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17min chapter
#3 – Thomas Wolf: Open-Source AI Scaling Laws
Scaling Theory
Exploring the Spectrum of Openness in AI Models
This chapter delves into the various levels of openness in AI models, from fully open source to closed proprietary models, examining components like data, code, and weights. Discussions include the benefits of foundation models with increasing returns, potential implications of lawsuits such as the New York Times versus OpenAI, concerns about power concentration with valuable data licenses, and the impact of regional laws on AI development. The chapter also explores how big tech companies strategically leverage both open and closed-source models, and debates the transition from open to closed source in tech projects amidst ethical considerations and survival in the industry.
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