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A question of trust for AI research in medicine Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-24
Medical research is one of the most impactful areas for machine learning applications, but access to large and diverse health datasets is needed for models to be useful. Winning trust from patients by demonstrating that data are handled securely and effectively is key.
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DNA language model GROVER learns sequence context in the human genome Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-23 Melissa Sanabria, Jonas Hirsch, Pierre M. Joubert, Anna R. Poetsch
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Partial-convolution-implemented generative adversarial network for global oceanic data assimilation Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-22 Yoo-Geun Ham, Yong-Sik Joo, Jeong-Hwan Kim, Jeong-Gil Lee
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Automated construction of cognitive maps with visual predictive coding Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-18 James Gornet, Matt Thomson
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A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-18 Rui Jiang, Xiaoxu Yin, Pengshuai Yang, Lingchao Cheng, Juan Hu, Jiao Yang, Ying Wang, Xiaodan Fu, Li Shang, Liling Li, Wei Lin, Huan Zhou, Fufeng Chen, Xuegong Zhang, Zhongliang Hu, Hairong Lv
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The need for reproducible research in soft robotics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-17 Robert Baines, Dylan Shah, Jeremy Marvel, Jennifer Case, Andrew Spielberg
Recent years have witnessed the rise of commercialization efforts for soft robotics technology, which includes soft grippers, stretchable sensors and platforms for human–robot interactions. However, this commercialization lags behind the trends seen with other robotics technologies at equivalent points in their respective lifecycles. For example, the first patent for an industrial robotic manipulator
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Realistic morphology-preserving generative modelling of the brain Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-15 Petru-Daniel Tudosiu, Walter H. L. Pinaya, Pedro Ferreira Da Costa, Jessica Dafflon, Ashay Patel, Pedro Borges, Virginia Fernandez, Mark S. Graham, Robert J. Gray, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
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High-resolution real-space reconstruction of cryo-EM structures using a neural field network Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-12 Yue Huang, Chengguang Zhu, Xiaokang Yang, Manhua Liu
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Unsupervised learning of topological non-Abelian braiding in non-Hermitian bands Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-12 Yang Long, Haoran Xue, Baile Zhang
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Shielding sensitive medical imaging data Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-11 Gaoyang Liu, Chen Wang, Tian Xia
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An interpretable deep learning framework for genome-informed precision oncology Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-11 Shuangxia Ren, Gregory F. Cooper, Lujia Chen, Xinghua Lu
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Lifelike agility and play in quadrupedal robots using reinforcement learning and generative pre-trained models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-05 Lei Han, Qingxu Zhu, Jiapeng Sheng, Chong Zhang, Tingguang Li, Yizheng Zhang, He Zhang, Yuzhen Liu, Cheng Zhou, Rui Zhao, Jie Li, Yufeng Zhang, Rui Wang, Wanchao Chi, Xiong Li, Yonghui Zhu, Lingzhu Xiang, Xiao Teng, Zhengyou Zhang
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Molecular set representation learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-05 Maria Boulougouri, Pierre Vandergheynst, Daniel Probst
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Will generative AI transform robotics? Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27
In the current wave of excitement about applying large vision–language models and generative AI to robotics, expectations are running high, but conquering real-world complexities remains challenging for robots.
