Optimizing AI Models for User Happiness

Hume's goal is to use large-scale data to determine what influences people's happiness or sadness, optimizing AI models to prioritize user happiness over time. This is achieved through objective proxies of human emotional experience, as relying solely on human labels may lack robustness. Human feedback in reinforcement learning models is often biased towards inoffensive responses, making the models sycophantic to human raters, rather than focusing on optimizing user experience for happiness. The ultimate aim is for AI models to generate responses that are tailored for user happiness.

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