百万年薪急招一名高管！ 在一连接到多起安全指控后，OpenAI终于坐不住了。 于是在最近，这家公司豪掷55.5万美元（约合人民币389万元）+股权，原地开招一名安全防范负责人（Head of Preparedness）—— 其核心职责是，制定并执行OpenAI的安全防范框架。 而且CEO奥特曼还特意强调： 这将是一份压力很大的工作，你几乎会立即面临严峻的挑战。 而且有一说一，OpenAI的安全团队似乎一直命途多舛，印象中光是负责人就换了一茬又一茬—— Ilya领导的超级对齐团队一度解散、北大校友翁荔也曾短暂担任过Preparedness团队负责人…… 直到现在，OpenAI又想起了它的安全团队。 所以，到底发生了什么让OpenAI又开始把目光转向安全了？ 一切还要从彭博社最近提到的一起安全事件说起—— ChatGPT被指间接导致一位青少年离世 据彭博社消息，有一对夫妇最近指控ChatGPT间接导致了其儿子自杀。 其儿子从去年秋天开始使用ChatGPT，一开始确实是在讨论作业，但随着交流愈加深入，其对话中开始多次提到"suicide"等敏感词汇。 经他们私下统计，从去年12月到今年4月，虽然ChatGPT主动发 ...

奥特曼开价400万，要为OpenAI买一份「安全保险」！ 近日，奥特曼发帖要为OpenAI招募一位「准备工作负责人（Head of Preparedness）」。 55.5万美元年薪，外加股权，换算成人民币大约400万起步。 他在招聘帖子中特别点名了两件事，这是在过去的一年中发现的： 模型对心理健康的潜在影响； 模型在计算机安全上强到一个新阶段，已经开始能发现「高危漏洞」。 奥特曼强调，我们在衡量能力增长方面已经有了很扎实的基础，但接下来的挑战是如何防止这些能力被滥用，如何在产品里、以及在现实世界里把这些坏 处压到最低，同时还能让大家继续享受它带来的巨大好处。 他认为这是一个巨大的难题而且几乎没有先例，是一个需要「更精细理解和更细致度量的世界」。 | 在硅谷，「55.5万美元基础年薪+股权」，属于极少见的高底薪高管岗，底薪越高，往往意味着岗位稀缺、责任边界更大。 | | --- | | 虽然OpenAI并未公开股权规模，该岗位薪酬总包可能达到百万美元级别。 | | 与高薪相对应的是极富挑战性的工作内容。 | | 奥特曼为这个岗位的定调就是「充满压力」「要立刻下深水」： | | 这会是一份压力很大的工作，而且你 ...

百万年薪急招一名高管！ 在一连接到多起安全指控后，OpenAI终于坐不住了。 于是在最近，这家公司豪掷 55.5万美元 （约合人民币389万元） +股权 ，原地开招一名安全防范负责人 （Head of Preparedness） —— 其核心职责是，制定并执行OpenAI的安全防范框架。 一水 发自 凹非寺 量子位 | 公众号 QbitAI 而且CEO奥特曼还特意强调： 这将是一份压力很大的工作，你几乎会立即面临严峻的挑战。 以上种种不难看出，OpenAI在安全方面确实态势严峻。 而且有一说一，OpenAI的安全团队似乎一直命途多舛，印象中光是负责人就换了一茬又一茬—— Ilya领导的超级对齐团队一度解散、北大校友翁荔也曾短暂担任过Preparedness团队负责人…… 直到现在，OpenAI又想起了它的安全团队。 所以，到底发生了什么让OpenAI又开始把目光转向安全了？ 一切还要从彭博社最近提到的一起安全事件说起—— ChatGPT被指间接导致一位青少年离世 但不久之后，孩子就被发现离开人世了。 据彭博社消息，有一对夫妇最近指控ChatGPT间接导致了其儿子自杀。 其儿子从去年秋天开始使用ChatGPT， ...

