The article explores the evolution of data acquisition strategies for AI-first startups, emphasizing the challenges faced in obtaining quality data in the past.
It mentions the changes in data acquisition needs over the years, including the importance of immense amounts of data and advancements in tools and techniques.
The article also discusses the collaboration between Moritz and Air Street Press to provide updates for AI-first founders in 2024.
It delves into the use of large generative models like LLMs and LMMs for synthetic data generation in various fields such as NLP and computer vision.
The article explains the two main methods of synthetic data generation: self-improvement and distillation, along with the controversy surrounding these approaches.
Furthermore, it touches upon the role of LLMs as labellers, highlighting their ability to label text datasets efficiently and consistently.
Source: https://press.airstreet.com/p/data-acquisition-strategies-for-ai
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