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]