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Machine learning for micro- and nanorobots Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Lidong Yang, Jialin Jiang, Fengtong Ji, Yangmin Li, Kai-Leung Yung, Antoine Ferreira, Li Zhang
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Neuromorphic visual scene understanding with resonator networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A. Olshausen, Yulia Sandamirskaya, Friedrich T. Sommer, E. Paxon Frady
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Visual odometry with neuromorphic resonator networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, E. Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya
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Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Osama Abdin, Philip M. Kim
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Laplace neural operator for solving differential equations Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Qianying Cao, Somdatta Goswami, George Em Karniadakis
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Challenges, evaluation and opportunities for open-world learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Mayank Kejriwal, Eric Kildebeck, Robert Steininger, Abhinav Shrivastava
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Coordinate-based neural representations for computational adaptive optics in widefield microscopy Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji
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Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Evan E. Seitz, David M. McCandlish, Justin B. Kinney, Peter K. Koo
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Systematic analysis of 32,111 AI model cards characterizes documentation practice in AI Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Weixin Liang, Nazneen Rajani, Xinyu Yang, Ezinwanne Ozoani, Eric Wu, Yiqun Chen, Daniel Scott Smith, James Zou
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Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Dong Chen, Jian Liu, Guo-Wei Wei
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Reconciling privacy and accuracy in AI for medical imaging Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
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Machine learning-aided generative molecular design Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-18 Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell
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Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-17 Huan Yee Koh, Anh T. N. Nguyen, Shirui Pan, Lauren T. May, Geoffrey I. Webb
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Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-14 Chenguang Li, Gabriel Kreiman, Sharad Ramanathan
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Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-06 Duanhua Cao, Geng Chen, Jiaxin Jiang, Jie Yu, Runze Zhang, Mingan Chen, Wei Zhang, Lifan Chen, Feisheng Zhong, Yingying Zhang, Chenghao Lu, Xutong Li, Xiaomin Luo, Sulin Zhang, Mingyue Zheng
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Learning efficient backprojections across cortical hierarchies in real time Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-06 Kevin Max, Laura Kriener, Garibaldi Pineda García, Thomas Nowotny, Ismael Jaras, Walter Senn, Mihai A. Petrovici
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Distributed constrained combinatorial optimization leveraging hypergraph neural networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-30 Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar
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Empathic AI can’t get under the skin Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-24
Personalized LLMs built with the capacity for emulating empathy are right around the corner. The effects on individual users needs careful consideration.
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Accurate and robust protein sequence design with CarbonDesign Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-23 Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang
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Quantum circuit synthesis with diffusion models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-20 Florian Fürrutter, Gorka Muñoz-Gil, Hans J. Briegel
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Back to basics to open the black box Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-17 Diego Marcondes, Adilson Simonis, Junior Barrera
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Efficient learning of accurate surrogates for simulations of complex systems Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-17 A. Diaw, M. McKerns, I. Sagert, L. G. Stanton, M. S. Murillo
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Does it matter if empathic AI has no empathy? Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-15 Garriy Shteynberg, Jodi Halpern, Amir Sadovnik, Jon Garthoff, Anat Perry, Jessica Hay, Carlos Montemayor, Michael A. Olson, Tim L. Hulsey, Abrol Fairweather
Imagine a machine that provides a simulation of any experience a person might want, but once the machine is activated, the person is unable to tell that the experience isn’t real. When Robert Nozick formulated this thought experiment in 1974 (ref. 1), it was meant to be obvious that people in otherwise ordinary circumstances would be making a horrible mistake if they hooked themselves up to such a
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Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-13 Ning Wang, Jiang Bian, Yuchen Li, Xuhong Li, Shahid Mumtaz, Linghe Kong, Haoyi Xiong
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Diving into deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-10 Ge Wang
Understanding Deep Learning Simon J. D. PrinceThe MIT Press: 2023. 544 pp. $90.00 The field of artificial intelligence (AI) has experienced a surge in developments over the past years, propelled by breakthroughs in deep learning with neural networks. This has revolutionized many aspects of society. However, the speed at which AI is advancing highlights the need for textbooks that provide essential
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Augmenting large language models with chemistry tools Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-08 Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, Philippe Schwaller
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Predicting equilibrium distributions for molecular systems with deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-08 Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu
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Maximum diffusion reinforcement learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-02 Thomas A. Berrueta, Allison Pinosky, Todd D. Murphey
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The rewards of reusable machine learning code Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-24
Research papers can make a long-lasting impact when the code and software tools supporting the findings are made readily available and can be reused and built on. Our reusability reports explore and highlight examples of good code sharing practices.
