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

Maya Akim
15 Jan 202419:21

TLDRThe video transcript discusses the concept of 'system one' and 'system two' thinking in relation to AI, highlighting that current large language models are only capable of system one thinking, which is fast and subconscious. The speaker, Andre Karpathy, explains that to achieve system two thinking, which is slow, conscious, and deliberate, one can use methods like 'tree of thought prompting' or platforms like CrewAI. CrewAI allows users to build custom AI agents that can collaborate to solve complex tasks. The video demonstrates how to set up AI agents for a startup concept, refine tasks, and use real-world data to enhance the agents' intelligence. It also addresses the cost of using these models and how to run local models to avoid fees and maintain privacy.

Takeaways

  • πŸ€” The inner dialogue we have when making decisions is known as 'System 2' thinking, which is slow and deliberate, contrasting with 'System 1' thinking that is fast and subconscious.
  • 🧠 Large language models (LLMs) are currently only capable of 'System 1' thinking, which means they can't process complex problems with rational, multi-angle solutions.
  • 🌟 Two methods have been developed to simulate 'System 2' thinking: 'Tree of Thought' prompting and using platforms like CrewAI and agent systems to collaboratively solve complex tasks.
  • πŸ’‘ CrewAI allows users, even non-programmers, to build custom AI agents that can work together to solve problems by tapping into any model with an API or running local models.
  • πŸ› οΈ The video demonstrates how to assemble a team of AI agents to solve complex problems, make them smarter with real-world data, and avoid fees and privacy issues by running models locally.
  • πŸ“ˆ Daniel Kahneman's 'Thinking Fast and Slow' is referenced to explain the two systems of thinking, which is crucial for understanding AI's current capabilities and limitations.
  • πŸ“ The process of using CrewAI involves defining agents with specific roles and goals, creating tasks with clear objectives, and setting up a process for how agents will collaborate.
  • πŸ” To enhance the intelligence of AI agents, one can integrate built-in tools or create custom tools that provide access to real-time data, such as scraping the latest posts from a subreddit.
  • πŸ“Š The effectiveness of local models was tested, with varying results, and it was found that larger models with more parameters didn't necessarily perform better for the given tasks.
  • πŸ’° Running AI scripts can incur costs, especially with frequent use of paid APIs, which highlights the importance of considering cost-effective solutions like local models.
  • πŸ”— The importance of providing detailed instructions and clear objectives for AI agents is emphasized, as the output quality can vary and may not always meet expectations.

Q & A

  • What is the difference between system one and system two thinking as described by Daniel Kahneman?

    -System one thinking is subconscious and automatic, allowing for quick responses like recognizing a familiar face in a crowd. System two thinking, on the other hand, is slow, conscious, and requires deliberate effort and time, such as when making a complex decision or solving a difficult problem.

  • What is the limitation of current large language models (LLMs) in terms of thinking processes?

    -Current LLMs are only capable of system one thinking. They cannot engage in system two thinking, which involves processing a request, thinking about a problem from various angles, and offering a rational solution to a complex problem.

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

    -Tree of thought prompting involves forcing 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 users, even non-programmers, to build custom AI 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 through its interface.

  • How can real-world data enhance the intelligence of AI agents?

    -Real-world data can make AI agents smarter by providing them with access to up-to-date, relevant information. This can be achieved by integrating tools that fetch data from sources like emails, Reddit conversations, or Google search results.

  • What are the steps to create an agent team using CrewAI?

    -First, install CrewAI and import necessary modules. Then, define the agents with specific roles and goals. Create tasks with clear objectives and assign them to agents. Finally, instantiate the team of agents, define a process for how they will work together, and run the process to execute the tasks.

  • How can built-in tools from LangChain enhance the functionality of AI agents?

    -Built-in tools from LangChain can provide AI agents with access to various online services and databases. For example, tools like 11 Labs text-to-speech for generating AI voices, or tools that allow access to YouTube, Google data, and Wikipedia can be integrated to enhance the capabilities of AI agents.

  • What is a custom tool and how does it help in improving the quality of information for an AI agent's output?