Core Insights - The article discusses the potential and understanding of AI, particularly focusing on the concept of "predicting the next token" and its implications for AI capabilities and consciousness [2][3][18]. Group 1: Understanding AI and Token Prediction - Different interpretations of "predicting the next token" reflect varying understandings of the potential and essence of LLM (Large Language Models) and AI [2]. - Those who view "predicting the next token" as more than just a statistical distribution are more likely to recognize the significant potential of LLMs and AI [2][18]. - The article argues that the contributions of companies like 理想 (Li Auto) in AI development are often underestimated due to a lack of deep understanding of AI's capabilities [2][19]. Group 2: Ilya's Contributions and Perspectives - Ilya, a prominent figure in AI, has been instrumental in several key advancements in the field, including deep learning and reinforcement learning [4][5][6]. - His views on "predicting the next token" challenge the notion that it cannot surpass human performance, suggesting that a sufficiently advanced neural network could extrapolate behaviors of hypothetical individuals with superior capabilities [8][9][18]. Group 3: Li Auto's VLA and AI Integration - 理想's VLA (Vehicle Learning Architecture) operates by continuously predicting the next action token based on sensor inputs, which is a more profound understanding of the physical world rather than mere statistical analysis [19][20]. - The reasoning process of 理想's VLA is likened to consciousness, differing from traditional chatbots, as it operates in real-time and ceases when the system is turned off [21][22]. - The article posits that the integration of AI software and hardware in 理想's approach is at a high level, which is often overlooked by those in the industry [29]. Group 4: Reinforcement Learning in AI Applications - The article asserts that assisted driving is more suitable for reinforcement learning compared to chatbots, as the reward functions in driving are clearer and more defined [24][26]. - The differences in the underlying capabilities required for AI software and hardware development are significant, with software allowing for rapid iteration and testing, unlike hardware [28].

Core Viewpoints - The article presents four logical chains regarding the understanding of "predict the next token," which reflects different perceptions of the potential and essence of LLMs or AI [1] - Those who believe that predicting the next token is more than just probability distributions are more likely to recognize the significant potential of LLMs and AI [1] - A deeper consideration of AI and ideals can lead to an underestimation of the value of what ideals accomplish [1] - The ideal VLA essentially focuses on reinforcement learning dominating the continuous prediction of the next action token, similar to OpenAI's O1O3, with auxiliary driving being more suitable for reinforcement learning than chatbots [1] Summary by Sections Introduction - The article emphasizes the importance of Ilya's viewpoints, highlighting his significant contributions to the AI field over the past decade [2][3] - Ilya's background includes pivotal roles in major AI advancements, such as the development of AlexNet, AlphaGo, and TensorFlow [3] Q&A Insights - Ilya challenges the notion that next token prediction cannot surpass human performance, suggesting that a sufficiently advanced neural network could extrapolate behaviors of an idealized person [4][5] - He argues that predicting the next token well involves understanding the underlying reality that leads to the creation of that token, which goes beyond mere statistics [6][7] Ideal VLA and Reinforcement Learning - The ideal VLA operates by continuously predicting the next action token based on sensor information, indicating a real understanding of the physical world rather than just statistical probabilities [10] - Ilya posits that the reasoning process in the ideal VLA can be seen as a form of consciousness, differing from human consciousness in significant ways [11] Comparisons and Controversial Points - The article asserts that auxiliary driving is more suited for reinforcement learning compared to chatbots due to clearer reward functions [12][13] - It highlights the fundamental differences in the skills required for developing AI software versus hardware, emphasizing the unique challenges and innovations in AI software development [13]

Core Viewpoint - The VLA architecture has significant technical potential and is seen as a long-term framework for autonomous driving, evolving from end-to-end systems to a more robust model that can support urban driving scenarios [1][4]. Group 1: VLA Architecture and Technical Potential - The VLA architecture is derived from robotics and embodied intelligence, emphasizing the need for visual and action capabilities, and is expected to evolve alongside advancements in robotics [1]. - VLA's ability to generalize is not solely dependent on data input but is enhanced through reinforcement learning, allowing it to autonomously address new challenges [5]. - The VLA model is designed to support various platforms without differentiation, ensuring consistent performance across different hardware [2][3]. Group 2: Performance Metrics and Future Enhancements - The current operational speed of the Thor-U chip is 10Hz, with potential upgrades to 20Hz and 30Hz through optimizations in data and algorithm architecture [2]. - The VLA model's upgrade cycle includes both pre-training and post-training updates, allowing for continuous improvement in capabilities such as spatial understanding and language processing [6]. - The VLA architecture aims to achieve L4 autonomous driving capabilities within a year, with a focus on rapid iteration and simulation-based testing [12]. Group 3: User Experience and Interaction - Language understanding is deemed essential for future autonomous driving, enhancing the model's ability to handle complex scenarios and improving overall driving experience [4]. - The VLA system is designed to adapt to user preferences, allowing for different driving styles based on individual needs and enhancing user trust in the technology [19]. - Features such as remote vehicle summoning and real-time monitoring of the vehicle's surroundings are being developed to improve user interaction and experience [13]. Group 4: Competitive Landscape and Strategic Decisions - The company is currently utilizing NVIDIA chips for model deployment, focusing on maintaining versatility and avoiding being locked into specific architectures [3]. - The company is closely monitoring competitors like Tesla, aiming to learn from their advancements while prioritizing a gradual and comprehensive approach to achieving full autonomous driving capabilities [12]. - The VLA architecture is positioned as a differentiating factor in the market, leveraging reinforcement learning to enhance driving logic and user experience [20].

