Sharing slides from Architecting Intelligence Talk #3, which I presented today to a Bay Area study group of AI researchers and engineers.

The talk covers:

• World Models (2018): Vision, predictive memory, and training inside a learned “dream.”
• DreamerV3: Learning behavior through imagined trajectories.
• The JEPA family: From image representations to video learning and action-conditioned planning.

The slides walk through the architectures, key experiments, and limitations.

📄 Presentation PDF: https://drive.google.com/file/d/1Mx7RwEAK3sMYQ45HuLKMud8jMnj1qLvQ/view?usp=sharing

Where do you see world models being useful beyond robotics and games? Would love to hear examples from systems you’re building.

This is a community for understanding how language models work—and exploring how to build better ones.

Bring your paper discussions, architecture deep dives, experiments, implementation questions, and open research ideas. Topics include Transformers, MoE, reasoning, training, alignment, inference, and architectures beyond Transformers.

A few expectations:

• Go deep, but stay welcoming. Questions at every level are welcome.
• Support claims with evidence. Share papers, code, or results when possible.
• Challenge ideas respectfully. Disagreement helps us learn.
• Keep promotion useful. Explain what your work contributes and disclose your connection.

You don’t need a finished project to contribute—a thoughtful question or failed experiment can start a great discussion.