Non-technical individuals can teach themselves to code and build chatbots using tools like ChatGPT and fine-tuning models with scraped data.
The current AI landscape is experiencing an explosion of experimentation, with individuals leveraging platforms like GitHub to learn from each other and extend the capabilities of AI tools.
Deep dives
Building Chatbots and Learning to Code in 3 Months
Emmett Homm, a non-technical individual, successfully taught himself to code in just three months and created celebrity chatbots as well as tools to assist others in building similar chatbots. His journey from being non-technical to building chatbots like Andrew Tate and Chamath Palihapitiya fascinated many. Emmett shares insights on how he accomplished this feat by leveraging tools like ChatGPT and fine-tuning models using scraped data. He also explains his process of cleaning data, prompting the AI model, and generating synthetic questions to train the chatbot. Emmett encourages non-technical individuals to tinker with coding environments, use ChatGPT extensively, and acquire a basic understanding of coding components and structure. He also predicts a surge in innovation as more people experiment and push the boundaries of AI tools.
The Cambrian Explosion of AI Experimentation and Learning
The current AI landscape is experiencing an explosion of experimentation, with individuals like Emmett actively tinkering, building, and iterating on AI applications. This rapid exploration has led to remarkable learning opportunities, as failures enable fast learning and facilitate accelerated progress. The accessibility of tools like ChatGPT allows individuals with little to no technical background to venture into AI projects, experiment, and learn. Through platforms like GitHub, people are learning from each other and extending the capabilities of AI tools. The iterative loop of learning from the technology and building tools to enhance the technology results in exponential growth and exciting projects.
Monetization and Future AI Trends
Emmett expresses interest in chatbots and intends to explore their potential applications, such as personal assistants and proprietary information databases for businesses. He aims to improve the infrastructure and tools for building chatbots quickly and effectively. Additionally, he highlights the largely untested area of AI application monetization, especially beyond the base layer providers like OpenAI and Pinecone. Emmett looks forward to seeing how monetization models will evolve, including possibilities such as traditional SaaS models, freemium tiers, or creative approaches. He believes that discovering successful monetization strategies will be crucial for his future projects.
Today in the first of a series on AI learning journeys, NLW is joined by Emmet Halm. At the beginning of 2023, Emmet was not a developer. A couple weeks ago, he released a chatbot that was tuned on the ever controversial Andrew Tate. Along the way, he also released a tool to help others fine tune LLMs on specific media creators. In this conversation, they talk about what it takes to start building in AI, and why there has never been a better time.
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