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The promise of generative AI for suicide prevention in India Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-02-06 Tanmoy Chakraborty, Koushik Sinha Deb, Himanshu Kulkarni, Sarah Masud, Suresh Bada Math, Gayatri Oke, Rajesh Sagar, Mona Sharma
The World Health Organization (WHO) estimates a global suicide rate of 9 per 100,000 people, amounting to 720,000 preventable deaths each year. Despite concerted multisectoral efforts, suicide prevention remains a complex public health challenge, shaped by the interplay of socioeconomic, cultural and stress-related factors. In India, the decriminalization of suicide via the 2017 Mental Healthcare Act1
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Discovering fully semantic representations via centroid- and orientation-aware feature learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-02-06 Jaehoon Cha, Jinhae Park, Samuel Pinilla, Kyle L. Morris, Christopher S. Allen, Mark I. Wilkinson, Jeyan Thiyagalingam
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Preserving and combining knowledge in robotic lifelong reinforcement learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-02-05 Yuan Meng, Zhenshan Bing, Xiangtong Yao, Kejia Chen, Kai Huang, Yang Gao, Fuchun Sun, Alois Knoll
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Why the carbon footprint of generative large language models alone will not help us assess their sustainability Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-02-03 Leonie N. Bossert, Wulf Loh
There is a growing awareness of the substantial environmental costs of large language models (LLMs), but discussing the sustainability of LLMs only in terms of CO2 emissions is not enough. This Comment emphasizes the need to take into account the social and ecological costs and benefits of LLMs as well.
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Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-28 Jeremy Wohlwend, Anusha Nathan, Nitan Shalon, Charles R. Crain, Rhoda Tano-Menka, Benjamin Goldberg, Emma Richards, Gaurav D. Gaiha, Regina Barzilay
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A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-28 Chenpeng Yu, Xing Fang, Shiye Tian, Hui Liu
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Machine learning solutions looking for PDE problems Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-27
Machine learning models are promising approaches to tackle partial differential equations, which are foundational descriptions of many scientific and engineering problems. However, in speaking with several experts about progress in the area, questions are emerging over what realistic advantages machine learning models have and how their performance should be evaluated.
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Evolutionary optimization of model merging recipes Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-27 Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha
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Moving towards genome-wide data integration for patient stratification with Integrate Any Omics Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-23 Shihao Ma, Andy G. X. Zeng, Benjamin Haibe-Kains, Anna Goldenberg, John E. Dick, Bo Wang
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What large language models know and what people think they know Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-21 Mark Steyvers, Heliodoro Tejeda, Aakriti Kumar, Catarina Belem, Sheer Karny, Xinyue Hu, Lukas W. Mayer, Padhraic Smyth
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A quantitative analysis of knowledge-learning preferences in large language models in molecular science Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-17 Pengfei Liu, Jun Tao, Zhixiang Ren
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A unified evolution-driven deep learning framework for virus variation driver prediction Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-17 Zhiwei Nie, Xudong Liu, Jie Chen, Zhennan Wang, Yutian Liu, Haorui Si, Tianyi Dong, Fan Xu, Guoli Song, Yu Wang, Peng Zhou, Wen Gao, Yonghong Tian
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A machine learning approach to leveraging electronic health records for enhanced omics analysis Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-16 Samson J. Mataraso, Camilo A. Espinosa, David Seong, S. Momsen Reincke, Eloise Berson, Jonathan D. Reiss, Yeasul Kim, Marc Ghanem, Chi-Hung Shu, Tomin James, Yuqi Tan, Sayane Shome, Ina A. Stelzer, Dorien Feyaerts, Ronald J. Wong, Gary M. Shaw, Martin S. Angst, Brice Gaudilliere, David K. Stevenson, Nima Aghaeepour
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Learning from models beyond fine-tuning Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-16 Hongling Zheng, Li Shen, Anke Tang, Yong Luo, Han Hu, Bo Du, Yonggang Wen, Dacheng Tao
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Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-15 Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian
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The design space of E(3)-equivariant atom-centred interatomic potentials Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-15 Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
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Visual cognition in multimodal large language models Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-15 Luca M. Schulze Buschoff, Elif Akata, Matthias Bethge, Eric Schulz
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Causal chambers as a real-world physical testbed for AI methodology Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-15 Juan L. Gamella, Jonas Peters, Peter Bühlmann
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Exploring scalable medical image encoders beyond text supervision Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-13 Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor, Kenza Bouzid, Valentina Salvatelli, Maximilian Ilse, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Matthew P. Lungren, Maria Teodora Wetscherek, Noel Codella, Stephanie L. Hyland, Javier Alvarez-Valle, Ozan Oktay
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Modern maxims for an AI oracle Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-13 M. J. Crockett
As powerful institutions increasingly promote AI systems, efforts to align those systems with human morality have grown. An open-source AI system aims to predict human moral judgments across a broad spectrum of everyday situations expressed in natural language. Identifying the limitations of such systems offers important insights for future work.
