How I Made AI Assistants Do My Work For Me: CrewAI

Maya Akim
15 Jan 202419:21

TLDRThe video script discusses the concept of using AI assistants to automate tasks and make decisions. It introduces the idea of 'system one' and 'system two' thinking, explaining that current large language models are only capable of system one thinking, which is fast and subconscious. The video then explores two methods to simulate system two thinking: tree of thought prompting and using platforms like CrewAI to build custom agents that can collaborate on complex tasks. The author demonstrates how to assemble a team of AI agents to analyze a startup concept, refine tasks, and access real-world data. The script also touches on the use of built-in and custom tools to enhance agent capabilities and discusses the cost implications of using such systems, offering insights into running local models as an alternative to avoid fees and maintain privacy.

Takeaways

  • 🤔 The human mind operates on two thinking systems: System 1 (fast, subconscious) and System 2 (slow, conscious). Current large language models (LLMs) are capable of only System 1 thinking.
  • 🌟 To simulate System 2 thinking, one can use 'tree of thought prompting,' which forces LLMs to consider an issue from multiple perspectives.
  • 💡 CrewAI is a platform that allows users to build custom AI agents that can collaborate to solve complex tasks, even without programming knowledge.
  • 🛠️ Users can integrate various models via APIs or local models through CrewAI, enhancing the agents' capabilities.
  • 📈 The video demonstrates creating a team of AI agents to analyze and refine a startup concept, showcasing a step-by-step process.
  • 📝 Defining specific tasks for agents is crucial; they should be outlined with clear goals and assigned to the appropriate agent.
  • 🔄 A sequential process is used where the output of one agent becomes the input for the next in the team.
  • 📊 To improve agent intelligence, real-world data can be integrated through built-in or custom tools.
  • 🔗 Built-in tools like Google scraper and Reddit scraper can provide agents with up-to-date and relevant information.
  • 💬 The quality of output can vary, and agents may sometimes not follow instructions exactly as given.
  • 💰 Using local models instead of cloud-based APIs can save on costs and maintain privacy, but requires sufficient RAM and can be less reliable.
  • 📉 Among the tested local models, some struggled with tasks, while others like 'OpenChat' performed relatively well.

Q & A

  • What is the main idea behind the concept of 'system one' and 'system two' thinking as described by Daniel Kahneman?

    -System one thinking is fast, subconscious, and automatic, like recognizing a familiar face in a crowd. System two thinking, on the other hand, is slow, conscious, and requires deliberate effort and time, like making a controversial purchase decision. The video discusses how current large language models are only capable of system one thinking.

  • What is the limitation of current large language models (LLMs) in terms of problem-solving?

    -Current LLMs cannot process a request by thinking about a problem from various angles and offering a very rational solution like system two thinking. They are more like auto-predict on steroids, lacking the ability for in-depth, rational analysis.

  • How does the 'tree of thought prompting' method simulate rational thinking in AI?

    -Tree of thought prompting forces the LLM to consider an issue from multiple perspectives or from the viewpoints of various experts. These experts then collaboratively make a final decision by respecting everyone's contribution.

  • What is CrewAI and how does it help in solving complex tasks?

    -CrewAI is a platform that allows anyone, even non-programmers, to build custom agents or experts that can collaborate with each other to solve complex tasks. It enables users to tap into any model with an API or run local models.

  • How does CrewAI's sequential process work in terms of agent collaboration?

    -In CrewAI's sequential process, the output of the first agent becomes the input for the second agent, and so on. This means agents work in a linear, step-by-step manner, passing information from one to the next.

  • What are the two methods to make AI agents smarter as discussed in the video?

    -The two methods are adding built-in tools that provide agents with access to real-world, real-time data, and creating custom tools that scrape or fetch specific data needed for the task at hand.

  • Why is it important to have a clearly defined goal for each agent in CrewAI?

    -Having a clearly defined goal helps to focus the agent's tasks and ensures that it works towards a specific outcome. It also aids in the decision-making process when multiple agents are collaborating.

  • How does the use of local models in CrewAI help with privacy and cost?

