The release of Anthropic's Mythos-class Claude Fable 5 is the latest signal that we are in a phase of exponential growth in AI capabilities, "takeoff mode". The biggest leaps are in engineering and scientific reasoning: frontier models now match or exceed expert-level performance on many technical tasks, and increasingly act as collaborators that plan, code, simulate, and design (as we show in our own research on self-improving agentic discovery systems).
Understand these technologies deeply is critically, both the foundations and how they're applied to critical industrial problems at scale, and to use them to drive innovation and technology development.
This July 27–30, I will teach Applied AI for Materials Discovery at
@MITProfessional (live online so you can participate from anywhere). It's a hands-on deep dive into the shift from predictive ML to agentic, closed-loop AI-native discovery and innovation. Highlights:
▶ AI scientists & recursive self-improving swarm intelligence: massively parallel agents that read literature, formulate hypotheses, write and run code, and critique each other's work
▶ Generative AI for inverse design: diffusion and flow matching for proteins, alloys, metamaterials, and crystals
▶ Foundation models that "think" physics: graph transformers, neural interatomic potentials, neural operators and PINNs
▶ Bridging the reality gap across scales: connecting atomic-scale agents to physics simulators and product-scale (DFT, MD, FEA) for automated verification of AI-generated designs
▶ Building custom reasoning models: fine-tuning, RL; incorporating first-principles physical agency (e.g. MCP, tool use)
▶ Unlocking dormant knowledge: turning unstructured data (papers, patents, lab notebooks, legacy PDFs, etc.) into structured, actionable insight
▶ Interpretability, reliability, and enterprise deployment
The course will provide you with ready-to-use agent templates, dozens of code notebooks, repos, and curated datasets you can deploy immediately in your organization.
More details on the course and registration link, see reply.
ALT The biggest leaps in AI are in engineering and scientific reasoning: frontier models now match or exceed expert-level performance on many technical tasks, and increasingly act as collaborators that plan, code, simulate, and design (as we show in our own research on self-improving agentic discovery systems). Understand these technologies deeply is critically, both the foundations and how they're applied to critical industrial problems at scale, and to use them to drive innovation and technology development.