This summary of the video was created by an AI. It might contain some inaccuracies.
00:00:00 – 00:19:22
The video explores advanced AI methods, focusing on enhancing decision-making in AI models by integrating slow, deliberate (system two) thinking alongside the fast, automatic (system one) approach. Drawing from Daniel Kahneman's "Thinking, Fast and Slow" and Andrej Karpathy's insights, the speaker discusses strategies like "Tree of Thought" prompting and CREAI collaborative platforms. This transition from theoretical concepts to practical applications includes creating AI agents within environments like VS Code using tools like Crew AI and OpenAI's GPT-4.
Key projects demonstrate the development of business plans and reports, utilizing specialized agents (e.g., marketers, technologists, researchers) to tackle tasks sequentially. The speaker emphasizes practical steps, including setting up development environments, defining agents' roles and tasks, and enhancing outputs with real-time data via tools like 11 Labs, YouTube, Google, and Wikipedia. Methods for scraping data from platforms like Reddit and overcoming challenges with subpar automated outputs through custom tools are also detailed.
Finally, the video addresses the technical hurdles of running local AI models, noting significant hardware demands and variable model performance. Experiments with different models (e.g., Llama, Falcon, OpenChat) reveal mixed outcomes, with some failing to execute tasks reliably. The speaker provides recommendations based on these tests and invites viewers to share their experiences, underpinning the importance of ongoing experimentation and community feedback in improving AI applications.
00:00:00
In this segment of the video, the speaker discusses the concept of decision-making, contrasting slow, deliberate thinking (system two) with fast, automatic thinking (system one). Referencing Daniel Kahneman’s book “Thinking, Fast and Slow” and a video by Andrej Karpathy, the speaker highlights that current large language models (LLMs) operate primarily through system one thinking. The speaker then introduces two methods to simulate system two thinking in AI: “Tree of Thought” prompting and collaborative platforms like CREAI. These methods allow AI to approach problems from multiple perspectives or through custom agents built by users. The segment concludes with a promise to demonstrate how to assemble and enhance AI agents, access real-world data, and run models locally to protect privacy and reduce costs.
00:03:00
In this part of the video, the presenter guides viewers on building an agent team to analyze and refine a startup concept, emphasizing simplicity even for non-programmers. The process starts with setting up a development environment, particularly in VS Code, creating, and activating a virtual environment. The next steps involve installing Crew AI, importing necessary modules and packages, and setting an OpenAI key with a preference for using GPT-4 over GPT-3.5 for better results.
Three agents are defined: a market researcher expert, a technologist, and a business development expert. Each agent is assigned a specific role, a clear goal, and a backstory to clarify their function. Verbos is set to True to enable detailed outputs and agent collaboration. The final step focuses on defining tasks for the agents—these should be specific with clear results, like a detailed business plan or market analysis, and should include descriptive details about the tasks.
00:06:00
In this part of the video, the speaker explains the creation of a project involving three specific tasks to develop elegant plugs for Crocs. They assign these tasks to different agents: a marketer to analyze demand and reach customers, a technologist to provide manufacturing analysis and suggestions, and a business consultant to compile the reports into a business plan. The agents work sequentially, each building upon the previous agent’s output. The speaker then runs the process, producing a business plan with detailed points, business goals, and a time schedule, including the use of 3D printing, machine learning, and sustainable materials. Finally, the speaker discusses enhancing agent intelligence with tools that provide real-time data, mentioning options like 11 Labs text-to-speech, YouTube, Google data, and Wikipedia integration.
00:09:00
In this part of the video, the speaker outlines a process for creating a detailed report in the form of a blog or newsletter about the latest AI and machine learning innovations. They utilize a team of three agents: a researcher, a technical writer, and a writing critic, each with specific tasks to produce a report with 10 paragraphs, bolded project names, and links to each project. The speaker uses a Google scraping tool to fetch search results and assigns the tool to an agent to generate the initial draft. However, the quality of the information is subpar, prompting the speaker to seek better information sources. They mention using custom tools and highlight the utility of pre-built tools like ‘human in the loop.’ Finally, the speaker plans to scrape data from the local Llama subreddit using a custom tool for more accurate and relevant content.
00:12:00
In this segment, the presenter explains the process of creating a Custom Tool using a class called browser tools and describes the scrape Reddit method. This method involves initializing a pro-Redit object with client ID, client secret, and user agent, selecting a subreddit to scrape, and iterating through the hottest posts to extract titles, URLs, and comments. The process includes handling API exceptions and compiling the scraped data into a list of dictionaries. The presenter also mentions copying code from a previous tool and shows the results obtained with GPT-4, highlighting the efficiency in automating research tasks. However, there are some inconsistencies in outputs from GPT-4. The presenter also tests the Gemini Pro API, which produced underwhelming results. Lastly, the cost of running scripts is discussed, emphasizing the need to avoid pricey API calls and maintain privacy with local models.
00:15:00
In this part of the video, the speaker discusses testing 13 open-source models and only finding one capable of completing the task to some extent, which was unexpected. They reveal the hardware requirements for running these models locally, emphasizing needing substantial RAM (8 GB for 7 billion parameters, 16 GB for 13 billion, and 32 GB for 33 billion parameters). Despite having a laptop with 16 GB of RAM, the speaker struggled to run certain models like Falcon and Vuna, which resulted in crashes.
They explain how to run local models using Lang chain and underscore the importance of setting the model correctly to avoid defaulting to CH GPD. Among the models tested, the worst performers included the llama 2 series with 7 billion parameters, which produced poor results. The best-performing model in their tests was OpenChat, which provided a decent output but failed to understand the task fully.
The speaker tried various approaches, such as prompt variations and parameter adjustments, but none improved the output quality. They conclude by considering testing more models with 13 billion parameters as a potential next step.
00:18:00
In this part of the video, the speaker discusses their experience running various AI models on their laptop to generate a newsletter. Initially, they tried the Llama 13 billion chat and text models in full precision, assuming the larger models would perform better. However, the results were disappointing, producing generic texts about self-driving cars and failing to mimic actual Reddit conversations. Out of desperation, the speaker tried a regular, non-fine-tuned Llama 13 billion parameters model, which surprisingly utilized subreddit data effectively, though the output wasn’t perfect. The speaker shares their notes on which models to avoid and which ones were acceptable on their GitHub. They also invite viewers to share their experiences with AI models and thank them for watching.