    -Using local models allows users to avoid paying fees to companies for API calls and keeps the data and conversations private, as the processing is done on the user's local machine rather than through a third-party service.

  • What are some of the challenges faced when using local models in CrewAI?

    -Some challenges include the requirement for a significant amount of RAM to run larger models, potential freezing or crashing of the system when attempting to run models beyond the laptop's capacity, and the variability in performance across different local models.

  • What is the significance of 'real-world data' in enhancing the intelligence of AI agents?

    -Real-world data provides context and up-to-date information that can make AI agents' responses more relevant and accurate. It allows agents to make informed decisions and offer solutions that are in line with current trends and data.

  • How does the video demonstrate the process of building an AI agent team to solve a specific business problem?

    -The video guides through setting up three agents with specific roles: a market researcher, a technologist, and a business development expert. It then defines tasks for each agent, such as analyzing potential demand, suggesting technological approaches, and writing a business plan, demonstrating how these agents can collaborate to solve complex business problems.

  • What are some of the tools that can be integrated with CrewAI to enhance agent capabilities?

    -Some of the tools that can be integrated include 11 Labs text to speech for generating realistic AI voices, and tools that provide access to YouTube, Google data, and Wikipedia. Additionally, custom tools like a Reddit scraper can be created to fetch specific data from platforms like Reddit.

Outlines

00:00

🤔 Contemplating Controversial Purchases and AI Thinking Systems

This paragraph introduces the concept of 'system two' thinking, which is a slow, deliberate, and effortful cognitive process. It contrasts this with 'system one' thinking, which is fast, subconscious, and automatic. The speaker uses the example of an internal dialogue when considering a purchase to illustrate system two thinking. The paragraph also references Daniel Kahneman's work from 'Thinking Fast and Slow' and transitions into discussing AI's current capabilities in relation to these thinking systems. It mentions a YouTube video by Andre Karpathy from OpenAI, which clarifies that large language models are only capable of system one thinking. The speaker then introduces two methods to simulate rational thinking in AI: tree of thought prompting and using platforms like Crew AI for building custom agents to solve complex tasks.

05:01

💡 Building a Team of AI Agents for Complex Problem Solving

The second paragraph details the process of setting up AI agents using Crew AI to tackle a startup concept. It explains how to install Crew AI, import necessary modules, and set up a virtual environment. The paragraph outlines creating three agents with specific roles: a market researcher expert, a technologist, and a business development expert. Each agent is assigned a goal, such as analyzing market demand or writing a business plan. The speaker also emphasizes defining tasks with specific results in mind and creating a process for agents to work together sequentially. The example provided involves creating a business plan for fashionable plugs for Crocs, demonstrating how the agents' outputs can be combined to form a comprehensive business strategy.

10:01

📈 Enhancing AI Agent Intelligence with Real-Time Data

This paragraph focuses on making AI agents smarter by integrating them with real-time data through tools. It discusses adding built-in tools from Lang Chain, such as text-to-speech and data access tools for platforms like YouTube, Google, and Wikipedia. The speaker then outlines a process for creating a detailed report on AI and machine learning innovations by using these tools. The paragraph also touches on the quality of information and how it affects the output, mentioning the use of a custom tool to scrape the latest posts from a subreddit for more relevant and up-to-date information. The speaker shares their experience with different pre-built and custom tools, including the challenges of obtaining quality information and the variability in agent performance.

15:03

💻 Running Local Models for Cost and Privacy Efficiency

The final paragraph discusses the cost implications of running AI scripts and the potential for using local models to avoid expensive API calls and maintain privacy. The speaker shares their experiments with various open-source models and their performance when tasked with generating content. It highlights the challenges of running certain models due to hardware limitations and the varying success rates of different models in understanding and completing tasks. The paragraph concludes with the speaker's recommendation of a local model that performed adequately and an invitation for viewers to share their experiences with Crew AI.

Mindmap

Keywords

💡AI Assistants

AI Assistants refer to artificial intelligence systems designed to perform tasks that would typically require human intelligence. In the context of the video, they are used to automate and enhance work processes, such as analyzing a startup concept or creating a business plan.

