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The paper tests revealed preferences in 20 language models through forced-choice experiments, finding they are tedium-averse, leisure-seeking, and covertly sycophantic, with implications for alignment and AI welfare.
The article questions whether theoretical principles still guide machine learning practices, highlighting how many once-standard theories have been challenged by empirical evidence.
Lilian Weng's blog post provides a comprehensive overview of scaling laws in deep learning, covering their derivation, compute-optimal allocation, and the debate between Kaplan et al. and Chinchilla.
Stanford REAP and CoPaper.AI have released Auto-Empirical Research Skills (AERS), an open-source toolkit with over 23,000 agent skills that automates the entire empirical research pipeline for social sciences, from topic selection to journal submission.
Auto-Empirical-Research-Skills (AERS) is an open-source toolkit that automates the entire empirical research pipeline using 23,000+ AI agent skills, from data cleaning to submission-ready drafts.
Google announces Empirical Research Assistance (ERA), an AI tool using Gemini to write and optimize scientific code, now published in Nature and being rolled out as part of Gemini for Science to help scientists worldwide accelerate computational discovery.
Google DeepMind introduces a computational discovery prototype that uses AlphaEvolve and Empirical Research Assistance to develop and score thousands of code variations in parallel, enabling faster testing of modelling approaches for epidemiology.