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Challenges in Machine Learning and Scientific Research

This chapter explores the limitations of large language models in scientific research, emphasizing the disconnect between language representations and scientific truths. It critiques the incentive structures in academia that prioritize paper counts over replication, leading to unreliable scientific findings. The discussion also introduces a novel discovery engine tool that enhances data analysis in plant biology and addresses the complexities of developing climate-resistant crops.

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