What's next for AI agentic workflows ft. Andrew Ng of AI Fund

Sequoia Capital
26 Mar 202413:40

TLDRThe transcript discusses the evolution of AI agents and their impact on computer science, highlighting the importance of iterative, agentic workflows in enhancing AI performance. It presents four key design patterns for AI agents: reflection, multi-agent collaboration, planning, and multi-agent debate, emphasizing their potential to boost productivity and innovate in various applications. The speaker also underscores the necessity of adjusting our expectations for AI response times and the promising future of AI development through these agentic reasoning design patterns.

Takeaways

  • 🧠 The importance of neural networks and GPUs in AI development, with Andrew Ng's contributions in the field through his work on Coursera and deeplearning.ai.
  • 👨‍🏫 The evolution from non-agentic to agentic workflows in AI, where the latter involves iterative processes similar to how humans write and revise essays.
  • 📈 The study comparing the performance of zero-shot prompting versus agentic workflows, showing that the latter can lead to significantly better results in tasks such as coding.
  • 🤖 The concept of 'agents' in AI and the potential for them to transform the future of technology, with a focus on four broad design patterns: reflection, multi-agent collaboration, planning, and multi-agent debate.
  • 🔄 The agentic workflow's ability to outperform even more advanced models like GPT-4 when applied correctly, highlighting the significance of the approach over the model itself.
  • 💡 The use of self-reflection in AI agents to improve code quality by having the agent review and revise its own work, demonstrating a level of autonomous improvement.
  • 👥 The potential of multi-agent systems, where different agents can take on various roles (e.g., coder, reviewer, CEO) and collaborate to solve complex problems.
  • 🛠️ The application of planning algorithms in AI, allowing agents to autonomously navigate around failures and achieve goals in a more human-like manner.
  • 📚 The recommendation for further reading and exploration into AI technologies and design patterns, emphasizing the value of continuous learning in the field.
  • 🚀 The anticipation of rapid advancements in AI capabilities due to agentic reasoning design patterns, and the potential for these to bring us closer to achieving complex tasks and possibly contribute to the path towards AGI (Artificial General Intelligence).

Q & A

  • What is the main focus of the transcript?

    -The main focus of the transcript is the discussion of AI agents and their potential in transforming the way we interact with and develop AI technologies, particularly through the use of agentic workflows and various design patterns.

  • Who is the speaker referring to at the beginning of the transcript?

    -The speaker is referring to Andrew Ng, a renowned computer science professor at Stanford University, known for his early work in neural networks with GPUs, the creator of Coursera, and the founder of Google Brain.

  • What is the significance of problem set number two of CS229 mentioned in the transcript?

    -The significance of problem set number two of CS229 is that it represents a personal anecdote where the speaker is asking Andrew Ng about a past assignment, highlighting the long-standing impact of Ng's teachings on AI and machine learning.

  • What are the two main types of workflows for using language models mentioned in the transcript?

    -The two main types of workflows mentioned are non-agentic workflow, where the AI generates a response to a prompt without further interaction, and agentic workflow, which involves a more iterative process where the AI revises and improves its output through multiple interactions.

  • How does the speaker describe the results of using an agentic workflow with GPT-3.5?

    -The speaker describes that using an agentic workflow with GPT-3.5 results in better performance than using GPT-4 with zero-shot prompting, indicating that the agentic workflow can enhance the capabilities of AI models.

  • What are the four broad design patterns the speaker mentions for agents?

    -The four broad design patterns for agents mentioned are reflection, planning, multi-agent collaboration, and two-use (using LM-based systems for various tasks such as analysis, information gathering, and action taking).

  • How does the speaker suggest we should adapt to agentic workflows?

    -The speaker suggests that we should learn to be patient and allocate more time for AI agents to process and respond in agentic workflows, as it can lead to better results compared to expecting immediate responses like in non-agentic workflows.

  • What is the significance of fast token generation in the context of agentic workflows?

    -Fast token generation is significant in agentic workflows because it allows the AI to generate responses at a much faster pace than a human could read, which is beneficial for iterative processes where multiple revisions and refinements are needed.