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Investigating machine moral judgement through the Delphi experiment Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-13 Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny T. Liang, Sydney Levine, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jack Hessel, Jon Borchardt, Taylor Sorensen, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini, Yejin Choi
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Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-06 Yonghan Yu, Ming Li
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Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI Nat. Mach. Intell. (IF 18.8) Pub Date : 2025-01-02 Pragalbh Vashishtha, Hitesh Gupta Kattamuri, Nikhil Thawari, Murugaiyan Amirthalingam, Rohit Batra
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Sequential memory improves sample and memory efficiency in episodic control Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-31 Ismael T. Freire, Adrián F. Amil, Paul F. M. J. Verschure
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ARNLE model identifies prevalence potential of SARS-CoV-2 variants Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-31 Yuqi Liu, Jing Li, Peihan Li, Yehong Yang, Kaiying Wang, Jinhui Li, Lang Yang, Jiangfeng Liu, Leili Jia, Aiping Wu, Juntao Yang, Peng Li, Hongbin Song
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Delineating the effective use of self-supervised learning in single-cell genomics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-27 Till Richter, Mojtaba Bahrami, Yufan Xia, David S. Fischer, Fabian J. Theis
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Strategies needed to counter potential AI misuse Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-18
Researchers urgently need more guidance to help them identify and mitigate potential risks when designing projects that involve AI developments.
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Seeking clarity rather than strong opinions on intelligence Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-18
Clear descriptions of intelligence in both living organisms and machines are essential to avoid confusion, sharpen thinking and guide interdisciplinary research. A Comment in this issue encourages researchers to answer key questions to improve clarity on the terms they use.
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Leveraging ancestral sequence reconstruction for protein representation learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-18 D. S. Matthews, M. A. Spence, A. C. Mater, J. Nichols, S. B. Pulsford, M. Sandhu, J. A. Kaczmarski, C. M. Miton, N. Tokuriki, C. J. Jackson
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Limitations in odour recognition and generalization in a neuromorphic olfactory circuit Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-16 Nik Dennler, André van Schaik, Michael Schmuker
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Reply to: Limitations in odour recognition and generalization in a neuromorphic olfactory circuit Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-16 Roy Moyal, Nabil Imam, Thomas A. Cleland
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Envisioning better benchmarks for machine learning PDE solvers Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-13 Johannes Brandstetter
Tackling partial differential equations with machine learning solvers is a promising direction, but recent analysis reveals challenges with making fair comparisons to previous methods. Stronger benchmark problems are needed for the field to advance.
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Discussions of machine versus living intelligence need more clarity Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-13 Nicolas Rouleau, Michael Levin
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Kernel approximation using analogue in-memory computing Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-13 Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos, Corey Lammie, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian
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Stable Cox regression for survival analysis under distribution shifts Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-13 Shaohua Fan, Renzhe Xu, Qian Dong, Yue He, Cheng Chang, Peng Cui
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Reply to: Deeper evaluation of a single-cell foundation model Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-12 Fan Yang, Fang Wang, Longkai Huang, Linjing Liu, Junzhou Huang, Jianhua Yao
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Deeper evaluation of a single-cell foundation model Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-12 Rebecca Boiarsky, Nalini M. Singh, Alejandro Buendia, Ava P. Amini, Gad Getz, David Sontag
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Successful implementation of the EU AI Act requires interdisciplinary efforts Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-10 Christian Montag, Michèle Finck
The EU Artificial Intelligence Act bans certain “subliminal techniques beyond a person’s consciousness”, but uses undefined legal terms. Interdisciplinary efforts are needed to ensure effective implementation of the legal text.
