Machine Learning Street Talk (MLST) cover image

Kenneth Stanley created a new social network based on serendipity and divergence

Machine Learning Street Talk (MLST)

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Exploring Minimal Criterion in Evolution and Subjective Domains

The concept of minimal criterion suggests a unique interpretation of evolution where success is binary - either achieved or not, without a continuum of quality. This approach emphasizes divergence over convergence by maintaining a minimal quality standard without prioritizing ranking. In subjective domains like text and speech, a minimal criterion standard allows for divergence while ensuring a base level of quality, preventing degeneration but leaving room for variety. It challenges the idea of ranking subjective content, highlighting the importance of individual interpretation and interaction with the material. The minimal criterion view offers a balance between quality control and freedom in evolutionary processes, promoting a nuanced understanding of evolution in both objective and subjective contexts.

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