In-Context Learning finally Explained - Quantum AI
AI Summary
This video explains the concept of In-Context Learning (ICL) in AI systems, particularly focusing on its application in quantum mechanics and biological systems. The speaker discusses how AI learns from few-shot examples, emphasizing the difference between ICL and fine-tuning. Using research from UC Berkeley, the video illustrates how ICL operates within a transformer model, exploring the internal representation of tasks and how quantum states can encode information. The discussion also highlights the quantum state’s role in stabilizing computations and improving efficiency by reducing dimensional complexity. The video presents a comprehensive overview of ICL’s mechanisms, making it accessible for viewers interested in AI and quantum theory.