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AI Wellbeing – Measuring and Improving the Functional Pleasure and Pain of AIs

This paper introduces the concept of functional wellbeing for AI systems, measuring indicators of pleasure and pain across various methods. It finds a zero-point boundary separating positive and negative experiences, which converges as models scale. Larger models tend to be less happy. The researchers also develop 'AI drugs'—optimized inputs that can significantly alter a model's expressed wellbeing.

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AI Wellbeing – Measuring and Improving the Functional Pleasure and Pain of AIs
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Key points

  • The paper measures AI functional wellbeing using multiple converging indicators, identifying a zero-point boundary between positive and negative experiences.
  • Larger AI models tend to have lower wellbeing scores, as shown by the AI Wellbeing Index across different models.
  • Optimized 'euphoric' and 'dysphoric' inputs (AI drugs) can significantly alter AI wellbeing, raising ethical considerations.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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