  • What does the speaker predict for the future of AI in relation to agentic workflows?

    -The speaker predicts that the capabilities of AI, particularly in terms of what AI could do, will expand dramatically due to the adoption of agentic workflows. They also suggest that these workflows could help us make progress towards AGI (Artificial General Intelligence).

  • What is the role of multi-agent collaboration in the design patterns discussed?

    -Multi-agent collaboration is a design pattern where multiple AI agents with different roles or perspectives work together on a task. This approach can lead to better performance and more complex problem-solving, as seen in the example of the multi-agent system where different agents act as CEO, designer, product manager, and tester.

  • How does the speaker view the role of planning algorithms in AI agents?

    -The speaker views planning algorithms as a crucial component in AI agents, enabling them to autonomously navigate around failures and perform complex tasks. They highlight the ability of AI agents to plan and execute tasks in a way that can lead to surprising and impressive results.

Outlines

00:00

🤖 Introduction to AI Agents and their Impact

The speaker begins by acknowledging Andreu's contributions to computer science, particularly in neural networks and AI, and introduces the concept of AI agents. The speaker highlights the importance of iterative, agentic workflows in AI development, contrasting them with non-agentic methods. They share their experience with using agentic workflows in their team and present a case study showing the effectiveness of using GPT-3.5 with an agentic approach over a non-agentic one. The speaker emphasizes the significance of this trend in AI and looks forward to discussing various design patterns related to agents.

05:01

📚 Overview of Design Patterns in AI Agents

The speaker delves into the design patterns observed in AI agents, noting the chaotic and rapidly evolving nature of the field. They categorize the patterns into four main types: reflection, multi-agent collaboration, planning, and multi-agent systems. The speaker provides examples for each pattern, such as using a single LM as a coder and a critic, or employing multiple agents with different roles to collaborate on tasks. They also mention the importance of fast token generation for iterative agent workflows and express excitement for upcoming AI models that could further enhance these patterns.

10:04

🚀 The Future of AI Agents and the Path to AGI

In the final paragraph, the speaker discusses the potential of AI agents to significantly improve productivity and reshape the way we interact with AI systems. They stress the need for patience and dedication when working with AI agents, as the iterative process may require more time than traditional methods. The speaker also touches on the importance of fast token generation for the efficiency of agent workflows. They conclude by expressing optimism about the progress towards AGI (Artificial General Intelligence) through the continued development and refinement of agentic reasoning design patterns.

Mindmap

Keywords

💡Neural Networks with GPUs

Neural Networks with GPUs refer to the use of graphical processing units to train and operate artificial neural networks. GPUs are particularly effective for the complex mathematical computations required in deep learning, as they can process large amounts of data simultaneously. In the context of the video, this technology is significant because it has been a foundational aspect of advancements in AI, enabling more sophisticated and powerful AI models like those discussed in the presentation.

💡Coursera

Coursera is an online learning platform that offers courses from universities and institutions worldwide. It provides a platform for both students and educators to engage in a variety of subjects. In the video, Coursera is mentioned as the platform where Andrew has created popular courses like 'deeplearning.ai', indicating its role in democratizing AI education and making it accessible to a broader audience.

💡Google Brain

Google Brain is a research project by Google that focuses on the development of advanced artificial intelligence systems. It involves the collaboration of researchers and engineers to push the boundaries of AI, particularly in deep learning and neural networks. The mention of Google Brain in the video underscores the speaker's involvement in cutting-edge AI research and development, as well as the collaborative nature of such projects.

💡AI Agents

AI Agents, as discussed in the video, are systems designed to interact with users or other software components in a more dynamic and autonomous way than traditional AI models. They can perform tasks, make decisions, and learn from interactions, often through iterative processes. The concept is central to the video's theme, as it represents the next step in AI evolution, where systems are not just processing information but actively engaging in tasks and improving over time.

