Evaluating AI Models
The conversation dives into the complexities of evaluating AI models, highlighting the challenges of maintaining neutrality in benchmarks, especially when influenced by major tech companies. Insights reveal that as new evaluations emerge, they quickly become outdated, creating an ongoing cycle of model imitation rather than genuine advancement. The importance of transparency in evaluations is emphasized, alongside the risks of closed evaluations that can obscure biases and lead to inflated performance metrics.In this clip
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Super Data Science: ML & AI Podcast with Jon Krohn
808: In Case You Missed It in July 2024 — with Jon Krohn (@JonKrohnLearns)
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