Zhang Bo (CAS): Three Key Requirements for Intelligent Agents <|eot_id|><|start_header_id|>assistant<|end_header_id|>
26 April 2025 路 Uncategorized 路
Source: 路 https://www.cnr.cn/tech/techds/20250402/t20250402_527121094.shtml
Result
During the '2025 Zhongguancun Forum Annual Meeting' on March 31, Academician Zhang Bo of the Chinese Academy of Sciences addressed the Zhipu OpenDay event, stating that artificial intelligence (AI) is transitioning into an era of 'intelligent agents.' He advocated for extending language models to encompass these agents, emphasizing the need for robust thinking, execution, and perception capabilities.
Zhang Bo highlighted two key implications of large-scale AI models: a fundamental shift in the AI paradigm and inevitable transformation across various industries. This impact arises from their ability to enable machines to think more like humans, a capability facilitated by the vast neural networks and extensive data learning employed in models like GPT, resulting in powerful language generation.
The speaker further explored methods for enhancing model performance, including improving machine reasoning abilities and utilizing AI feedback reinforcement learning (RLAIF), with a particular focus on human feedback reinforcement learning (RLHF). He underscored the importance of evolving language models into intelligent agents and identified three major challenges currently facing AI: hierarchical space problems, the distinction between process and result, and the need for greater explainability.
Concluding his remarks, Zhang Bo suggested that AI companies should continuously reflect and adapt to better align with contemporary business practices.
During the '2025 Zhongguancun Forum Annual Meeting' on March 31, Academician Zhang Bo of the Chinese Academy of Sciences addressed the Zhipu OpenDay event, stating that artificial intelligence (AI) is transitioning into an era of 'intelligent agents.' He advocated for extending language models to encompass these agents, emphasizing the need for robust thinking, execution, and perception capabilities.
Zhang Bo highlighted two key implications of large-scale AI models: a fundamental shift in the AI paradigm and inevitable transformation across various industries. This impact arises from their ability to enable machines to think more like humans, a capability facilitated by the vast neural networks and extensive data learning employed in models like GPT, resulting in powerful language generation.
The speaker further explored methods for enhancing model performance, including improving machine reasoning abilities and utilizing AI feedback reinforcement learning (RLAIF), with a particular focus on human feedback reinforcement learning (RLHF). He underscored the importance of evolving language models into intelligent agents and identified three major challenges currently facing AI: hierarchical space problems, the distinction between process and result, and the need for greater explainability.
Concluding his remarks, Zhang Bo suggested that AI companies should continuously reflect and adapt to better align with contemporary business practices.