AI Pioneer Shows The Power of AI AGENTS - "The Future Is Agentic"

Matthew Berman
29 Mar 202423:47

TLDRDr. Andrew Ng, a leading figure in AI and co-founder of Google Brain, delivered a talk at Sequoia Capital emphasizing the transformative potential of AI agents. Ng highlighted the agentic workflow, where multiple agents collaborate and iterate on tasks, leading to superior results compared to traditional non-agentic approaches. He discussed the effectiveness of this method using case studies, such as coding benchmarks where agentic workflows outperformed even advanced models like GPT-4. Ng also touched on various design patterns in agentic systems, including reflection, tool use, planning, and multi-agent collaboration. He concluded by suggesting that as models become more commoditized, the cost of tokens will be less of an issue, and the future of AI will increasingly involve agentic reasoning, which could bring us closer to achieving general AI.

Takeaways

  • 🚀 Dr. Andrew Ng is very optimistic about AI agents and their potential to reason and improve upon tasks through iterative processes.
  • 🌟 Sequoia is a legendary Silicon Valley venture capital firm with a portfolio that includes significant companies like Apple, Airbnb, and Instagram.
  • 🤖 Non-agentic workflows are compared to asking a person to write an essay without revision, whereas agentic workflows involve multiple AI agents with different roles collaborating and iterating on a task.
  • 📈 Agentic workflows can lead to significantly better results, as demonstrated by the coding benchmark study where GPT 3.5 with an agentic workflow outperformed GPT 4 with zero-shot prompting.
  • 🛠️ Tool use allows AI agents to utilize hardcoded functions, APIs, and libraries, enhancing their capabilities without the need to reinvent these tools.
  • 🤝 Multi-agent collaboration involves different AI agents working together, each with a specific role, leading to a more robust and effective outcome.
  • 🔄 Reflection is a technique where an AI agent reviews its own output, identifies areas for improvement, and generates a better result.
  • 📝 Planning involves giving AI agents the ability to think through steps and plan actions, which can lead to more effective solutions.
  • 🔍 The use of fast token generation is crucial for agentic workflows, as it allows for quicker iterations and improvements.
  • 📈 The future of AI is expected to expand dramatically due to agentic workflows, potentially leading to a significant productivity boost.
  • ⏱️ Patience may be required when using agentic workflows, as the process may take longer than traditional instant responses from AI models.

Q & A

  • What is the main topic of Dr. Andrew Nig's talk at Sequoia?

    -The main topic of Dr. Andrew Nig's talk is the power and future of AI agents, emphasizing the importance of an agentic workflow in artificial intelligence.

  • What is the significance of the agentic workflow in AI?

    -The agentic workflow allows for multiple AI agents with different roles and tools to work together and iterate on a task, leading to better results compared to a non-agentic, one-shot approach.

  • How does Dr. Andrew Nig's background contribute to his credibility in discussing AI?

    -Dr. Andrew Nig is a computer scientist, co-founder and head of Google Brain, former Chief Scientist of Baidu, and a leading mind in artificial intelligence with education from UC Berkeley, MIT, and Carnegie Mellon, which establishes his expertise and credibility in the field.

  • What is Corsera, and how does it relate to Dr. Andrew Nig's work?

    -Corsera is a company co-founded by Dr. Andrew Nig that offers free online courses in various subjects, including computer science and math, making knowledge in these areas accessible to a wide audience.

  • How does Sequoia's investment portfolio reflect their ability to pick technological winners?

    -Sequoia's portfolio includes companies like Reddit, Instacart, DoorDash, Airbnb, Apple, Block, Snowflake, Vanta, Zoom, Stripe, WhatsApp, and Instagram, which together represent over 25% of the total value of the NASDAQ, demonstrating their success in identifying and investing in innovative technology companies.

  • What is the difference between zero-shot prompting and an agentic workflow in AI?

    -Zero-shot prompting involves giving a single prompt to an AI model without any examples or opportunities for iteration, whereas an agentic workflow involves multiple agents with different roles working together, iterating on a task to achieve a better outcome.

  • How does the agentic workflow improve performance on the HumanEval Benchmark?

    -When using an agentic workflow with GPT 3.5, the performance on the HumanEval Benchmark improves from 48% with zero-shot prompting to over 95%, showing the power of iterative processes and collaboration among agents.

  • What are some of the broad design patterns seen in AI agents?

    -Some broad design patterns in AI agents include reflection, tool use, planning, and multi-agent collaboration. These patterns enhance the capabilities of AI agents, allowing them to perform tasks more effectively and autonomously.

