这是辛顿首次访问中国并进行演讲。77岁的辛顿长期受腰椎间盘疾病的困扰，身体欠佳的他几乎无法坐飞机。 谷歌团队曾为邀请他去英国考察DeepMind团队特地包下私人飞机，并改造了座椅。 辛顿还在WAIC开幕前一天参加了第四届人工智能国际安全对话(International Dialogues on Al Safety,IDAIS)，并 与20余名人工智能行业专家联名签署发布了《AI安全国际对话上海共识》。 在最新的演讲中，他谈及"数字智能是否会取代生物智能"的问题，并讨论了AI可能带来的挑战与潜在的应对方 法。辛顿此前已多次在公开信和演讲中指出，当前AI系统已经具备自主学习和演化的潜能。 辛顿指出，在过去60多年里，AI发展存在两种不同的范式和路径--以符号型的逻辑性范式以及以生物为基础的 范式。1985年，辛顿做了一个小模型，尝试结合这两种理论，以解释人们如何理解词汇。 "我认为，如今的大语言模型就是我当年微型语言模型的衍生。"他表示，"它们使用更多词作为输入，采用更 多层的神经元结构，由于需要处理大量模糊数字，学习特征之间也建立了更复杂的交互模式。但和我做的小模 型一样，大语言模型理解语言的方式与人类相似-- ...

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]