How I Made AI Assistants Do My Work For Me: CrewAI
TLDRThe video script discusses the concept of system 1 and system 2 thinking, highlighting the capabilities of AI language models in simulating these thinking processes. It introduces two methods to enhance AI's rational thinking: tree of thought prompting and platforms like Crew AI for building custom agent systems. The video provides a step-by-step guide on creating a team of AI agents to tackle complex problems, accessing real-world data, and avoiding fees and privacy concerns by running local models. The script also shares personal experiences and insights on the effectiveness of different AI models and tools.
Takeaways
- ๐ง The concept of 'System 1' and 'System 2' thinking is introduced, with 'System 2' being the slower, more deliberate type of thinking that we aim to achieve with AI.
- ๐ Daniel Kahneman's 'Thinking Fast and Slow' is referenced as a source that differentiates between fast (System 1) and slow (System 2) thinking.
- ๐ Large language models (LLMs) like GPT are currently only capable of System 1 thinking, which is fast and subconscious.
- ๐ณ The video discusses two methods to simulate System 2 thinking: 'Tree of Thought' prompting and platforms like Crew and Agent Systems CREI.
- ๐ 'Tree of Thought' prompting involves forcing an LLM to consider an issue from multiple perspectives, akin to a group decision-making process.
- ๐ค Crew AI and Agent Systems CREI allow users to build custom agents that can collaborate to solve complex tasks, even without programming knowledge.
- ๐ The video provides a practical guide on setting up and using Crew AI to define agents and tasks for solving a hypothetical business problem.
- ๐ An example is given where three agents (marketer, technologist, and business development expert) are set up to analyze and refine a startup concept.
- ๐ The importance of defining clear roles and goals for each agent, as well as specific tasks with desired outcomes, is emphasized.
- ๐ ๏ธ The video also explores ways to enhance the intelligence of AI agents by giving them access to real-world data through built-in and custom tools.
- ๐ก The presenter shares personal experiences and insights on the effectiveness of different local models and APIs, highlighting the variability in AI performance.
Q & A
What is the difference between System 1 and System 2 thinking?
-System 1 thinking is fast, subconscious, and automatic, like effortlessly recognizing a familiar face in a crowd. System 2 thinking is slow, conscious, and requires deliberate effort and time, such as when processing a complex problem from various angles.
What are the two methods to simulate rational thinking in AI?
-The two methods are: 1) Tree of thought prompting, which involves forcing the AI to consider an issue from multiple perspectives, and 2) Utilizing platforms like Crew, which allows users to build custom agents or experts that can collaborate to solve complex tasks.
How does Crew AI enable non-programmers to build custom AI agents?
-Crew AI provides a platform that allows users to define agents with specific roles and goals, assign tasks, and create a process for these agents to work together, enabling even non-programmers to build and utilize custom AI agents.
What is the role of a 'market researcher expert' agent in the given example?
-The market researcher expert agent's role is to understand if there is a substantial need for the product and provide guidance on how to reach the widest possible target audience.
How does the 'technologist' agent contribute to the startup concept in the example?
-The technologist agent is responsible for providing analysis and suggestions on how to make the product, in this case, the plugs for Crocs.
What is the main goal of the 'business development expert' agent?
-The business development expert agent's goal is to take into consideration everyone's reports and write a comprehensive business plan.
How can AI agents be made smarter according to the script?
-AI agents can be made smarter by giving them access to real-world, real-time data through built-in tools or custom-made tools that scrape or fetch data from various sources like Reddit or Google search results.
What was the issue with the newsletter generated by the AI agents?
-The issue with the newsletter was that the information quality was not the best, as none of the projects mentioned were currently in the news, and the content was generic and not up-to-date.
How can local models be used instead of OpenAI models to avoid costs and maintain privacy?
-Local models can be used by installing the appropriate open-source models and instructing Crew AI to use the local model instead of the OpenAI model. This requires sufficient RAM and can help avoid paying for API calls and maintain conversation privacy.
What was the best performing local model in the creator's experiment?
-The best performing local model in the creator's experiment was the regular Llama 13 billion parameters model, which managed to incorporate data from the local subreddit into its output, although not in the exact desired format.
What was the main challenge faced when using local models?
