From Chips to Thoughts: Building Physical Intelligence into Robotic Systems Daniela Rus

📋 论文概要

该论文探讨了如何将物理智能集成到机器人系统中，从芯片设计到高级认知，旨在解决AI系统的高能耗问题，并实现更高效、自主的机器人。

💡 主要创新点

• 提出了从芯片到思想的物理智能架构，强调硬件与算法的协同优化。
• 展示了在机器人系统中实现高效AI的路径，包括能源管理和实时处理。

🏷 关键词

📄 原文摘要

1. Introduction The rapid growth of AI technologies has brought unprecedented advancements across numerous domains, from healthcare to autonomous systems, yet this progress has been accompanied by substantial energy demands. Generative AI has revolutionized the accessibility of advanced machine learning models, putting powerful AI capabilities directly into the hands of everyday users. Tools that generate text, images, video, and audio have made AI more democratic, fueling creativity, productivity, and innovation across industries. This explosion of generative AI applications, from chatbots to art generators, has transformed AI from a niche tool into an indispensable asset in our digital lives, as many can now access these technologies through their phones or computers. However, this widespread accessibility comes with significant trade-offs, particularly in terms of the energy required to train and deploy these enormous models. Large generative models such as GPT,

👥 作者与机构

Director, CSAIL & Andrew and Erna Viterbi Professor,

Massachusetts Institute of Technology, Cambridge, MA