Big Technology Podcast
Generative AI 101: Tokens, Pre-training, Fine-tuning, Reasoning — With Dylan Patel
Apr 23, 2025
Dylan Patel, Founder and CEO of SemiAnalysis, specializes in semiconductor and generative AI research. He dives into how generative AI operates, breaking down the roles of tokens, pre-training, and fine-tuning. The discussion highlights the leap in reasoning capabilities thanks to human feedback, and the efficiency breakthroughs from companies like DeepSeek. Patel also addresses the growing race for colossal AI data centers and speculates on what GPT-5’s hybrid training could achieve. This conversation is a must-listen for anyone curious about the future of AI!
42:09
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Quick takeaways
- Tokens serve as essential units in generative AI, enabling sophisticated language processing by capturing nuanced relationships between words through complex vector representations.
- The advancement of reasoning capabilities in AI allows for deeper problem evaluation, leading to improved performance in tasks like coding and mathematics through enhanced model training methodologies.
Deep dives
Understanding Tokens and Their Role
Tokens are essential units in generative AI that represent chunks of words, enabling models to process language efficiently. Each token is not limited to a single numerical value but is represented by multiple vectors that capture the nuances and relationships between words. For instance, the model learns that 'king' and 'queen' share similarities while also differing on specific attributes, such as gender. This intricate representation allows the model to predict language patterns and generate coherent text by converting numerical data back into understandable language.
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