💡System 1 and System 2 Thinking

System 1 and System 2 thinking are concepts from Daniel Kahneman's 'Thinking Fast and Slow'. System 1 represents fast, intuitive, and subconscious thought processes, while System 2 is slow, deliberate, and logical. The video discusses the limitations of current AI, which can perform System 1 tasks but struggle with the more complex System 2 thinking.

💡Tree of Thought Prompting

Tree of Thought Prompting is a method used to simulate rational thinking in AI by forcing the AI to consider an issue from multiple perspectives. This technique is mentioned as a way to work around the current limitations of AI in performing complex, multi-faceted tasks.

💡CrewAI

CrewAI is a platform that allows users to build custom AI agents or experts that can collaborate to solve complex tasks. It is highlighted in the video as a tool that can be used to create a team of AI agents to tackle various aspects of a project, such as market research or business development.

💡API

API stands for Application Programming Interface, which is a set of protocols and tools for building software applications. In the video, the use of APIs is mentioned to tap into various models that can be used by CrewAI to enhance the capabilities of the AI agents.

💡Local Models

Local models refer to AI models that are run on a user's own computer rather than relying on cloud-based services. The video discusses the benefits of using local models, such as avoiding fees and maintaining privacy, and also the technical requirements and challenges associated with them.

💡LangChain

LangChain is a tool or platform mentioned in the video that is used to manage and execute tasks using AI agents. It is part of the process of setting up and running the custom AI team created with CrewAI.

💡Prompting

Prompting in the context of AI refers to the act of providing input or a starting point for the AI to generate a response or perform a task. The video script discusses how effective prompting can guide AI agents to produce desired outcomes.

💡Custom Tools

Custom tools in the video refer to user-created applications that are designed to scrape or fetch specific data from the internet, such as Reddit posts. These tools are integrated with AI agents to enhance their ability to gather and process information.

💡Newsletter

A newsletter is a regularly distributed publication that provides updates or news. In the video, the AI agents are tasked with creating a detailed newsletter about AI and machine learning innovations, which serves as an example of a complex task that can be automated with AI assistance.

💡Open Source Models

Open source models are AI models whose designs are publicly accessible and can be modified by anyone. The video discusses experimenting with various open source models to find the best ones for understanding tasks and generating useful outputs.

Highlights

The transcript discusses the use of AI assistants to automate tasks and the concept of 'system one' and 'system two' thinking as described by Daniel Kahneman.

Current large language models (LLMs) are only capable of 'system one' thinking, which is fast and subconscious, unlike 'system two' thinking that is slow and deliberate.

Two methods are presented to simulate 'system two' thinking in AI: tree of thought prompting and using platforms like CrewAI and agent systems.

CrewAI allows users to build custom AI agents that can collaborate to solve complex tasks, even without programming knowledge.

The video demonstrates how to assemble a team of AI agents to analyze and refine a startup concept.

A virtual environment and specific modules are used to set up the AI agents, with each agent having a defined role and goal.

Tasks are assigned to agents with specific goals, such as market analysis or writing a business plan, leading to a sequential process of information gathering and analysis.

Built-in tools and custom-made tools can be added to AI agents to provide them with real-time data from the internet, such as Google search results or Reddit posts.

The quality of the AI's output can vary, and sometimes it may not follow instructions exactly as given.

Local models can be run instead of relying on cloud-based APIs to avoid costs and maintain privacy, with specific hardware requirements.

Different open-source models were tested, with varying levels of success in understanding and completing tasks.

The best performing local model with seven billion parameters was OpenChat, but it lacked specific data from the local subreddit.

The transcript provides a step-by-step guide on how to use CrewAI to automate complex problem-solving and data gathering.

The use of AI agents can significantly reduce the time spent on research and analysis, as demonstrated by the automation of tasks such as reading and summarizing Reddit posts.

Despite the potential of AI agents, there are still challenges in achieving consistent and accurate outputs that fully align with the user's instructions.

The video concludes with a discussion on the costs associated with using cloud-based AI models and the benefits of using local models for privacy and cost-saving.

The author shares personal experiences and insights on using CrewAI and invites viewers to share their own experiences.