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 significance of iterative, agentic workflows in enhancing AI performance. It emphasizes the importance of design patterns like reflection, multi-agent collaboration, planning, and two-use tools, which can significantly boost productivity and lead to remarkable outcomes. The speaker also anticipates the expansion of AI capabilities through agentic reasoning and faster token generation, suggesting a promising step towards AGI.

Takeaways

  • 🧠 The importance of neural networks and GPUs was a significant focus in computer science, with figures like Andrew Ng leading the way.
  • 💡 The transition from non-agentic to agentic workflows in AI development is highlighted, emphasizing iterative processes.
  • 📝 Agentic workflows involve a more interactive process with the AI, such as drafting, revising, and refining outputs.
  • 🚀 The study found that using an agentic workflow with GPT-3.5 improved performance over GPT-4.
  • 🔍 The concept of 'reflection' in AI agents is introduced as a powerful tool for self-improvement and error correction.
  • 🤖 The potential of multi-agent collaboration is discussed, where different AI agents can work together to achieve better results.
  • 🛠️ Planning algorithms in AI are noted to enable agents to autonomously handle failures and reroute processes effectively.
  • 📈 The agentic reasoning design patterns are expected to significantly boost productivity in AI applications.
  • 🕒 Patience is required when working with agentic workflows, as immediate responses may not always be feasible.
  • 🚀 The future of AI is anticipated to see rapid expansion in capabilities due to the adoption of agentic workflows.

Q & A

  • What is the main focus of the discussion in the transcript?

    -The main focus of the discussion is on the development and application of AI agents using neural networks with GPUs, and the exploration of various design patterns in agentic workflows for improving AI performance and productivity.

  • Who is the speaker referring to when mentioning 'Andreu' and what are his notable achievements?

    -The speaker is referring to Andrew Ng, a renowned computer science professor at Stanford, known for his early contributions to neural networks with GPUs. He is also the creator of Coursera, popular courses like deeplearning.ai, and the founder and early lead of Google Brain.

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

    -The significance of the problem set number two of CS229 is that it represents a personal anecdote where the speaker is curious about the mistakes they made in their assignment, which was related to a 'b' and 'I', possibly referring to a coding or machine learning task.

  • What are the two main workflows for using language models as described in the transcript?

    -The two main workflows for using language models are the non-agentic workflow, where the model generates an answer to a prompt without interaction, and the agentic workflow, which involves a more iterative process where the AI and the user collaborate to refine and improve the output over time.

  • How does the agentic workflow improve results compared to the non-agentic workflow?

    -The agentic workflow improves results by allowing for an iterative process where the AI can revise and improve its output based on feedback and additional prompts. This collaborative approach leads to better performance and more accurate or refined outcomes compared to a one-time, non-interactive response.

  • What is the 'reflection' design pattern mentioned in the transcript?

    -The 'reflection' design pattern involves prompting an AI or language model to review and critique its own output or code. This self-evaluation can help identify and fix errors or improve the quality of the AI's work.

  • How does the 'multi-agent collaboration' design pattern function?

    -The 'multi-agent collaboration' design pattern functions by using multiple AI agents, each playing a different role, to work together on a task. For example, one agent might act as a coder while another acts as a critic, reviewing and improving the code. This collaborative approach can lead to better results and more complex problem-solving.

  • What is the significance of 'planning' in AI agents as described in the transcript?

    -The 'planning' design pattern in AI agents is significant because it allows the AI to autonomously navigate around failures or obstacles. By planning and rerouting, the AI can achieve goals that would otherwise be difficult or impossible to accomplish in a linear, non-adaptive manner.

  • What does the speaker suggest about the future of AI agents and token generation?

    -The speaker suggests that the future of AI agents will involve faster token generation, which will be crucial for agentic workflows that require iterative interactions. Generating more tokens quickly, even from a slightly lower quality LM, could potentially yield better results due to the increased number of iterations possible.

  • What is the speaker's overall perspective on the role of agentic workflows in AI development?

    -The speaker believes that agentic workflows are a powerful tool for AI development, offering significant productivity boosts and enabling AI to achieve tasks that were previously unimaginable. They see these workflows as an important step towards artificial general intelligence (AGI) and encourage the adoption of these design patterns for better AI performance.

  • What is the significance of the 'multi-agent debate' design pattern mentioned in the transcript?

    -The 'multi-agent debate' design pattern is significant because it leverages the power of multiple AI agents working together to debate and challenge each other's ideas or solutions. This collaborative yet competitive approach can lead to more robust and well-rounded outcomes, as different perspectives are considered and refined through the debate process.

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. He introduces the concept of AI agents, emphasizing their potential as a significant trend in AI development. The speaker shares his experience with problem sets from a Stanford course and leads into a discussion about the iterative, agentic workflow in AI, contrasting it with the traditional non-agentic approach. He highlights the superior results achieved through agentic workflows and shares a case study on coding benchmarks to illustrate the effectiveness of this method.

05:01

📚 Design Patterns in AI Agents

The speaker delves into the design patterns observed in AI agents, noting the chaotic yet promising landscape of AI research and development. He categorizes the patterns into four main types: reflection, multi-agent collaboration, planning, and multi-agent debate. The speaker provides examples of each, such as using AI for code review and collaboration, utilizing AI in various tools for information gathering and action, planning algorithms that allow AI to navigate around failures, and multi-agent systems that simulate different roles to solve complex tasks. He emphasizes the potential of these patterns to enhance productivity and the evolving nature of AI technology.