  • How does tool use in AI agents enhance their capabilities?

    -Tool use allows AI agents to utilize predefined functions or tools for specific tasks, such as web scraping or data analysis. By providing these tools, the AI can perform tasks more reliably and efficiently without needing to generate the solution from scratch.

  • What is the potential impact of faster token generation on agentic workflows?

    -Faster token generation can significantly improve the efficiency of agentic workflows by allowing for more rapid iteration and response times. This can lead to quicker task completion and more opportunities for agents to refine and improve their output.

  • Why is patience important when using agentic workflows?

    -Patience is important because agentic workflows often involve multiple iterations and may require more time for the AI to process and refine the output. Allowing sufficient time for this process can lead to higher quality results compared to expecting immediate responses.

Outlines

00:00

🚀 Dr. Andrew Ng's Talk on Agents and AI's Future

Dr. Andrew Ng, a renowned computer scientist, co-founder of Google Brain, and head of Corsera, delivered a talk at Sequoia, a prestigious Silicon Valley venture capital firm. Ng highlighted the potential of agents powered by models like GPT 3.5, which can reason at the level of GPT 4. He emphasized the importance of iterative workflows where multiple agents with different roles collaborate to achieve the best outcome, much like human planning and iteration. Ng's talk also touched on the impressive portfolio of Sequoia, which includes companies like Apple, Airbnb, and Zoom, representing over 25% of the NASDAQ's total value.

05:02

🤖 Agentic Workflows and Their Impact on AI

The video script discusses the concept of agentic workflows, contrasting them with non-agentic approaches. In agentic workflows, AI models take on specific roles, such as writers, reviewers, and fact-checkers, working together to iteratively refine tasks. This approach is shown to outperform zero-shot prompting and even GPT 4 in certain tasks. The script also mentions the use of reflection, tool use, and multi-agent collaboration as key components in enhancing AI's capabilities. These strategies are seen as robust technologies that can significantly improve the performance of large language models (LLMs).

10:04

🔍 Reflection and Tool Use in AI Agents

The script delves into the specifics of how reflection and tool use can enhance the performance of AI agents. Reflection involves an AI model reviewing its own output and making improvements, which can lead to better code and more accurate responses. Tool use allows AI models to utilize custom-coded tools and external libraries, expanding their capabilities beyond what they were initially trained to do. The combination of these techniques can lead to more reliable and efficient AI systems, although the script notes that multi-agent collaboration can be challenging to implement reliably.

15:05

📈 Planning and Multi-Agent Collaboration

The video script outlines the design patterns for AI agents, emphasizing planning and multi-agent collaboration. Planning involves giving AI models the ability to think through steps and explain their reasoning, which often results in better outcomes. Multi-agent collaboration, where different agents with distinct roles work together, can be highly effective but also finicky. The script provides examples of how these agents can be used in practice, such as in coding tasks where one agent might write the code while another reviews it. The potential of these collaborative systems is significant, though they may require patience and iteration to achieve the best results.

20:07

🚀 The Future of AI with Agentic Reasoning

The final paragraph discusses the future of AI with agentic reasoning. It suggests that the capabilities of AI will expand dramatically due to agentic workflows. The script also addresses the need for patience when working with AI agents, as they may require more time to process and respond compared to traditional instant feedback systems. The importance of fast token generation for iterative agentic workflows is highlighted, as it allows for more loops and potentially better results. The script concludes with excitement for the upcoming models like GPT 5 and the potential of agentic reasoning to take a step forward in the journey towards artificial general intelligence (AGI).

Mindmap

Keywords

💡AI Agents

AI Agents, as discussed in the video, refer to autonomous systems that can perform tasks, make decisions, and interact with other systems or humans. They are a core part of the future of artificial intelligence, as they can simulate human-like workflows, such as writing essays or coding, through iterative processes. In the context of the video, AI Agents are seen as the future of productivity and efficiency in AI applications.

💡Dr. Andrew Ng

Dr. Andrew Ng is a renowned computer scientist, known for his contributions to the fields of machine learning and artificial intelligence. He co-founded Google Brain and is a leading mind in AI, with educational background from institutions like UC Berkeley, MIT, and Carnegie Mellon. In the video, his talk at Sequoia Capital emphasizes the potential and power of AI agents, highlighting his belief in their significance for the future of AI.

