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Philosophers question if ML models truly predict or merely accommodate

By PulseAugur Editorial · [1 sources] ·

A philosophical examination explores the distinction between accommodation and prediction in the context of machine learning models. The analysis suggests that while ML models are often described as predictive, their outputs frequently lean towards accommodation, meaning the evidence they succeed on was part of their training data. This distinction becomes blurred due to the complex relationship between input patterns and the model's selection process during training. The paper delves into the implications of this perspective for concepts like confirmation, novelty, and scientific realism. AI

IMPACT Challenges the common perception of machine learning models as purely predictive, suggesting a stronger role for accommodation in their outputs.

RANK_REASON The item is a philosophical paper discussing machine learning concepts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Philosophers question if ML models truly predict or merely accommodate

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The item is a philosophical paper discussing machine learning concepts. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Do Machine Learning Models Predict?

    Machine learning (ML) models are said to predict. Philosophers draw a line between accommodation and prediction, depending on whether or not the evidence a theory succeeds on was used in constructing it. Does this distinction survive when a theory is replaced by an ML model? We a…

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