-The main challenge was that most local models struggled to understand the specific tasks assigned to them, resulting in generic or irrelevant outputs. Only the regular Llama 13 billion parameters model somewhat met the expectations by including subreddit data in its response.
Outlines
๐ค System 1 and System 2 Thinking in AI
This paragraph introduces the concept of System 1 and System 2 thinking as described in Daniel Kahneman's book 'Thinking Fast and Slow'. It explains that System 2 thinking is a slow, conscious, and deliberate process, while System 1 is fast, subconscious, and automatic. The speaker discusses the limitations of current large language models (LLMs), which are only capable of System 1 thinking. The video aims to explore methods to simulate System 2 thinking in AI, including tree of thought prompting and platforms like Crew and Agent Systems CREI, which allow users to build custom agents to solve complex tasks.
๐ก Building a Team of AI Agents with Crew AI
The speaker demonstrates how to use Crew AI to build a team of AI agents to tackle complex problems. They guide the audience through setting up three agents with specific roles: a market researcher, a technologist, and a business development expert. The process involves defining tasks, creating a 'crew' of agents, and assigning them specific goals. The speaker then runs a script to generate a business plan with the help of these agents, highlighting the potential of AI to streamline and automate certain aspects of work.
๐ Enhancing AI Agents with Real-World Data
The paragraph discusses methods to make AI agents smarter by giving them access to real-world data. It outlines two approaches: using built-in tools available in LangChain, such as text-to-speech and Google search tools, and creating custom tools. The speaker shares their experience of building a custom Reddit scraper tool to gather information for a newsletter about AI and machine learning innovations, emphasizing the flexibility and control that custom tools provide.
๐ฐ Cost-Effective AI Solutions with Local Models
In this section, the speaker shares their experience with local models as an alternative to expensive API calls. They discuss the challenges of running large models on a laptop with limited RAM and the results of testing various open-source models. The speaker identifies the best and worst performing models based on their ability to understand tasks and generate meaningful output. They conclude with a recommendation to use a specific local model that successfully incorporated data from a subreddit into a newsletter.
Mindmap
Keywords
๐กSystem 1 and System 2 thinking
๐กAI Assistance
๐กTree of Thought Prompting
๐กCrew AI
๐กAgent Systems
๐กReal-world Data
๐กLocal Models
๐กCustom Tools
๐กOpen AI
๐กStartup Concept
Highlights
The concept of 'System 1' and 'System 2' thinking is introduced, with System 1 being fast and subconscious, while System 2 is slow and conscious.
Large language models, such as GPT-4, are currently only capable of System 1 thinking, which is fast but lacks deep problem-solving capabilities.
Two methods have been developed to simulate System 2 thinking in AI: tree of thought prompting and platforms like Crew AI and Agent Systems CREST.
Tree of thought prompting involves forcing an LLM to consider an issue from multiple perspectives, leading to a collective decision-making process.
Crew AI and Agent Systems CREST allow users, even non-programmers, to build custom agents that can collaborate to solve complex tasks.
The video demonstrates how to assemble a team of AI agents to solve complex problems and make them smarter by integrating real-world data.
A detailed guide on setting up three AI agents (marketer, technologist, and business development expert) for a startup concept is provided.
Tasks should be defined as specific results, and agents should be assigned blueprints for different goals, with a sequential process for collaboration.
The use of built-in Lang Chain tools, such as text-to-speech and Google data access, can enhance the capabilities of AI agents by providing real-time data.
Custom tools can be created for AI agents to scrape specific information, such as the latest posts from the local LLAMA subreddit.
The experiment with local models shows that not all models understand tasks correctly, and some may produce generic or irrelevant outputs.
The best performing local model with seven billion parameters was found to be open chat, despite not containing data from the local LLAMA subreddit.
Running local models can help avoid the costs associated with API calls and maintain privacy, but they may have limitations in understanding complex tasks.
The video provides a comprehensive guide on using Crew AI for building AI agent teams, including code and prompts for various tasks.
The importance of selecting the right AI model and tools for specific tasks is emphasized, as different models have varying levels of success in understanding and executing tasks.
The video concludes with a recommendation to explore the GitHub repository for notes on local models and code used in the demonstration.
The transcript discusses the potential of AI in automating research and information gathering, highlighting the time-saving benefits.
The video encourages viewers to share their experiences with Crew AI and explore the possibilities of AI agent teams for complex problem-solving.