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 ...

Core Insights - Yoshua Bengio has become the first individual to surpass 1 million citations on Google Scholar, marking a significant milestone in the field of artificial intelligence (AI) research [1][5][7] - The citation growth of Bengio aligns closely with the rise of AI technology from the periphery to the center of global attention over the past two decades [5][7] - Bengio, along with Geoffrey Hinton and Yann LeCun, is recognized as one of the "three giants" of deep learning, collectively awarded the Turing Award for their contributions to computer science [8][47] Citation Milestones - Bengio's citation count reached 1,000,244, with an h-index of 251 and an i10-index of 977, indicating a high level of impact in his published works [1][3] - His most cited paper, "Generative Adversarial Nets," has garnered 104,225 citations since its publication in 2014 [1][22][33] - The second most cited work is the textbook "Deep Learning," co-authored with Hinton and LeCun, which has received over 103,000 citations [1][26][33] Personal Background and Academic Journey - Born in Paris in 1964 to a family with a rich cultural background, Bengio developed an early interest in science fiction and technology [9][10] - He pursued his education at McGill University, obtaining degrees in electrical engineering and computer science, and later conducted postdoctoral research at MIT and AT&T Bell Labs [12][13] - Bengio returned to Montreal in 1993, where he began his influential academic career [12] Contributions to AI and Deep Learning - Bengio has made foundational contributions to AI, particularly in neural networks, during a period known as the "AI winter," when skepticism about the field was prevalent [13][15] - His research has led to significant advancements, including the development of long short-term memory networks (LSTM) and the introduction of word embeddings in natural language processing [18][19] - He has been instrumental in promoting ethical considerations in AI, advocating for responsible development and use of AI technologies [19][27] Ethical Advocacy and Future Vision - As AI technologies rapidly advance, Bengio has expressed concerns about their potential misuse, transitioning from a pure scientist to an active advocate for ethical AI [18][19] - He has participated in drafting ethical guidelines and has called for international regulations to prevent the development of autonomous weapons [19][27] - Bengio emphasizes the importance of ensuring that AI serves humanity positively, drawing inspiration from optimistic visions of the future [18][19][27] Ongoing Research and Influence - At 61, Bengio continues to publish influential research, including recent papers on AI consciousness and safety [36][37][38] - He remains a mentor to emerging researchers, fostering the next generation of talent in the AI field [41] - His legacy is characterized by both groundbreaking scientific contributions and a commitment to ethical considerations in technology [47][48]

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]