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William Beauchamp, Founder of two $100M+ companies, Chai Research and Seamless Capital, discusses the future of AI models and the differences between search engine and reasoning engine approaches. They explore language as a representation of thought space and the challenges of scaling a software engineering team. The connection between risk-taking and creativity in building an AI startup is highlighted, along with the value of fine-tuning in language models and potential for federating access to copyright content.
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Quick takeaways
- Fine-tuning language models, prompt engineering, and instruction training optimize language model performance.
- The value in language models lies in curated and fine-tuned data, and a market for data retrieval may emerge.
Deep dives
Building Language Models and the Power of Fine-tuning
The guest discusses the importance of fine-tuning language models to improve their performance. He emphasizes that increasing the size of the model alone does not necessarily lead to better results. Fine-tuning, reinforcement learning, prompt engineering, and instruction training are key factors in optimizing the performance of language models. He also highlights that small teams of specialized researchers and data scientists can create models that outperform larger monolithic companies, and this distributed approach to model development can be leveraged to build an exceptional language model.
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Listen to the best highlights from the podcasts you love and dive into the full episode