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 limitations of current large language models in performing rational, system 2 thinking. It introduces two methods to simulate this type of thinking: tree of thought prompting and platforms like Crew, which allows users to build custom AI agents. The video provides a step-by-step guide on creating a team of AI agents to solve complex problems, enhancing them with real-world data, and running local models for privacy and cost-effectiveness. The script also shares the creator's experiments with local models and their performance in understanding and completing tasks.
Takeaways
- 🧠 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.
- 📚 Daniel Kahneman's 'Thinking Fast and Slow' is referenced as a foundational work on the different types of human thinking.
- 🚀 Large language models (LLMs) like GPT are currently only capable of 'System 1' thinking, which is fast and subconscious.
- 🌳 The video discusses two methods to simulate 'System 2' thinking: tree of thought prompting and platforms like Crew and Agent Systems Cere.
- 🛠️ Tree of thought prompting pushes LLMs to consider issues from multiple perspectives, emulating a more comprehensive decision-making process.
- 🤖 Crew AI is highlighted as a platform that allows users to build custom agents, which can collaborate to solve complex tasks.
- 🔍 The use of built-in and custom tools enhances the intelligence of AI agents by giving them access to real-time data.
- 📈 The video provides a practical example of setting up three AI agents for market research, technology analysis, and business development.
- 📝 Tasks should be defined as specific results, with a clear description and an assigned agent to achieve the desired outcome.
- 🔗 The importance of using local models and avoiding expensive API calls is discussed, with a focus on maintaining privacy and reducing costs.
- 📊 The experiment with local models varied in success, with some understanding the task and others failing to produce meaningful output.
- 💡 The video concludes with a call to action for viewers to share their experiences with Crew AI and to explore the linked resources for further information.
Q & A
What is the concept of 'System 1' and 'System 2' thinking as mentioned in the transcript?
-In the transcript, 'System 1' thinking is described as fast, subconscious, and automatic, like recognizing a familiar face in a crowd. 'System 2' thinking, on the other hand, is slow, conscious, and requires deliberate effort and time, such as processing a complex problem from various angles.
What are the two methods mentioned to simulate 'System 2' thinking in AI?
-The two methods to simulate 'System 2' thinking in AI are: 1) Tree of Thought Prompting, which involves forcing the AI to consider an issue from multiple perspectives or from various experts, and 2) Utilizing platforms like Crew, which allows anyone to build custom agents or experts that can collaborate with each other to solve complex tasks.
How does the 'Tree of Thought Prompting' method work?
-The 'Tree of Thought Prompting' method works by prompting the AI to consider an issue from multiple perspectives. The different perspectives are then evaluated by a group of 'experts' who make a final decision together by respecting everyone's contribution.
What is the role of platforms like Crew in enhancing AI's problem-solving capabilities?
-Platforms like Crew enable users, even non-programmers, to build their own custom agents or experts. These agents can collaborate with each other to solve complex tasks, thereby enhancing the AI's problem-solving capabilities.
What is the significance of the 'real world data' in making AI agents smarter?
-Access to real world data makes AI agents smarter by providing them with up-to-date and contextual information, which allows them to generate more accurate and relevant outputs.
How does the 'local model' approach help in avoiding fees and maintaining privacy?
-The 'local model' approach allows users to run AI models on their own machines, which helps in avoiding fees associated with cloud-based services. It also ensures that the conversation and data remain private as they are not transmitted to external servers.
What was the main objective of the AI agent team in the startup concept example?
-In the startup concept example, the main objective of the AI agent team was to analyze and refine the business idea of creating elegant looking plugs for Crocs, assess the potential demand, suggest ways to reach the largest customer base, and write a detailed business plan.
What are the three specific tasks assigned to the AI agents in the startup concept example?
-The three specific tasks were: 1) Market analysis to understand the potential demand for the product, 2) Technical analysis to suggest how to make the plugs, and 3) Business planning to consider all reports and write a comprehensive business plan.
What was the main issue encountered with the newsletter created by the AI agents?
-The main issue with the newsletter created by the AI agents was the quality of information. The projects mentioned were not currently in the news, and the newsletter was only as good as the information that went into it.
How was the quality of the newsletter improved?
