This podcast episode provides an insightful journey through the evolution of AI methodologies, from the early days of data science characterized by small-scale model building to the transformative rise of foundation models and generative AI. Daniel discusses the significance of model training, the emergence of transfer learning, and the paradigm shift in AI consumption practices driven by user interaction with models through prompts. He emphasizes that as AI continues to evolve, the integration of various methodologies and the emphasis on prompt engineering will be key in shaping the future, reinforcing the notion that AI remains a product of engineering and careful design rather than a magical solution.
