Part 2/5:
Huang then discussed the profound shift from "Software 1.0," where programmers explicitly coded algorithms, to "Software 2.0," where machine learning models learn from data to perform tasks. This transition has led to a reinvention of the entire computing stack, from hardware to software development.
The key insight is that AI models, rather than human-written code, are now the driving force behind software development. These models, trained on massive datasets, can learn to perform a wide range of tasks, from image recognition to protein structure prediction. Huang referred to this as the development of a "universal function approximator," capable of understanding and translating between various data modalities.
The Rise of AI Agents
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