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The benefits, risks and bounds of personalizing the alignment of large language models to individuals Nat. Mach. Intell. (IF 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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 23.8) 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
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Federated learning is not a cure-all for data ethics Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-18 Marieke Bak, Vince I. Madai, Leo Anthony Celi, Georgios A. Kaissis, Ronald Cornet, Menno Maris, Daniel Rueckert, Alena Buyx, Stuart McLennan
Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital health requires acknowledgement of its limitations.
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Foundation model for cancer imaging biomarkers Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-15 Suraj Pai, Dennis Bontempi, Ibrahim Hadzic, Vasco Prudente, Mateo Sokač, Tafadzwa L. Chaunzwa, Simon Bernatz, Ahmed Hosny, Raymond H. Mak, Nicolai J. Birkbak, Hugo J. W. L. Aerts
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Connecting molecular properties with plain language Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-13 Glen M. Hocky
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Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-11 Rodrigo Bonazzola, Enzo Ferrante, Nishant Ravikumar, Yan Xia, Bernard Keavney, Sven Plein, Tanveer Syeda-Mahmood, Alejandro F. Frangi
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PocketFlow is a data-and-knowledge-driven structure-based molecular generative model Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-11 Yuanyuan Jiang, Guo Zhang, Jing You, Hailin Zhang, Rui Yao, Huanzhang Xie, Liyun Zhang, Ziyi Xia, Mengzhe Dai, Yunjie Wu, Linli Li, Shengyong Yang
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The democratization of global AI governance and the role of tech companies Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-08 Eva Erman, Markus Furendal
Can non-state multinational tech companies counteract the potential democratic deficit in the emerging global governance of AI? We argue that although they may strengthen core values of democracy such as accountability and transparency, they currently lack the right kind of authority to democratize global AI governance.
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Learning high-level visual representations from a child’s perspective without strong inductive biases Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-07 A. Emin Orhan, Brenden M. Lake
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Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-03-05 Yingying Cao, Tian-Gen Chang, Sahil Sahni, Eytan Ruppin
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Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-28 Tian Lan, Shuquan Su, Pengyao Ping, Gyorgy Hutvagner, Tao Liu, Yi Pan, Jinyan Li
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AI protein shake-up Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-23
One of the most successful areas for deep learning in scientific discovery has been protein predictions and engineering. We take a closer look at four studies in this issue that advance protein science with innovative deep learning approaches.
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Codon language embeddings provide strong signals for use in protein engineering Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-23 Carlos Outeiral, Charlotte M. Deane
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Neural multi-task learning in drug design Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-20 Stephan Allenspach, Jan A. Hiss, Gisbert Schneider
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Mitigating allocative tradeoffs and harms in an environmental justice data tool Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-16 Benjamin Q. Huynh, Elizabeth T. Chin, Allison Koenecke, Derek Ouyang, Daniel E. Ho, Mathew V. Kiang, David H. Rehkopf
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A causal perspective on dataset bias in machine learning for medical imaging Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-15 Charles Jones, Daniel C. Castro, Fabio De Sousa Ribeiro, Ozan Oktay, Melissa McCradden, Ben Glocker
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Reusability report: Unpaired deep-learning approaches for holographic image reconstruction Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-15 Yuhe Zhang, Tobias Ritschel, Pablo Villanueva-Perez
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Protein function prediction as approximate semantic entailment Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-14 Maxat Kulmanov, Francisco J. Guzmán-Vega, Paula Duek Roggli, Lydie Lane, Stefan T. Arold, Robert Hoehndorf
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A computational framework for neural network-based variational Monte Carlo with Forward Laplacian Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-13 Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, Liwei Wang
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Lessons from a challenge on forecasting epileptic seizures from non-cerebral signals Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-13 Kenny Schlegel, Denis Kleyko, Benjamin H. Brinkmann, Ewan S. Nurse, Ross W. Gayler, Peer Neubert
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Weak signal extraction enabled by deep neural network denoising of diffraction data Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-13 Jens Oppliger, M. Michael Denner, Julia Küspert, Ruggero Frison, Qisi Wang, Alexander Morawietz, Oleh Ivashko, Ann-Christin Dippel, Martin von Zimmermann, Izabela Biało, Leonardo Martinelli, Benoît Fauqué, Jaewon Choi, Mirian Garcia-Fernandez, Ke-Jin Zhou, Niels Bech Christensen, Tohru Kurosawa, Naoki Momono, Migaku Oda, Fabian D. Natterer, Mark H. Fischer, Titus Neupert, Johan Chang
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State-specific protein–ligand complex structure prediction with a multiscale deep generative model Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-12 Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller, Animashree Anandkumar
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Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-07 Harry Coppock, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Kieran Baker, Jobie Budd, Richard Payne, Emma Karoune, David Hurley, Alexander Titcomb, Sabrina Egglestone, Ana Tendero Cañadas, Lorraine Butler, Radka Jersakova, Jonathon Mellor, Selina Patel, Tracey Thornley, Peter Diggle, Sylvia Richardson, Josef Packham, Björn W. Schuller, Davide Pigoli, