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20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton
Aug 28, 2024
Arvind Narayanan, a Princeton professor and co-author of "AI Snake Oil," challenges the myth that simply adding more compute equates to better AI performance. He emphasizes that data quality, not just volume, is crucial for advancements in AI. The conversation dives into the future of AI models, debating whether we'll have a few large dominant models or many specialized ones. Narayanan critiques current generative AI pitfalls and stresses the importance of genuine user experiences over misleading benchmark scores. His insights offer a fresh perspective on AI's evolving landscape.
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Quick takeaways
- Arvind Narayanan argues that adding more compute alone does not guarantee improved AI model performance, as diminishing returns become evident.
- The importance of understanding product-market fit in generative AI is highlighted, stressing the need for genuine product development over mere novelty.
Deep dives
The Diminishing Returns of AI Training
The future of large AI models may be constrained by diminishing returns regarding compute performance. Historically, significant increases in model size have led to improved performance, but this trend appears to be plateauing as data becomes a bottleneck. Many companies have already trained models on nearly all accessible data, and while further compute still aids performance, the gains may not be as impactful as before. The current landscape suggests a shift towards smaller models that maintain capability while being cheaper and more manageable.
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