Machine learning

Rivian’s Roadmap to AI Architecture and Autonomy with Founder and CEO RJ Scaringe

Rivian’s Roadmap to AI Architecture and Autonomy with Founder and CEO RJ Scaringe

Rivian CEO RJ Scaringe discusses the company's complete pivot from a rules-based '1.0' autonomy system to a vertically integrated, neural network-based architecture. He outlines the essential ingredients for success in autonomous driving—from custom inference chips to a robust data flywheel—and explains why a software-defined vehicle architecture is non-negotiable for survival. Scaringe also touches on the upcoming R2 model, the importance of market choice, and how superior, proprietary data will be the key differentiator in the age of AI-driven vehicles.

Fuzzy Extractors are Practical

Fuzzy Extractors are Practical

Amey Shukla from the University of Connecticut presents a novel system for biometric key derivation that closes the long-standing gap between the theory and practice of device-level authentication. The talk introduces a practical fuzzy extractor system, "Zeta then Lock," which, combined with an integrated machine learning feature extractor, achieves 105 bits of entropy with a 92% true accept rate for iris biometrics, overcoming the "more errors than entropy" problem that plagued previous designs.

The Future of AI Molecular Discovery

The Future of AI Molecular Discovery

Professor Ellen Zhong discusses the shift from viewing proteins as static objects to dynamic molecular machines. She explores how cryo-electron microscopy (cryo-EM) combined with machine learning creates complex inverse problems to reveal protein motion, moving beyond the "solved" problem of static structure prediction and toward a future of AI-driven scientific discovery.

The ML Technique Every Founder Should Know

The ML Technique Every Founder Should Know

YC Visiting Partner Francois Chaubard and YC General Partner Ankit Gupta break down diffusion, the machine learning framework behind generative AI models like Sora and Midjourney. They discuss its core principles, trace its evolution from complex KL-divergence methods to the elegant simplicity of flow matching, and explore its vast applications beyond images, from protein folding to robotics, arguing it's a key component for future AI systems.

Ideas: Community building, machine learning, and the future of AI

Ideas: Community building, machine learning, and the future of AI

Co-founders Jenn Wortman Vaughan and Hanna Wallach reflect on 20 years of the Women in Machine Learning (WiML) workshop, discussing its origins, their parallel careers in responsible AI, and the future challenges of evaluating generative AI and fostering critical thought.

AI Is Eating Logistics

AI Is Eating Logistics

Ryan Petersen, founder and CEO of Flexport, explains how AI and Machine Learning are being implemented to revolutionize the multi-trillion-dollar logistics industry. He details specific applications, from ML models that optimize container routing to LLM agents that automate communication, and discusses the cultural and strategic shifts required for a large company to embrace AI-driven, bottom-up innovation.