💡Sequoia Capital

Sequoia Capital is a prominent venture capital firm located in Silicon Valley, known for its successful investments in high-technology companies. The video mentions Sequoia's impressive portfolio, which includes significant contributions to the NASDAQ's total value. The firm's association with Dr. Andrew Ng's talk underscores the importance and potential impact of AI agents in the tech industry.

💡GPT (Generative Pre-trained Transformer)

GPT refers to a type of AI language model that is pre-trained on a large amount of text data, enabling it to generate human-like text. The video discusses versions like GPT 3.5 and GPT 4, emphasizing how these models can be utilized within an agentic workflow to perform complex tasks more effectively. GPT models are a fundamental technology that powers the AI agents discussed.

💡Agentic Workflow

An agentic workflow is an iterative process where AI agents perform tasks in a step-by-step manner, much like a human would. The video explains that this workflow allows for multiple agents, each with different roles, to collaborate and improve upon a task through repeated cycles of action and revision. This approach is contrasted with a non-agentic workflow, where a task is completed in one go without revision.

💡Iteration

Iteration in the context of the video refers to the process of repeating a task with the goal of improving upon it. It is a key component of the agentic workflow, where AI agents perform a task, review it, make necessary changes, and repeat the process until an optimal outcome is achieved. Iteration is central to how AI agents can produce high-quality results.

💡Human Eval Benchmark

The Human Eval Benchmark is a coding challenge benchmark used to measure the performance of AI systems in solving coding problems. The video uses this benchmark to illustrate the effectiveness of AI agents, particularly when using an agentic workflow. It shows that an agentic approach with GPT 3.5 outperforms even GPT 4 using a non-agentic approach.

💡Tool Use

Tool use in the video refers to the ability of AI agents to utilize predefined tools or functions to perform specific tasks, such as web scraping or data analysis. By incorporating tool use, AI agents can expand their capabilities beyond what the base language model can do, leading to more efficient and effective outcomes in their tasks.

💡Reflection

Reflection, as used in the context of AI agents, is the process where an agent reviews its own output, identifies areas for improvement, and then generates a revised output. This self-assessment and improvement step is a powerful feature of agentic workflows, allowing AI agents to enhance their performance and the quality of their work.

💡Multi-Agent Collaboration

Multi-agent collaboration involves multiple AI agents working together to accomplish a task. Each agent may have a different role or expertise, and they collaborate and iterate to achieve a common goal. The video highlights this as an emerging and powerful approach, where the collective intelligence of multiple agents can lead to superior results compared to a single agent.

💡Planning

Planning in the context of AI agents involves the agent's ability to think through steps and devise a strategy to achieve a task. It is a critical component for complex tasks that require a sequence of actions. The video emphasizes the importance of planning in agentic workflows, as it allows AI agents to simulate a more human-like approach to problem-solving.

Highlights

Dr. Andrew Ng, a leading figure in AI, delivered a talk at Sequoia Capital emphasizing the potential of AI agents.

Ng believes that the future of AI will be 'agentic', with systems capable of reasoning and iterative improvement.

AI agents can perform tasks more effectively by assuming different roles and collaborating through an iterative process.

Sequoia Capital is renowned for its successful investments, with a portfolio that represents over 25% of the NASDAQ's total value.

The agentic workflow allows for multiple AI agents to work together, each with a specific role, leading to higher quality outcomes.

Case study: Using an agentic workflow with GPT 3.5 outperformed GPT 4 using a zero-shot approach in a coding benchmark.

Reflection, a technique where an AI reviews and improves its own output, can significantly enhance performance.

Tool use allows AI agents to utilize custom-coded tools, expanding their capabilities beyond language modeling.

Planning and multi-agent collaboration are emerging technologies that can lead to more sophisticated and reliable AI behaviors.

Ng suggests that as AI evolves, we may need to adjust our expectations for instant responses and allow agents more time to process and iterate.

The use of fast token generation can improve agent workflows by allowing for more rapid iteration and refinement.

Ng anticipates that the capabilities of AI agents will expand dramatically as agentic workflows become more prevalent.

The talk suggests that agentic workflows could be a step forward on the path towards Artificial General Intelligence (AGI).

Corporations like Google Brain and startups such as Coursera have been instrumental in advancing AI and making educational resources accessible.

The integration of AI agents into the development process can lead to a significant boost in productivity and efficiency.

Ng's talk encourages embracing the 'messy' and iterative nature of agentic workflows for better results in AI applications.

The future of AI development may rely on the successful implementation of agentic design patterns and collaborative multi-agent systems.

The talk concludes with optimism for the role of AI agents in transforming how we approach problem-solving and innovation.