Cursor's Federico Cassano and Fireworks' Dmytro Dzhulgakov detail their collaboration on Composer 2, a specialized foundation model for software engineering. They discuss their top-down training strategy, the infrastructure challenges of large-scale distributed Reinforcement Learning on sparse models, and how model specialization achieves frontier performance with superior efficiency.
Jazmia Henry details the end-to-end process of building specialized foundation models for the energy industry. She covers the four key stages from data curation of unstructured, handwritten documents to optimizing inference, and introduces her Grounded Continuous Evaluation (GCE) framework to combat reward hacking in reinforcement learning.
Isaac Robinson from Roboflow explains why Vision Transformers (ViTs), despite their initial disadvantages in computational complexity and lack of inductive bias, ultimately surpassed Convolutional Neural Networks (CNNs) for computer vision tasks. The talk covers the critical roles of massive, ViT-specific pre-training methods like MAE and DINO, the architectural evolution through models like Swin, ConvNeXt, and Hiera, and optimizations borrowed from the LLM ecosystem. It culminates in a discussion on the practical deployment challenges of large foundation models like SAM and how Neural Architecture Search can bridge the gap.
Dmitri Dolgov, co-CEO of Waymo, discusses the 20-year journey from the DARPA challenge to full autonomy. He explains the Waymo Foundation Model—a multimodal world action model powering the driver, simulator, and critic—and how their "end-to-end plus" architecture enables superhuman safety and exponential scaling.
Sequoia Capital partners argue that AI is a revolution in computation, not just communication. They explain how long-horizon agents are reshaping every layer of work, comparing the current shift to the Industrial Revolution's impact on manual labor and suggesting that innovations that once took a century are now possible in a hundred days.
AI and security experts debate the complex relationship between open source and AI, weighing the foundational role of open source in innovation against the significant security challenges of both proprietary and open models, and exploring the difference between 'secure' and 'securable' systems.