💡Non-Agentic Workflow

A non-agentic workflow refers to a process where an AI model, like a language model, performs a task in a one-way, non-interactive manner. This is akin to the traditional approach where users input a prompt and the AI generates an output without any back-and-forth interaction. The video contrasts this with agentic workflows, which are more iterative and interactive, leading to better results.

💡Iterative Workflow

An iterative workflow is a process that involves repeating tasks with the intention of improving outcomes over successive cycles. In the context of AI, this means engaging in a back-and-forth interaction with the AI system to refine and improve results. This approach is contrasted with non-agentic workflows in the video, where the iterative workflow allows for more nuanced and higher-quality outputs from AI systems.

💡Human Eval Benchmark

The Human Eval Benchmark is a coding challenge or test designed to evaluate the performance of AI systems, particularly in coding tasks. It provides a standard measure to compare the capabilities of different AI models in solving problems that would typically be done by human programmers. In the video, the benchmark is used to demonstrate the effectiveness of agentic workflows in improving the performance of AI models like GPT-3.5.

💡Design Patterns

In the context of the video, design patterns refer to reusable solutions to common problems in software and AI development. These patterns provide a structured approach to building AI systems, particularly AI agents, that can be applied in various scenarios to improve efficiency and effectiveness. The video discusses several design patterns for AI agents, emphasizing their importance in advancing AI capabilities.

💡Reflection

In the context of AI, reflection refers to a process where an AI system reviews and evaluates its own output or actions, much like self-reflection in humans. This concept is used as a design pattern in AI agents to improve their performance by having them check their own work for correctness and efficiency. The video highlights reflection as a powerful tool for enhancing AI systems, as it allows for self-improvement and learning.

💡Multi-Agent Collaboration

Multi-agent collaboration refers to a system where multiple AI agents work together to achieve a common goal. Each agent may have different roles or responsibilities, and they interact with each other to complete tasks more effectively. This concept is significant in the video as it showcases the potential for AI systems to not only operate independently but also to collaborate and leverage collective intelligence.

💡Two-Use Tools

Two-use tools, as mentioned in the video, are AI systems or models that can perform both analysis and action, expanding the capabilities of language models (LMs). These tools enable LMs to not only generate output but also to interact with other systems, gather information, and take actions, making them more versatile and useful in a wider range of applications.

💡Planning Algorithms

Planning algorithms are a set of computational methods used by AI agents to determine a sequence of actions to achieve a specific goal. These algorithms are crucial for AI systems to autonomously navigate complex tasks and adapt to changing circumstances. In the video, planning algorithms are highlighted as a key component in creating AI agents that can respond to failures and reroute their approach to successfully complete tasks.

Highlights

Andreu's early contributions to neural networks with GPUs and his role in creating Coursera and popular courses like deeplearning.ai.

The founder and early lead of Google Brain, showcasing the impact of Andreu's work on the field of AI.

The importance of the iterative, agentic workflow in AI development, which is contrasted with the non-agentic workflow.

The surprising effectiveness of the agentic workflow in delivering better results compared to non-agentic approaches.

The case study of using coding benchmarks to measure the performance of AI models like GPT-3.5 and GPT-4.

The discovery that an agentic workflow with GPT-3.5 outperforms GPT-4 without it, highlighting the significance of workflow design.

The broad design patterns observed in agents, which are categorized into four main areas: reflection, planning, multi-agent collaboration, and two-use tools.

The effectiveness of self-reflection in agents for improving code quality by having the LM review its own generated code.

The potential of two-use tools in expanding the capabilities of LMs, as demonstrated by their applications in the computer vision community.

The impact of planning algorithms on AI agents' ability to autonomously handle failures and reroute processes.

The use of multi-agent collaboration in creating complex programs and its potential in boosting productivity.

The importance of fast token generation in agented workflows for iterating over tasks quickly.

The anticipation of significant advancements in AI capabilities due to the adoption of agentic workflows.

The need for patience and dedication when working with AI agents, as opposed to the instant feedback we are used to with web searches.

The potential of agentic reasoning design patterns in bringing us closer to AGI and their role in this long journey.

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