Hangyu Mao
Head of R&D, Knowledge Enhancement, FTI Big Model, Racer Technology
He is now working in FTI, the R&D leader of Knowledge Enhancement for FTI Big Models, and also the leader of Intelligent Interaction Team. He mainly focuses on Agent, RAG, Alignment, RL, LLM, etc. He has published more than 30 papers in ICLR and NeurIPS, ICML and other CCF-A/B conferences and journals, and has applied for more than ten international and domestic patents, and the related research has landed on the ground in enterprise scenarios and produced large benefits. He has served as PC, Senior PC, Area Chair of the above international conferences, Forum Chair of China Conference on Data Mining (CCDM), and Executive Member of CCF Multi-Intelligence Group. He and his team have won the ‘Typical Case of AI Large Model Scenario Application’ at the Global Digital Economy Conference, the champion of NeurIPS Reinforcement Learning Competition at the International Conference on Artificial Intelligence, the ‘Outstanding Doctoral Dissertation in Multi-Intelligence Research’ at the China Computer Federation, the ‘Outstanding Doctoral Dissertation in Multi-Intelligence Research’ at Beijing Municipal Government, and the ‘Excellent Dissertation in Multi-Intelligence Research’ at Beijing Municipality. ‘D. Thesis Award for Multi-Intelligence Research of the Chinese Computer Society, Outstanding Graduate of Beijing Municipality, and Huawei Innovative Pioneer Presidential Award.
Topic
From Reinforcement Learning (Multi)Intelligentsia to Large Language Modelling (Multi)Intelligentsia
Big Language Models represented by ChatGPT is one of the hottest research hotspots in the field of Artificial Intelligence, and AI Agent is one of the most promising ways to apply Big Language Models. This report takes Intelligent Body (Agent) as a clue, firstly combing the research of Decision Intelligent Body and Multi-Intelligent Body based on Reinforcement Learning, secondly introducing the research of AI Agent and AI Agents based on Big Language Models, and lastly sharing some insights from enterprise practice without disclosing the company's secrets.
