the loop – indefinitely
- iii) Form-fitted customization for each project
Why hybrid AI? The reliability layer demands a strategic hybridization of methods – such as predictive AI and genAI – as well as the strategic embedding of humans-in-the-loop (human/machine collaboration is also sometimes called “hybrid AI”).
The most ideal way to soften the AI bubble’s looming detonation would be to boost AI’s realized value. To this end, developing a reliability layer is a critical, emerging discipline. It’s vital for establishing system robustness that would make AI pilots ready for production. And it’s a fruitful way to test the very limits of LLMs, exploring and expanding the feasibility of industry’s ever-increasing AI ambitions.
Come to HYBRID AI 2026 to turn AI’s potential into realized value – by discovering best practices that make AI products robust and deployment-worthy.
Venue: The Clift Royal Sonesta, downtown San Francisco.
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