作者 | 论文推土机 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1948137034842611877 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 Diffusion 扩散模型整理 本文整理diffusion的数学原理，只有少量微分方程，随机微分方程和概率相关的公式，所以只需要基础数学背景也能完全看懂。如果实在看不懂也没关系，关注高亮块的 结论即可。快速阅读办法就是只看高亮块然后接受结论即可。更快速的阅读办法是只看第一章朗之万采样建立对diffusion的直观印象即可。总结来说：相对论就是和美女 在一起时间短，加班的时候时间长；diffusion就是用网络学习怎么解常微分/随机微分方程。 本文分成五部分内容： 首先我们整理与diffusion model相关的各个基础概念，这部分的整理都是数学定义，主要来自以下链接： [An Introduction to Flow Matching and Diffusion ...

Core Viewpoint - The article discusses the advancements and challenges in the development of the VLA (Visual-Language-Action) model for autonomous driving, emphasizing the importance of reinforcement learning and the integration of 3D spatial understanding with global semantic comprehension. Group 1: VLA Model Development - The VLA model incorporates reinforcement learning, which is crucial for its development and performance [1] - The integration of 3D spatial understanding and global semantic comprehension enhances the model's capabilities compared to previous versions [7] - The transition from VLM (Visual-Language Model) to VLA involves a shift from parallel to a more integrated architecture, allowing for deeper cognitive processing [3][4] Group 2: Technical Challenges - The deployment of the VLA model faces challenges such as multi-modal alignment, data training difficulties, and the complexity of deploying on a single chip [8][9] - The model's performance is expected to improve significantly with advancements in chip technology and optimization techniques [9][10] - The need for extensive data labeling and the potential for overfitting in simulation data are highlighted as ongoing concerns [23][32] Group 3: Industry Comparisons - The article compares the gradual approach of the company in advancing from L2 to L4 autonomous driving with the rapid expansion strategies of competitors like Tesla [11] - The company aims to provide a more comprehensive driving experience by focusing on user needs and safety, rather than solely on technological capabilities [11][22] Group 4: Future Directions - The company plans to enhance the VLA model's capabilities through continuous iteration and integration of user feedback, aiming for a more personalized driving experience [35] - The importance of regulatory compliance and collaboration with government bodies in advancing autonomous driving technology is emphasized [17][18]

Core Viewpoint - The article discusses the advancements and future potential of video generation models, emphasizing the need for deeper understanding capabilities in visual AI, moving beyond mere generation to true comprehension [1][5][4]. Group 1: Video Generation Models - The team at Jumpscale has open-sourced two significant video generation models: Step-Video-T2V and Step-Video-TI2V, both with 30 billion parameters, which have garnered considerable attention in the AI video generation field [1][12]. - Current diffusion video models, even at 30 billion parameters, show limited generalization capabilities compared to language models, but possess strong memory capabilities [5][26]. - The future of video generation models may involve a shift from mere generation to models that possess deep visual understanding, requiring a change in learning paradigms from mapping learning to causal prediction learning [5][20]. Group 2: Challenges and Innovations - The article outlines six major challenges in AI-generated content (AIGC), focusing on data quality, efficiency, controllability, and the need for high-quality data [39][32]. - The integration of autoregressive and diffusion models is seen as a promising direction for enhancing video generation and understanding capabilities [21][20]. - The importance of high-quality, diverse natural data is highlighted as a critical factor in building robust foundational models, rather than relying heavily on synthetic data [14][16]. Group 3: Future Predictions - Predictions indicate that foundational visual models with deeper understanding capabilities may emerge within the next 1-2 years, potentially leading to a "GPT-3 moment" in the visual domain [4][36]. - The convergence of video generation with embodied intelligence and robotics is anticipated, providing essential visual understanding capabilities for future AI applications [37][42]. - The article suggests that the future of AIGC will enable individuals to easily create high-quality content, democratizing content creation [38][48].