【人工智能】AI智能体工作流 | Agentic Reasoning | 吴恩达Andrew Ng | 红杉AI Ascent 2024分享 | Agent 4大设计模式

Summary

In the third installment of the Sequoia Capital AI Summit, Professor Andrew Ng, a renowned figure in AI education, shared insights on 'AI agentic workflow,' emphasizing the iterative process that leads to superior results compared to non-agentic workflows. Ng highlighted the importance of rapid token generation and outlined four AI design patterns: Reflection, Tool Use, Planning, and Multiagent Collaboration. He demonstrated how these patterns can enhance AI's capabilities, from self-correction to complex task execution and collaboration among multiple AI entities. Ng's presentation underscored the potential for AI to expand the scope of tasks it can perform this year and the necessity for patience in allowing AI systems to iterate and improve their outputs. He concluded by likening the journey to artificial general intelligence to a long path, where agentic workflows represent significant strides towards that goal.

Takeaways

• 📈 **Iterative AI Workflows:** Andrew Ng emphasized the effectiveness of iterative workflows for AI agents, comparing it to the process of writing an article with the ability to revise and iterate, leading to better results.
• 🤖 **AI Agentic Workflow:** Ng introduced the concept of AI agentic workflow, which involves multiple steps such as outlining, drafting, revising, and iterating, similar to how humans approach complex tasks.
• 📝 **Programming Task Analysis:** Using HumanEval programming assessment benchmarks, Ng's team analyzed how AI performs on programming tasks. They found that an iterative approach can significantly improve the accuracy of code generation.
• 🔍 **Four AI Design Patterns:** Ng categorized AI design into four patterns: Reflection, Tool Use, Planning, and Multiagent Collaboration, each serving different functions and applications in AI tasks.
• 🤓 **Reflection in AI:** Reflection allows AI to review and improve its own output, which can lead to the discovery and correction of errors, and the generation of more efficient code.
• 🛠️ **Tool Use in AI:** Tool use involves AI systems utilizing various tools for tasks like code generation, API calls, and data analysis, expanding the capabilities of large language models.
• 📈 **Planning for Complex Tasks:** Planning enables AI to break down complex tasks and execute them in a structured manner, which can be surprisingly effective when it works well.
• 🤝 **Multiagent Collaboration:** Multiple AI agents can collaborate to perform tasks, with each agent playing a different role, such as a CEO or software engineer, leading to complex and sometimes impressive outcomes.
• ⏱️ **Speed of Token Generation:** Ng highlighted the importance of fast token generation for AI models, suggesting that a faster, lower-quality model can outperform a slower, higher-quality one due to the increased number of iterations.
• 🚀 **Potential for AI Expansion:** Ng predicted a significant expansion in the types of tasks AI can perform this year, largely due to the adoption of agentic workflows and rapid iteration.
• 🌟 **Patience with AI:** He also mentioned the need for patience when working with AI, as immediate results are not always the best approach, and allowing AI time to process and respond can lead to better outcomes.
• ⛓️ **AI as a Collaborative System:** Ng concluded by suggesting that AI can move beyond being a single-task tool to a collaborative system that can handle complex problems and workflows, which is a significant step towards artificial general intelligence.

Q & A

What is the main topic of discussion in the third session of the Sequoia Capital AI Summit?

The main topic of discussion in the third session is Professor Andrew Ng's (吴恩达) sharing on the workflow of intelligent agents, including the iterative model of intelligent agent workflows and the analysis of the effects based on artificial benchmarks.

What is the term used to describe the iterative process of AI development as discussed by Professor Ng?

The term used is 'AI agentic workflow', which refers to the iterative process where an AI agent performs tasks, evaluates the results, and refines its approach accordingly.

How does Professor Ng categorize the design patterns of AI intelligent agents?

Professor Ng categorizes the design patterns of AI intelligent agents into four types: Reflection, Tool Use, Planning, and Multiagent Collaboration.

What is the significance of using an iterative approach with AI, as opposed to a one-time prompt-response model?

The iterative approach allows for refinement and improvement of the AI's output over multiple cycles, leading to better results compared to a one-time prompt-response model, which is akin to writing an article without the ability to revise.

How does the use of an iterative process with AI models affect the accuracy of programming tasks?

Using an iterative process can significantly improve the accuracy of programming tasks. For instance, GPT-3.5's accuracy increased when using an intelligent agent workflow, even surpassing GPT-4 in some cases.

What is the role of 'Reflection' in the design pattern of AI intelligent agents?

Reflection involves the AI agent examining and revising its own output. It allows the AI to identify issues in its generated code or response and suggest improvements, leading to more accurate and efficient outcomes.

How does 'Tool Use' expand the capabilities of large language models?

'Tool Use' involves the AI agent performing actions such as generating code, calling APIs, or manipulating images. This expands the capabilities of large language models by allowing them to interact with various tools and perform practical operations.

What is the concept of 'Planning' in the context of AI intelligent agents?

'Planning' refers to the AI agent's ability to break down complex tasks into smaller, manageable steps and execute them in a planned sequence, which can lead to more effective problem-solving.

Can you explain the 'Multiagent Collaboration' design pattern?

'Multiagent Collaboration' involves multiple AI agents working together to accomplish a task, each playing a different role. This collaborative approach can simulate a real-world work environment and lead to more diverse and effective solutions.

What is the importance of rapid token generation in the context of AI intelligent agents?

Rapid token generation is crucial because it allows AI agents to iterate more quickly, which can lead to better results. Even if the quality of the language model is slightly lower, the speed of iteration can compensate and lead to more refined outputs.

What is Professor Ng's outlook on the potential of AI intelligent agents in expanding the scope of tasks AI can perform?

Professor Ng is optimistic about the potential of AI intelligent agents. He believes that through intelligent agent workflows, the variety of tasks that AI can perform will significantly expand, and these workflows can bring results close to what might be expected from a future model like GPT-5.

How does Professor Ng describe the journey towards artificial general intelligence (AGI) in relation to intelligent agents?

Professor Ng describes the journey towards AGI as a long-term endeavor rather than a final destination. He believes that intelligent agents can help make small but significant strides on this journey.