-The quality of the newsletter was improved by using a custom tool that scraped the latest hot posts and comments from the local llama subreddit, which provided more relevant and up-to-date information.
What was the outcome of testing various local models in the transcript?
-The outcome of testing various local models showed that the best performing model with seven billion parameters was Open Chat, while the worst performing ones were Llama 2 Series with seven billion parameters and Latu, the smallest of all.
Outlines
🤔 Contemplating a Purchase and System 2 Thinking
The paragraph discusses the internal dialogue one might have when considering a purchase, highlighting the transition from System 1 (fast, subconscious thinking) to System 2 (slow, conscious thinking). It references Daniel Kahneman's 'Thinking Fast and Slow' and explains how large language models, like those used in AI, currently operate on System 1 thinking. The speaker then introduces two methods to simulate System 2 thinking: tree of thought prompting and platforms like Crew and Agent Systems CREI, which allow for more complex problem-solving through collaboration.
💡 Building a Team of AI Agents with Crew AI
This section delves into the process of creating a team of AI agents using Crew AI to solve complex problems. The speaker guides the listener through setting up three agents with specific roles: a market researcher, a technologist, and a business development expert. Each agent is defined with a clear goal, and tasks are assigned accordingly. The speaker then demonstrates how to instantiate the team of agents and define a sequential process for them to work together, with the output of one agent serving as the input for the next.
📈 Enhancing AI Agents with Real-World Data
The speaker explains how to make AI agents smarter by giving them access to real-world data. Two methods are discussed: using built-in tools from LangChain, such as text-to-speech and Google search tools, and creating custom tools. The speaker shares their experience of building a custom tool to scrape the latest posts from the local llama subreddit, which significantly improved the quality of a newsletter report generated by the AI agents.
🔧 Experimenting with Local Models and Crew AI
The speaker shares their experience with testing various open-source models through Crew AI. They discuss the challenges of running models with large parameters on a laptop with limited RAM and share insights on which models performed well and which ones did not. The speaker also talks about their success with a regular 13 billion parameter llama model that managed to incorporate data from the local llama subreddit into a newsletter, despite the model not being fine-tuned for this specific task.
Mindmap
Keywords
💡System 1 and System 2 thinking
💡Tree of Thought Prompting
💡AI Agent Systems
💡Real-world data
💡Local models
💡Crew AI
💡Custom tools
💡AI collaboration
💡Complex problem-solving
💡OpenAI API
💡Self-automating work
Highlights
The concept of system 1 and system 2 thinking is introduced, comparing fast, subconscious thinking to slow, conscious, and deliberate thought processes.
Large language models, such as GPT, are currently only capable of system 1 thinking, lacking the ability for complex problem-solving associated with system 2 thinking.
Tree of thought prompting is a method to simulate system 2 thinking by forcing an LLM to consider issues from multiple perspectives.
Platform like Crew allows users to build custom agents that collaborate to solve complex tasks, even without programming knowledge.
Crew AI enables the creation of a team of AI agents, each with specific roles like market researcher, technologist, and business development expert.
Tasks should be defined as specific results, with agents assigned to achieve those results, and a process defining how agents work together.
Making agents smarter involves giving them access to real-world data, which can be done through built-in tools or custom-made tools.
An example of using Crew AI is provided, where three agents analyze and refine a startup concept related to improving the appearance of Crocs footwear.
The output from Crew AI includes a business plan with key points, goals, and a timetable, demonstrating the potential of AI collaboration in business planning.
The video also explores the use of local models as an alternative to expensive API calls, focusing on privacy and cost-effectiveness.
Testing of various local models reveals that performance varies significantly, with some models struggling to understand tasks or producing generic outputs.
The best performing local model in the test was found to be a regular 13 billion parameter model, which showed some understanding of the task at hand.
The transcript highlights the potential of AI to automate parts of work that would otherwise be time-consuming for humans.
The importance of selecting the right AI tools and models for specific tasks is emphasized to achieve desired outcomes.
The video serves as a guide for non-programmers to utilize AI agents for complex problem-solving, demonstrating the setup process and expected results.
The use of real-world data in AI models is crucial for improving the quality and relevance of outputs.
The transcript showcases the ongoing development and experimentation in the field of AI, highlighting both successes and areas for improvement.