Core Viewpoint - The article discusses the influential contributions of Andrej Karpathy in the AI field, particularly his role in coining significant terms and concepts that have shaped the industry, such as "hallucinations," "Software 2.0," "Software 3.0," "vibe coding," and "bacterial coding" [1][6][9]. Group 1: Naming and Concepts - Karpathy coined the term "hallucinations" to describe the limitations of neural networks, which generate meaningless content when faced with unfamiliar concepts [1][3]. - He is recognized as a master of naming in the AI community, having introduced terms like "Software 2.0" and "Software 3.0," which have gained traction over the years [6][9]. - The act of naming is emphasized as a foundational behavior in knowledge creation, serving as a stable target for global scientific focus [7]. Group 2: Software Evolution - "Software 1.0" refers to traditional programming where explicit instructions are written in languages like Python and C++ [12][14]. - "Software 2.0" represents a shift to neural networks, where developers train models using datasets instead of writing explicit rules [15]. - "Software 3.0" allows users to generate code through simple English prompts, making programming accessible to non-developers [16][17]. Group 3: Innovative Programming Approaches - "Vibe coding" encourages developers to immerse themselves in the development atmosphere, relying on LLMs to generate code based on verbal requests [22][24]. - "Bacterial coding" promotes writing modular, self-contained code that can be easily shared and reused, inspired by the adaptability of bacterial genomes [30][35]. - Karpathy suggests balancing the flexibility of bacterial coding with the structured approach of eukaryotic coding to support complex system development [38]. Group 4: Context Engineering - Context engineering has gained attention as a more comprehensive approach than prompt engineering, focusing on providing structured context for AI applications [43][44]. - The article highlights a shift towards optimizing documentation for AI readability, indicating a trend where 99.9% of content may be processed by AI in the future [45].

Group 1 - The exploration of dreams has evolved from ancient beliefs to modern neuroscience, indicating that dreams may have significant connections to human thought, memory, and creativity [1][3][17] - REM sleep, discovered in 1953, is characterized by high brain activity similar to wakefulness, where most vivid dreams occur [3][4][6] - Dreams are not random; they are closely linked to daily experiences, emotions, and memories, facilitated by the brain's complex neural networks, particularly the Default Mode Network (DMN) [11][12][13] Group 2 - During REM sleep, the brain processes and reorganizes memories, often amplifying emotional experiences, which explains the intense feelings associated with dreams [12][13] - The brain's activity during REM sleep involves various regions, such as the visual cortex for imagery and the limbic system for emotions, while the prefrontal cortex's activity is suppressed, leading to illogical dream narratives [9][10][11] - Dreams may serve as a means of emotional regulation, helping individuals cope with stress and anxiety by reprocessing emotional memories [12][13] Group 3 - The similarities between dreams and hallucinations suggest a shared neurobiological basis, particularly in conditions like schizophrenia, where individuals may struggle to distinguish between reality and their internal perceptions [14][15] - Lucid dreaming, where individuals maintain self-awareness, may offer therapeutic potential for those experiencing hallucinations, allowing them to better control their experiences [16] - The ongoing research into dreams not only seeks to unravel their mysteries but also aims to address fundamental questions about consciousness and reality [17]

北邮网安团队 投稿 量子位 | 公众号 QbitAI 风险缺口：长链CoT放大"误差滚雪球" 推理大模型（RLLMs）能把复杂问题拆解成几十步推理，再给出看似缜密的结论。然而，随着推理链条变长，一个令人不安的趋势浮出水面 —— 错误不再是偶发失误，而是沿链条滚雪球式放大 。 在医疗、金融、法律等高风险场景，一次细小偏差就可能酿成灾难。 当推理链从3步延伸到50+步，幻觉率暴增10倍；反思节点也束手无策。 遗憾的是，当前安全评估几乎都停留在结果级：判定答案对错、衡量毒性与否，犹如"考试只看最后分数"。 这种做法忽视了一个关键问题： 错误到底是如何在链内生根、扩散并固化的？ 如果无法洞察这一机制，就难以对症下药。 北京邮电大学的研究团队为解决这一问题，采取了以下方法： 来自北京邮电大学的研究团队通过 思维链审计实验 ，首次定量揭示了这一"越想越错"现象背后的元认知偏差： 长链推理中的反思不是纠错机制，而是给幻觉颁发"理性证书"—— 模型为保持与用户提示语义一致， 宁可篡改协议定义也不否定前提 。 首先 基于RFC协议文档构建受控知识域 ，再让模型生成 30–60步 的长链推理，并在关键节点插入reflection ...

Core Viewpoint - The accuracy of data is crucial for financial services companies utilizing Generative AI (GenAI) and Large Language Models (LLM), as inaccurate or low-quality data can adversely affect company strategy, operations, risk management, and compliance [1][3]. Group 1: Causes of Data Inaccuracy - Data inaccuracy in the financial services sector often arises from multiple factors, including the increasing volume and variety of data sourced from multiple vendors, patents, and third-party sources [4]. - "Hallucination" is a significant challenge in the financial sector regarding Generative AI, where models generate coherent but factually incorrect or misleading information due to their reliance on learned patterns from training data without factual verification [4]. Group 2: Importance of Retrieval-Augmented Generation (RAG) - RAG is a critical technology for improving the accuracy of Generative AI and significantly reducing hallucinations by integrating real data with generated responses [6]. - RAG combines the generative capabilities of LLMs with effective data retrieval systems, allowing for more accurate and contextually relevant answers, especially in financial risk assessments [6]. - RAG enhances the utilization of various data formats, enabling the processing of both structured and unstructured data efficiently, and connects existing legacy systems without the need for costly migrations or retraining of LLMs [7]. Group 3: Benefits of RAG - RAG helps address the main causes of data inaccuracy discussed earlier, providing more accurate answers based on proprietary data and reducing hallucinations [8]. - It allows for the integration of the latest knowledge and user permission management, ensuring that responses are based on up-to-date information [8].