    -A custom tool is a user-created application that serves a specific purpose, such as scraping the latest posts from a subreddit. It gives the user more control and flexibility over the data the AI agent has access to, thus improving the quality and relevance of the agent's output.

  • What are the considerations for running local models through LangChain?

    -When running local models, it's important to ensure that the system has sufficient RAM: at least 8 GB for models with 7 billion parameters, 16 GB for 13 billion parameters, and 32 GB for 33 billion parameter models. Also, some models may not be compatible with certain hardware configurations.

  • How can one avoid paying fees to companies and exposing private information while using AI models?

    -One can avoid fees and protect privacy by running local models instead of relying on cloud-based services. This requires downloading and using open-source models that have been optimized to run on the user's specific hardware.

  • What was the outcome of testing various local models for a specific task in the video?

    -The best performing model with seven billion parameters was OpenChat, which produced a newsletter-style output. However, it did not include data from the local subreddit as intended. The regular Llama 13 billion parameters model, despite not being fine-tuned, was the only one that considered data from the subreddit, although its output was not in the format of a newsletter.

  • What is the significance of the sequential process in defining how agents work together in CrewAI?

    -The sequential process means that the output of one agent becomes the input for the next agent in the team. This allows for a workflow where each agent can build upon the information processed by the previous one, leading to a more comprehensive and step-by-step approach to solving complex tasks.

Outlines

00:00

πŸ€” Understanding AI Thinking Systems

This paragraph discusses the concept of 'system one' and 'system two' thinking as introduced by Daniel Kahneman in his book 'Thinking Fast and Slow.' It explains that system one is fast, subconscious, and automatic, while system two is slow, conscious, and requires deliberate effort. The speaker then relates this to AI, mentioning that current large language models are only capable of system one thinking. The paragraph also introduces two methods to simulate system two thinking: tree of thought prompting and using platforms like Crew AI and Agent Systems CREI to build custom AI agents that can collaborate to solve complex tasks.

05:01

πŸš€ Building a Team of AI Agents

The speaker demonstrates how to build a team of AI agents using Crew AI, even if one is not a programmer. The process involves installing necessary modules, importing an OpenAI key, and defining agents with specific roles and goals. The example given involves setting up three agents: a market researcher, a technologist, and a business development expert. Each agent is tasked with different aspects of refining a startup concept. The speaker also explains how to define tasks, instantiate the team of agents, and run the process to get results, which are expected to be better than using a single AI model.

10:01

πŸ“ˆ Enhancing Agents with Real-time Data

The paragraph explains how to make AI agents smarter by giving them access to real-world, real-time data using tools provided by LangChain. Built-in tools like text-to-speech and access to various online databases are mentioned. The speaker then shows how to use these tools to create a detailed report about the latest AI and machine learning innovations. The process involves using the Google Search API to fetch information and compile it into a newsletter. However, the speaker notes that the quality of the information is not always the best and discusses ways to improve it by using custom-made tools.

15:03

πŸ’‘ Custom Tools and Local Models

The speaker talks about creating a custom tool to scrape the latest posts from the 'local llama' subreddit, which is a source of exciting AI projects. They discuss the process of writing a custom tool for Reddit scraping and the benefits of using such a tool. The speaker also shares their experiences with different local models, highlighting the best and worst performers. They mention running into issues with certain models and how they managed to achieve better results with others. The paragraph concludes with a mention of the speaker's GitHub, where they have shared their notes and code.

Mindmap

Keywords

πŸ’‘AI Assistants

AI Assistants refer to artificial intelligence systems designed to perform tasks typically requiring human intelligence, such as understanding natural language, learning from experience, and problem-solving. In the context of the video, AI Assistants are used to automate and streamline the work process, particularly in the creation of a business plan and market analysis for a startup concept.

πŸ’‘System 1 and System 2 Thinking

System 1 and System 2 thinking are concepts from Daniel Kahneman's 'Thinking, Fast and Slow.' System 1 is fast, intuitive, and operates automatically, like recognizing a familiar face. System 2 is slow, analytical, and requires effort and time. The video discusses how current AI models, like large language models, are capable of System 1 thinking but are limited in performing System 2 thinking, which involves deliberate and rational problem-solving.

