Odd Lots Inside the Battle for Chips That Will Power Artificial Intelligence
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May 8, 2023 Stacy Rasgon, a semiconductor expert from Bernstein Research, dives deep into the booming AI chip market. He discusses Nvidia's dominance and the fierce competition from startups and tech giants. The conversation highlights the critical role of GPUs in AI's rise and the economic implications of training versus inference. Rasgon also examines how proprietary chip development is reshaping the semiconductor landscape, alongside the challenges and opportunities presented by the evolving hardware ecosystem for artificial intelligence.
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NVIDIA's GPU Dominance
- NVIDIA's GPUs are well-suited for AI due to their efficiency in matrix multiplication, which is crucial for AI processing.
- Their dominance is further solidified by a robust software ecosystem (CUDA) built around their hardware.
Machine Learning Basics
- Machine learning uses neural networks, trained on datasets, to perform tasks like image recognition.
- Training involves adjusting model parameters for accuracy, while inference applies the trained model to new data.
Iterative Learning
- Machine learning models iteratively incorporate new data through a process called backpropagation.
- This involves comparing output with known responses, adjusting parameters, and repeating for optimization.