According to Jeff Dean (@JeffDean), both Gemini Flash and Waymo are leveraging knowledge distillation, as detailed in the research paper arxiv.org/abs/1503.02531, to create high-quality, computationally efficient AI models from larger-scale, more resource-intensive models. This process allows companies to deploy advanced machine learning models with reduced computational requirements, making it feasible to run these models on resource-constrained hardware such as autonomous vehicles. For businesses, this trend highlights a growing opportunity to optimize AI deployment costs and expand the use cases for edge AI, particularly in industries like automotive and mobile devices (source: twitter.com/JeffDean/status/1998453396001657217).

According to @GoogleDeepMind, Waymo's AI has achieved significant advancements by simulating billions of miles in various driving scenarios and weather conditions, in addition to real-world testing. This large-scale data enables Waymo’s self-driving technology to handle rare and complex events, improving autonomous vehicle safety and reliability. The podcast discussion with Waymo's Distinguished Engineer Vincent Vanhoucke highlights the use of 3D world modeling, multimodal models, and advanced sensor technologies, positioning Waymo as a leader in scalable, safe autonomous driving solutions (Source: @GoogleDeepMind).