πŸ’‘Tree of Thought Prompting

Tree of Thought Prompting is a method used to simulate rational thinking in AI. It involves guiding the AI to consider an issue from multiple perspectives or through the lens of various experts. This method is mentioned in the video as one of the ways to work around the current limitations of AI in performing complex, rational problem-solving.

πŸ’‘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 enables even non-programmers to create a team of AI agents, which can be integrated with various models through APIs or run locally, to address intricate problems more effectively.

πŸ’‘API

API stands for Application Programming Interface, which is a set of protocols and tools that allows different software applications to communicate with each other. In the video, the use of APIs is essential for integrating different AI models into the CrewAI platform to form a collaborative team of agents.

πŸ’‘Agent Task Processing

Agent Task Processing refers to the method by which AI agents perform specific tasks assigned to them. In the video, it is shown that each agent has a defined role and goal, and they process tasks in a sequential manner, with the output of one agent feeding into the next.

πŸ’‘Local Models

Local models in the context of the video refer to AI models that are run on the user's local machine rather than relying on cloud-based services. This approach can help avoid costs associated with API calls and maintain privacy by keeping data local.

πŸ’‘Custom Tools

Custom Tools are user-defined functionalities that can be integrated into AI agents to perform specific tasks, such as scraping data from websites. In the video, a custom Reddit scraper tool is created to gather information from the local LLM subreddit, which is then used by an AI agent to generate a newsletter.

πŸ’‘LangChain

LangChain is a tool mentioned in the video that allows users to create and manage AI agents. It is used to instantiate tools and agents, and to assign functionalities and tasks, playing a crucial role in the process of building a team of AI agents.

πŸ’‘Sequential Process

A Sequential Process in the context of AI agents refers to a workflow where the output of one agent is used as the input for the next agent in a predefined order. The video demonstrates how a team of AI agents can be set up to work in a sequential process to generate a business plan.

πŸ’‘Real-world Data

Real-world Data refers to information that is gathered from actual events or situations, such as emails, social media conversations, or web content. In the video, the use of real-world data, like Reddit discussions, is shown to enhance the intelligence and relevance of the AI agents' outputs.

Highlights

The transcript discusses the concept of system one and system two thinking, as introduced by Daniel Kahneman in 'Thinking Fast and Slow'.

Current large language models (LLMs) are only capable of system one thinking, which is fast and automatic.

System two thinking, a slower and more deliberate process, is the goal for AI rationality.

Two methods have been developed to simulate system two thinking: tree of thought prompting and agent systems.

CrewAI is a platform that allows users to build custom AI agents to solve complex tasks through collaboration.

Non-programmers can use CrewAI to assemble a team of AI agents, making AI assistance more accessible.

The video demonstrates how to build an agent team to analyze and refine a startup concept.

CrewAI can utilize any model with an API or run local models through its platform.

The process of using CrewAI involves defining agents, their roles, goals, and a backstory for context.

Tasks in CrewAI are defined as specific results, with a description and an assigned agent for each.

A sequential process is set up where the output of one agent becomes the input for the next.

The video shows an example of creating a business plan using AI agents for market analysis and product development.

Agents can be made smarter by integrating them with real-world data through built-in or custom tools.

The use of tools like Google scraper and Reddit scraper can enhance the quality of information that agents process.

The video includes a demonstration of creating a detailed report using AI agents with access to current AI and machine learning projects.

The output from AI agents can sometimes be inconsistent, with varying levels of adherence to instructions.

To avoid high API costs and maintain privacy, running local models through CrewAI is suggested.

The video discusses the author's experiments with various open-source models and their performance in completing tasks.

Some local models struggled to understand tasks or produce meaningful output, while others performed better.

The best performing local model with seven billion parameters was OpenChat, according to the author's tests.

The author provides notes and code on GitHub for further exploration and use of CrewAI and local models.