李飞飞的答案：大模型之后，Agent向何处去？

Core Insights - The article discusses the rising prominence of Agent AI, with 2025 being viewed as a pivotal year for this technology [1][2] - A significant paper led by Fei-Fei Li titled "Agent AI: Surveying the Horizons of Multimodal Interaction" has sparked extensive discussion in the industry [3][6] Summary by Sections Overview of the Paper - The paper, consisting of 80 pages, provides a clear framework for the somewhat chaotic field of Agent AI, integrating various technological strands into a new multimodal perspective [5][6] - It emphasizes the evolution from large models to agents, reflecting the current strategies of major players like Google, OpenAI, and Microsoft [6] New Paradigm of Agent AI - The paper introduces a novel cognitive architecture for Agent AI, which is not merely a compilation of existing technologies but a forward-thinking approach to the development of Artificial General Intelligence (AGI) [9] - It defines five core modules: Environment and Perception, Cognition, Action, Learning, and Memory, which together form an interactive cognitive loop [10][26] Core Modules Explained - Environment and Perception: Agents actively perceive information from their surroundings in a multimodal manner, incorporating various data types [12][13] - Cognition: Acts as the processing center for agents, enabling complex activities such as reasoning and empathy [15][16] - Action: Converts cognitive decisions into specific operational commands, affecting both physical and virtual environments [18][19] - Learning: Highlights the continuous learning and self-evolution capabilities of agents through various mechanisms [20][21] - Memory: Offers a structured system for long-term knowledge retention, allowing agents to leverage past experiences for new tasks [23][24] Role of Large Models - The framework's feasibility is attributed to the maturity of large foundational models, particularly LLMs and VLMs, which provide essential cognitive capabilities for agents [28][29] - These models enable agents to decompose vague instructions into actionable tasks, significantly reducing the complexity of task programming [30][31] Challenges and Ethical Considerations - The paper identifies the issue of "hallucination" in models, where they may generate inaccurate content, posing risks in real-world interactions [32][33] - It emphasizes the need for inclusivity in designing Agent AI, addressing biases in training data and ensuring ethical interactions [36][39] - The importance of establishing regulatory frameworks for data privacy and security in Agent AI applications is also highlighted [38][39] Application Potential - The paper explores the vast application potential of Agent AI in gaming, robotics, and healthcare [40] - In gaming, Agent AI can create dynamic NPCs that interact meaningfully with players, enhancing immersion [42][43] - In robotics, agents can autonomously execute complex tasks based on simple verbal commands, streamlining user interaction [48][49] - In healthcare, Agent AI can assist in preliminary diagnostics and patient monitoring, improving efficiency in resource-limited settings [54][57] Future Directions - The paper acknowledges that Agent AI is still in its early stages, facing challenges in integrating multiple modalities and creating general-purpose agents [58][60] - It proposes new evaluation benchmarks to measure agent intelligence and guide future research [61]