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An interpretable RNA foundation model for exploring functional RNA motifs in plants Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-09 Haopeng Yu, Heng Yang, Wenqing Sun, Zongyun Yan, Xiaofei Yang, Huakun Zhang, Yiliang Ding, Ke Li
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Evaluating generalizability of artificial intelligence models for molecular datasets Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-06 Yasha Ektefaie, Andrew Shen, Daria Bykova, Maximillian G. Marin, Marinka Zitnik, Maha Farhat
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Learning spatiotemporal dynamics with a pretrained generative model Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-06 Zeyu Li, Wang Han, Yue Zhang, Qingfei Fu, Jingxuan Li, Lizi Qin, Ruoyu Dong, Hao Sun, Yue Deng, Lijun Yang
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Towards a personalized AI assistant to learn machine learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-05 Pascal Wallisch, Ibrahim Sheikh
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LLM-based agentic systems in medicine and healthcare Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-05 Jianing Qiu, Kyle Lam, Guohao Li, Amish Acharya, Tien Yin Wong, Ara Darzi, Wu Yuan, Eric J. Topol
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Modulating emotional states of rats through a rat-like robot with learned interaction patterns Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-05 Guanglu Jia, Zhe Chen, Yulai Zhang, Zhenshan Bing, Zhenzhen Quan, Xuechao Chen, Alois Knoll, Qiang Huang, Qing Shi
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Nanobody–antigen interaction prediction with ensemble deep learning and prompt-based protein language models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-05 Juntao Deng, Miao Gu, Pengyan Zhang, Mingyu Dong, Tao Liu, Yabin Zhang, Min Liu
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Deep learning at the forefront of detecting tipping points Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-04 Smita Deb, Partha Sharathi Dutta
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AI in biomaterials discovery: generating self-assembling peptides with resource-efficient deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-12-02 Tianang Leng, Cesar de la Fuente-Nunez
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A plea for caution and guidance about using AI in genomics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-29 Mohammad Hosseini, Christopher R. Donohue
The incorporation of artificial intelligence (AI) into genetics and genomics research can enable research that would have been otherwise impossible. However, these benefits must be considered together with the potential risks to humans, other sentient beings, and the environment. Genetic and genomic advances require much trial and error to succeed; this is ethically fraught when the consequences are
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Deep learning for predicting rate-induced tipping Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-28 Yu Huang, Sebastian Bathiany, Peter Ashwin, Niklas Boers
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Self-decoupling three-axis forces in a simple sensor Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-27 Kuanming Yao, Qiuna Zhuang
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Multimodal language and graph learning of adsorption configuration in catalysis Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-27 Janghoon Ock, Srivathsan Badrinarayanan, Rishikesh Magar, Akshay Antony, Amir Barati Farimani
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Contextual feature extraction hierarchies converge in large language models and the brain Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-26 Gavin Mischler, Yinghao Aaron Li, Stephan Bickel, Ashesh D. Mehta, Nima Mesgarani
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Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-26 Artem A. Trotsyuk, Quinn Waeiss, Raina Talwar Bhatia, Brandon J. Aponte, Isabella M. L. Heffernan, Devika Madgavkar, Ryan Marshall Felder, Lisa Soleymani Lehmann, Megan J. Palmer, Hank Greely, Russell Wald, Lea Goetz, Markus Trengove, Robert Vandersluis, Herbert Lin, Mildred K. Cho, Russ B. Altman, Drew Endy, David A. Relman, Margaret Levi, Debra Satz, David Magnus
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AI pioneers win 2024 Nobel prizes Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-22
The 2024 Nobel prizes in physics and chemistry highlight the interdisciplinary nature and impact of AI in science.
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Machine learning for practical quantum error mitigation Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-22 Haoran Liao, Derek S. Wang, Iskandar Sitdikov, Ciro Salcedo, Alireza Seif, Zlatko K. Minev
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Efficient rare event sampling with unsupervised normalizing flows Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-19 Solomon Asghar, Qing-Xiang Pei, Giorgio Volpe, Ran Ni
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Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-19 Marko Njirjak, Lucija Žužić, Marko Babić, Patrizia Janković, Erik Otović, Daniela Kalafatovic, Goran Mauša
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A soft skin with self-decoupled three-axis force-sensing taxels Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-19 Youcan Yan, Ahmed Zermane, Jia Pan, Abderrahmane Kheddar
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Clinical large language models with misplaced focus Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-11-18 Zining Luo, Haowei Ma, Zhiwu Li, Yuquan Chen, Yixin Sun, Aimin Hu, Jiang Yu, Yang Qiao, Junxian Gu, Hongying Li, Xuxi Peng, Dunrui Wang, Ying Liu, Zhenglong Liu, Jiebin Xie, Zhen Jiang, Gang Tian
On 12 September 2024, OpenAI released two new large language models (LLMs) — o1-preview and o1-mini — marking an important shift in the competitive landscape of commercial LLMs, particularly concerning their reasoning capabilities. Since the introduction of GPT-3.5, OpenAI has launched 31 LLMs in two years. Researchers are rapidly applying these evolving commercial models in clinical medicine, achieving