Education Track
The NeurIPS 2026 Education Track distills emerging AI ideas, techniques, and paradigms into accessible, reusable educational formats.
As the volume of AI research accelerates, the track offers the community a platform to crystallize complex new concepts into their simplest terms. Contributions explain modern AI advancements with minimal technical jargon, maximizing accessibility for broad audiences.
AI’s nextphase will be shaped by learning from real-world experience. In industry, however, such experience is embedded in proprietary workflows, physical experiments, and decisions with real consequences. Turning such experience into a reliable source of learning requires more than deploying capable models. In this talk, I will describe our efforts to build the industrial learning loop through four connected foundations. 1) Access to training. Frontier model development remains concentrated in a handful of AI companies. To enable others to train their own models, we released an open, end-to-end, frontier-grade pipeline—from cluster setup to inference, including code, weights, corpus, environments, and logs. 2) The hands to build. Industrial data often cannot leave the building, and domain experts should not have to become AI engineers. Our agentic R&D system is designed for in-house development: experts define tasks, constraints, and acceptance criteria in natural language, while agents coordinate experimentation, model post-training, and workflow development. In one deployment, 300 AI agents worked together to post-train a roughly 400-billion-parameter model in two months. 3) Readiness on both sides. General-purpose AI often falls short of industrial requirements, while industrial infrastructure is rarely designed to support iterative learning. To address these challenges, we developed specialized models—from quantum chemistry at the scale of 100,000 atoms to a DNA model 200 times faster than before—alongside autonomous laboratories for high-throughput experimentation with AI models in the loop. 4) Training grounds built on trust. Industrial environments cannot simply be scraped. We therefore designed institutional arrangements in which enterprises define problems and partners share risks, creating incentives to open the loop. Across six enterprise groups and nine joint programs, individual engagements began to form shared infrastructure. Together, these pieces set the industrial learning loop in motion on a materials problem in the semiconductor sector that had resisted progress for decades, delivering a result that met industrial standards and was accepted by domain experts.
Creative AI Session 1
Affinity Support Pilot Program
Barbara Engelhardt
Creative AI Session 2
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