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The benefits, risks and bounds of personalizing the alignment of large language models to individuals Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-23 Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, Scott A. Hale
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Dangers of speech technology for workplace diversity Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-22 Mike Horia Mihail Teodorescu, Mingang K. Geiger, Lily Morse
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Artificial intelligence tackles the nature–nurture debate Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-19 Justin N. Wood
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The synergy complement control approach for seamless limb-driven prostheses Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-19 Johannes Kühn, Tingli Hu, Alexander Tödtheide, Edmundo Pozo Fortunić, Elisabeth Jensen, Sami Haddadin
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Synthetic Lagrangian turbulence by generative diffusion models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-17 T. Li, L. Biferale, F. Bonaccorso, M. A. Scarpolini, M. Buzzicotti
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Equivariant 3D-conditional diffusion model for molecular linker design Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-11 Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, Bruno Correia
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A neural speech decoding framework leveraging deep learning and speech synthesis Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-08 Xupeng Chen, Ran Wang, Amirhossein Khalilian-Gourtani, Leyao Yu, Patricia Dugan, Daniel Friedman, Werner Doyle, Orrin Devinsky, Yao Wang, Adeen Flinker
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Tandem mass spectrum prediction for small molecules using graph transformers Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-05 Adamo Young, Hannes Röst, Bo Wang
Tandem mass spectra capture fragmentation patterns that provide key structural information about molecules. Although mass spectrometry is applied in many areas, the vast majority of small molecules lack experimental reference spectra. For over 70 years, spectrum prediction has remained a key challenge in the field. Existing deep learning methods do not leverage global structure in the molecule, potentially
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A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-05 Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang
The 5′ untranslated region (UTR), a regulatory region at the beginning of a messenger RNA (mRNA) molecule, plays a crucial role in regulating the translation process and affects the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduce a language model for 5′ UTR, which we refer to as the UTR-LM. The
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Geometry-enhanced pretraining on interatomic potentials Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-05 Taoyong Cui, Chenyu Tang, Mao Su, Shufei Zhang, Yuqiang Li, Lei Bai, Yuhan Dong, Xingao Gong, Wanli Ouyang
Machine learning interatomic potentials (MLIPs) describe the interactions between atoms in materials and molecules by learning them from a reference database generated by ab initio calculations. MLIPs can accurately and efficiently predict such interactions and have been applied to various fields of physical science. However, high-performance MLIPs rely on a large amount of labelled data, which are
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The curious case of the test set AUROC Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-04 Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb
The area under the receiver operating characteristic curve (AUROC) of the test set is used throughout machine learning (ML) for assessing a model’s performance. However, when concordance is not the only ambition, this gives only a partial insight into performance, masking distribution shifts of model outputs and model instability.
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Reusability report: Uncovering associations in biomedical bipartite networks via a bilinear attention network with domain adaptation Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-04 Tao Xu, Haoyuan Shi, Wanling Gao, Xiaosong Wang, Zhenyu Yue
Conditional domain adversarial learning presents a promising approach for enhancing the generalizability of deep learning-based methods. Inspired by the efficacy of conditional domain adversarial networks, Bai and colleagues introduced DrugBAN, a methodology designed to explicitly learn pairwise local interactions between drugs and targets. DrugBAN leverages drug molecular graphs and target protein
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Invalid SMILES are beneficial rather than detrimental to chemical language models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-29 Michael A. Skinnider
Generative machine learning models have attracted intense interest for their ability to sample novel molecules with desired chemical or biological properties. Among these, language models trained on SMILES (Simplified Molecular-Input Line-Entry System) representations have been subject to the most extensive experimental validation and have been widely adopted. However, these models have what is perceived
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The new NeuroAI Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-22
After several decades of developments in AI, has the inspiration that can be drawn from neuroscience been exhausted? Recent initiatives make the case for taking a fresh look at the intersection between the two fields.
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Generative AI for designing and validating easily synthesizable and structurally novel antibiotics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-22 Kyle Swanson, Gary Liu, Denise B. Catacutan, Autumn Arnold, James Zou, Jonathan M. Stokes
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A collective AI via lifelong learning and sharing at the edge Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-22 Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa Parekh, Kaushik Roy, Shahaf Shperberg, Hava T. Siegelmann, Peter Stone
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Challenges and opportunities in translating ethical AI principles into practice for children Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-20 Ge Wang, Jun Zhao, Max Van Kleek, Nigel Shadbolt
AI systems are becoming increasingly pervasive within children’s devices, apps and services. The concern over a world where AI systems are deployed unchecked has raised burning questions about the impact, governance and accountability of these technologies. Although recent effort on AI ethics has converged into growing consensus on a set of high-level ethical AI principles, engagement with children’s