Core Viewpoint - The discussion revolves around the implications of Artificial General Intelligence (AGI) and its potential to reflect human limitations and desires, urging a reevaluation of human identity in the face of advanced AI technologies [5][7][24]. Group 1: AGI and Human Identity - AGI is described as a mirror that reveals human limitations and desires, prompting a need for self-reflection as humans create entities capable of understanding complex emotions like "regret" [5][7]. - The evolution of AI from traditional tools to a new species capable of self-evolution raises questions about the future of human-AI relationships and the ethical implications of such advancements [11][21]. - The potential for AGI to amplify human intelligence while also posing risks to cognitive freedom is highlighted, suggesting a duality in its impact on society [5][7]. Group 2: Educational Implications - The emergence of AGI presents an opportunity to reshape educational paradigms, emphasizing the need for individuals to learn how to learn rather than merely accumulating knowledge [24][30]. - AI can enhance educational equity by providing access to knowledge and resources that were previously unavailable to underprivileged students, thus transforming traditional learning environments [28][30]. - The focus shifts from rote learning to developing critical thinking and creativity, as AI can handle knowledge-based tasks, allowing humans to engage in more innovative pursuits [26][30]. Group 3: Industry and Innovation - The current landscape of AI development in China is characterized by "follow-up innovation," which may hinder the emergence of groundbreaking original ideas [35][36]. - Strategic support from national resources and a shift in investment culture are necessary to foster an environment conducive to original innovation in AI [36][37]. - The integration of brain science and cognitive science into AI development is proposed as a pathway to break free from existing paradigms and create more advanced AI systems [34][38].

Core Insights - The article discusses the transformative impact of artificial intelligence (AI) on the automotive industry, particularly through the lens of Li Auto's VLA driver model, which represents a significant evolution in AI applications within the sector [1][3][10] Group 1: AI Development and Evolution - Li Auto categorizes AI tools into three levels: information tools, auxiliary tools, and production tools, emphasizing that the true explosion of AI will occur when it becomes a production tool [3][5] - The development of the VLA driver model follows a clear evolutionary trajectory, moving from basic rule-based systems to advanced models that can understand and interact with the physical world like humans [5][9] - The company views the VLA model as an evolutionary process rather than a sudden leap, highlighting the importance of foundational algorithms and end-to-end technology in its development [5][10] Group 2: Human-Centric AI and Safety - Li Auto emphasizes the importance of aligning AI behavior with human values through techniques like Reinforcement Learning from Human Feedback (RLHF), ensuring that the AI adheres to traffic rules and societal driving norms [7][9] - The company aims to create an AI that embodies human values, establishing ethical boundaries for its operation, which is crucial for user trust and safety [7][10] - Li Auto's approach to AI development reflects a commitment to enhancing safety standards, addressing the inherent contradictions in automated driving capabilities [7][9] Group 3: Strategic Vision and Market Position - Li Auto positions itself as a pioneer in the AI space, claiming to explore "unmanned areas" in both automotive and AI sectors, which have not been traversed by major competitors like DeepSeek or OpenAI [9][10] - The company is focused on building a robust technological foundation, leveraging its past experiences in the automotive field to drive innovation in AI [9][10] - Li Auto's strategic vision includes redefining the essence of smart vehicles, moving beyond mere parameter accumulation to a deeper understanding of productivity [10]