10:04

🚀 Future Trends and the Path to AGI

In the final paragraph, the speaker discusses the future trends in AI, predicting a significant expansion of AI capabilities due to agentic workflows. He touches on the need for patience when working with AI agents, as they may require more time to process and respond effectively. The speaker also highlights the importance of fast token generation for iterative AI workflows. He expresses excitement for upcoming AI models and reflects on the journey towards Artificial General Intelligence (AGI), suggesting that the use of agentic reasoning design patterns could contribute to this long-term goal.

Mindmap

Keywords

💡Neural Networks with GPUs

Neural Networks with GPUs refer to the use of Graphical Processing Units to train and run artificial neural networks, which are a form of machine learning. In the context of the video, this technology was pivotal in the early development of AI, as it allowed for faster and more efficient computation, which was crucial for the advancement of deep learning and AI applications. Andreu, a computer science professor at Stanford, was an early pioneer in this field.

💡Deep Learning

Deep Learning is a subset of machine learning that involves the use of artificial neural networks with multiple layers to learn and make decisions based on data. It is a key concept in the video as it underpins the development of AI agents and is the focus of the courses taught by Andreu, such as those on deeplearning.ai. The video emphasizes the importance of deep learning in creating advanced AI systems capable of complex tasks.

💡AI Agents

AI Agents refer to autonomous systems or programs that can perform tasks, make decisions, and interact with users or other systems in a manner similar to a human. In the video, the speaker discusses the exciting trend of AI agents in technology, emphasizing their potential to transform how we build and interact with AI systems. The concept of AI agents is central to the narrative, as it shapes the future of AI applications and user experiences.

💡Agentic Workflow

An Agentic Workflow is an iterative process where an AI system is used not just to generate output but also to actively participate in refining and improving the output over time. This concept is contrasted with a non-agentic workflow, where the AI simply generates a response to a prompt without further interaction. The speaker in the video highlights the superior results achieved through agentic workflows, as they allow for more dynamic and interactive engagement with the AI.

💡Reflection

In the context of the video, Reflection refers to the process where an AI system is prompted to review and assess its own output or code for correctness and efficiency. This self-evaluation allows the AI to identify potential issues and make improvements, mimicking the way a human might review their own work. Reflection is one of the design patterns discussed for enhancing AI agents' performance and capability.

💡Multi-Agent Collaboration

Multi-Agent Collaboration involves multiple AI agents working together to achieve a common goal. Each agent may have a different role or function, and they interact and communicate with each other to solve problems or complete tasks. The video discusses this as an emerging design pattern, where the collective intelligence of multiple agents can surpass the capabilities of individual agents or even more advanced single agents.

💡Planning Algorithms

Planning Algorithms are computational methods used by AI agents to determine a sequence of actions to achieve a specific goal or objective. These algorithms are critical in enabling AI to navigate complex tasks and make decisions based on the current state and future goals. In the video, planning algorithms are highlighted as a key technology that allows AI agents to demonstrate impressive problem-solving capabilities, such as rerouting around failures.

💡Two-Use Systems

Two-Use Systems, as discussed in the video, are AI systems that can both generate output and interact with other tools or systems to extend their capabilities. This dual functionality allows AI agents to gather information, perform analysis, and take actions beyond what they can do by themselves. The concept is rooted in the idea that AI can be more versatile and useful when it can integrate with existing tools and platforms.

💡Fast Token Generation

Fast Token Generation refers to the ability of AI systems, particularly language models, to quickly produce a large number of tokens or text elements during the agentic workflow process. This speed is advantageous as it allows for more iterations and refinements, leading to better outcomes. The concept is important in the context of the video because it highlights the efficiency gains that can be achieved through rapid generation of content by AI.

💡AGI (Artificial General Intelligence)

Artificial General Intelligence (AGI) is the hypothetical intelligence of a machine that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, much like a human being. In the video, the speaker views the path to AGI as a journey rather than a destination, suggesting that advancements in agentic workflows and AI design patterns contribute to incremental progress towards achieving AGI.

💡迭代 (Iteration)

迭代, or iteration in English, is the process of repeating a cycle of activities with the goal of improving a product or process with each cycle. In the context of the video, iteration is crucial for agentic workflows, as it allows AI agents to refine their output through repeated cycles of generation, review, and revision. This concept is central to the speaker's argument about the effectiveness of agentic workflows in achieving better results with AI systems.

Highlights

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

The introduction of agentic workflow in AI, contrasting with non-agentic workflow.

The iterative process of agentic workflow leading to better results.

The case study showing the effectiveness of agentic workflow with GPT 3.5 over non-agentic zero shot prompting.

Broad design patterns observed in agents, including reflection, planning, and multi-agent collaboration.

Self-reflection in agents as a tool for improvement.

Two-use systems expanding the capabilities of LMs in fields like computer vision.

Planning algorithms enabling AI agents to autonomously recover from failures.

Multi-agent collaboration leading to complex problem-solving and decision-making.

The concept of fast token generation and its importance in agented workflows.

The impact of agentic reasoning design patterns on productivity and the potential for AGI.

The need for patience and delegation in working with AI agents.

The anticipation of advancements in AI models like Cloud 5, CL 4, GPT 5, and Gemini 2.0.

The importance of agent workflows in potentially achieving higher performance on early models.

The role of AI agents in personal workflows and productivity.

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