Scientific discovery in the age of artificial intelligence
Hanchen Wang,Tianfan Fu,Yuanqi Du,Wenhao Gao,Kexin Huang,Ziming Liu,Payal Chandak,Shengchao Liu,Peter Van Katwyk,A Deac,Animashree Anandkumar,Karianne J. Bergen,Carla Gomes,Shirley Ho,Pushmeet Kohli,L. Lasenby,Jure Leskovec,Tie-Yan Liu,Arjun K. Manrai,Debora Marks,Bharath Ramsundar,Le Song,Jimeng Sun,Jian Tang,Petar Veličković,Max Welling,Linfeng Zhang,Connor W. Coley,Yoshua Bengio,Marinka Zitnik +29 more
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TL;DR: This work examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deeplearning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
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About: This article is published in Nature. The article was published on 01 Aug 2023.
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Citations
Generative AI enhances individual creativity but reduces the collective diversity of novel content
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TL;DR: It is found that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers, which point to an increase in individual creativity at the risk of losing collective novelty.
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Can large language models provide useful feedback on research papers? A large-scale empirical analysis
Weixin Liang,Yuhui Zhang,Hancheng Cao,Binglu Wang,Daisy Ding,Xinyu Yang,Kailas Vodrahalli,Siyu He,Daniel Smith,Yian Yin,Daniel A McFarland,James Zou +11 more
TL;DR: An automated pipeline using GPT-4 to provide comments on the full PDFs of scientific papers shows that LLM-generated feedback can help researchers, but also identifies several limitations.
A GPT‐4 Reticular Chemist for Guiding MOF Discovery**
Zhiling Zheng,Zichao Rong,Nakul Rampal,Christian Borgs,Jennifer T Chayes,Omar M. Yaghi +5 more
TL;DR: A GPT-4 Reticular Chemist framework guides MOF discovery through iterative human-AI interaction, enabling the learning of AI from experimental outcomes and the discovery of new MOFs.
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Large Language Models on Graphs: A Comprehensive Survey
Bowen Jin,Gang Liu,Chi Han,Meng Jiang,Heng Ji,Jiawei Han +5 more
TL;DR: A systematic review of scenarios and techniques related to large language models on graphs, including LLM as Predictor, LLM as Encoder, and LLM as Aligner, and compare the advantages and disadvantages of different schools of models is provided.
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