ML in PL

That's a wrap on this season's recordings. The last batch goes wide: from the metal underneath your models, to ML rewriting how science and engineering work, to what it takes to build AI that generalises beyond data-rich settings. A good one to end on.

🎓 Maciej Draguła & Artem Yerofieiev (Tenstorrent) — All Things Metal
Modern processors spend most of their time and energy waiting for data, and most developers never see it. Tenstorrent builds programmable RISC-V processors with explicit control over data movement, paired with TT-Metal, a low-level API for full hardware control, and TT-Train, a C++ multi-device training engine for transformer workloads. For anyone curious about what sits below PyTorch, this one goes all the way down.

🎓 Johannes Brandstetter — What's the Next Wave of Disruption in Science and Engineering?
From weather and climate modeling to computational fluid dynamics and multi-physics simulation, Johannes connects the dots and argues that scientific ML is past the proof-of-concept stage. The talk focuses on what it takes to build reference models for entire industry verticals and what that means for engineering process cycles.

🎓 Herke van Hoof — Modular Learning for Improving AI Assistants
Most AI success stories require abundant data. Robotics, real-world infrastructure, and scientific domains largely don't have that. Herke makes the case for modular approaches, where complex behaviour is composed from simpler elements, and walks through three projects where modularity improved generalisation, data efficiency, and instructability.

Links in the comments 👇