Core Viewpoint - The departure of Yann LeCun from Meta is seen as a significant event in the AI industry, highlighting a clash between traditional deep learning approaches and the emerging dominance of large language models (LLMs) [1][29]. Group 1: Yann LeCun's Position - Yann LeCun is recognized as a pivotal figure in AI, often referred to as the "father of convolutional neural networks" (CNNs), and has been celebrated for his contributions over the past 40 years [3][10]. - Despite his accolades, there are criticisms regarding the originality of his work, with claims that he has appropriated ideas from earlier researchers without proper acknowledgment [10][28]. - LeCun's recent criticism of LLMs, which he describes as a "dead end," contrasts sharply with Meta's aggressive investment in this technology [31][45]. Group 2: Gary Marcus's Critique - Gary Marcus, a prominent critic of deep learning, argues that LeCun's contributions have been overstated and that he has misled the AI community regarding the capabilities of CNNs and LLMs [5][8]. - Marcus emphasizes the need for a hybrid approach that combines neural networks with symbolic reasoning, which he believes is essential for achieving true artificial general intelligence (AGI) [8][28]. - He accuses LeCun of being a "public relations creation" rather than a solitary genius, suggesting that his achievements are built on the foundations laid by others [10][28]. Group 3: Industry Implications - The ongoing debate between LeCun and Marcus reflects broader tensions within the AI community regarding the future direction of AI research and development [6][29]. - LeCun's potential departure from Meta to pursue his vision of "world models" indicates a shift towards alternative AI methodologies that prioritize understanding over mere data processing [31][47]. - The competition between traditional AI paradigms and newer models like LLMs is likely to shape the future landscape of the industry, influencing funding, research focus, and technological advancements [30][48].

编辑：冷猫、陈陈 实现通用人工智能的一大终极目标就是创建能够自我学习，自我改进的人工智能体。 这个目标已经是老生常谈了。其实在 2003 年，能够自我改进的智能体的理论模型就已经由著名的「现代 AI 之父」Jürgen Schmidhuber 提出，称为哥德尔机。 哥德尔机是一种自我改进型通用智能系统理论模型，设计灵感来自于哥德尔（Kurt Gödel）的不完备性定理。它的核心思想是：机器能够像数学家一样，通过形式 证明自身程序的改进在长期内将带来更高收益，然后安全地修改自己。 机器之心报道 Jürgen Schmidhuber 是一名德国计算机科学家，以人工智能、深度学习和人工神经网络领域的成就而知名，现任达勒・莫尔人工智能研究所（IDSIA）联合主任， 阿卜杜拉国王科技大学人工智能研究院院长。 1997 年，Jürgen Schmidhuber 发表了长短期记忆网络（LSTM）论文。2011 年，Jürgen Schmidhuber 在 IDSIA 的团队 GPU 上实现了卷积神经网络（CNN）的显著加 速，这种方法基于杨立昆等人早期提出的 CNN 设计 ，已成为计算机视觉领域的核心。 通俗来说，就是一个 ...

Group 1 - The core viewpoint of the article revolves around the potential of AI to surpass human intelligence and the associated risks, as articulated by Geoffrey Hinton during the World Artificial Intelligence Conference (WAIC) [1][4][6] - Hinton emphasizes the need for a global effort to address the dangers posed by AI, suggesting that nations should collaborate on AI safety and training [5][6] - The article highlights Hinton's historical contributions to AI, particularly his development of the AlexNet algorithm, which revolutionized deep learning [5][6] Group 2 - Hinton discusses the evolution of AI over the past 60 years, identifying two main paradigms: symbolic logic and biologically inspired approaches [3][4] - He expresses concerns about the rapid advancement of AI technologies, estimating a 10% to 20% probability that AI could potentially threaten human civilization [6] - Hinton advocates for allocating significant computational resources towards ensuring AI systems align with human intentions, criticizing tech companies for prioritizing profit over safety [6]

Group 1 - The market cannot be precisely predicted, and the goal is to build a cognitive framework to understand its current state and infer short-term evolution [1] - Traditional technical analysis attempts to reduce the complexity of market processes but often overlooks the high-dimensional latent space that drives price movements [1] Group 2 - Early deep learning models like CNNs capture local spatial patterns but fail to understand the path dependency of time series data [2] - LSTM and its variants address the limitations of CNNs by capturing sequential dependencies, but they assume a linear flow of information, which does not reflect the complex interactions in real markets [3] Group 3 - A paradigm shift is needed from sequential dependency modeling to spatio-temporal structural dependency modeling to better capture market dynamics [5] - The core of the proposed approach is a dynamic temporal knowledge graph that models relationships among entities, which is essential for understanding market interactions [6] Group 4 - The use of heterogeneous Hawkes processes allows for modeling event flows within the knowledge graph, capturing the ripple effects of market events [6] - By maximizing the log-likelihood function, the system can derive embedding vectors for entities and relationships, projecting the knowledge graph into a lower-dimensional latent space [7] Group 5 - The model's output is a posterior probability that combines likelihood from data and prior probability based on human insights, emphasizing the importance of human judgment in the decision-making process [9][10] - The company aims to create a decision framework that optimizes long-term expected value rather than focusing on short-term gains, leveraging the cognitive spread between its insights and market averages [11]