Practical AI AI is more than GenAI
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Sep 5, 2024 The discussion dives into the fascinating evolution of artificial intelligence, charting its journey from statistical methods to generative models. Listeners learn how different AI methodologies are interrelated, emphasizing the importance of model training and data representation. This exploration provides a more comprehensive understanding of the AI landscape beyond just generative AI, highlighting the practical applications of these technologies.
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AI vs. Generative AI
- Many think AI means only Generative AI, but AI encompasses broader concepts.
- Generative AI's emergence wasn't spontaneous; it's part of a larger AI, machine learning, and data science evolution.
Early Data Science Focus
- Early data science (2010-2017) focused on smaller models, sometimes neural networks.
- The primary role involved curating example inputs/outputs (training data) and creating parameterized software functions.
Foundation Models and Transfer Learning
- Around 2017, foundation models and transfer learning emerged.
- Large models, trained on massive datasets, offered good starting points for fine-tuning, reducing the need for extensive data.