Continuous learning

Apr 28, 2026

AI Infrastructure, Ray, and Why Nonlinear Careers Win — with Linda Haviv

Linda Haviv discusses the modern AI landscape, emphasizing that non-linear career paths and systems thinking are now more valuable than pure coding skills. She explores how open-source technology, like the Ray framework, is democratizing AI development and closing the gap with proprietary models, and why building a personal brand through content creation is essential for career growth and community building in a rapidly evolving industry.

Mar 23, 2026

Will machines ever be intelligent?

Doug Burger, Nicolò Fusi, and Subutai Ahmad explore the intelligence of AI, contrasting transformer-based LLMs with the human brain's distributed, continuously learning architecture. They delve into differences in efficiency, representation, and sensory-motor grounding, debating what intelligence truly means and how future AI might bridge the gap.

Mar 05, 2026

AI That Learns While You Use It

Sudip Roy, Co-founder & CTO of Adaption Labs, discusses how "Adaptation" using gradient-free, inference-time techniques can solve the last 5% reliability gap that stalls enterprise AI adoption, offering a more dynamic and cost-effective alternative to traditional fine-tuning or simply waiting for the next frontier model.

Aug 27, 2025

Too much lock-in for too little gain: agent frameworks are a dead-end // Valliappa Lakshmanan

Lak Lakshmanan presents a robust architecture for building production-quality, framework-agnostic agentic systems. He advocates for using simple, composable GenAI patterns, off-the-shelf tools for governance, and a strong emphasis on a human-in-the-loop design to create continuously learning systems that avoid vendor lock-in.