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Ep 43: CEO/Co-Founder of Contextual AI Douwe Kiela Reaction to o1, What’s Next in Reasoning and Innovations in Post-Training
Unsupervised Learning
Evolution of AI Development and Evaluation Frameworks
The concept of an AI developer has evolved significantly, shifting from a focus on traditional machine learning expertise to a focus on proficiency in utilizing APIs. This change necessitates a reimagining of evaluation frameworks to make them more accessible for modern developers who may not have deep statistical or machine learning backgrounds. There is a growing realization that effective evaluation processes should integrate diverse methodologies, as traditional methods may not serve the needs of contemporary AI practitioners effectively. In the past year, there have been notable shifts in perspectives regarding synthetic data, agentic workflows, and evaluative metrics. Initial skepticism towards certain AI strategies, such as chain of thought methodologies, has transformed into recognition of their efficacy when combined with broader approaches. The interplay of these various advancements is likely to increase the overall performance of language models, emphasizing the importance of innovative intersections in AI development.
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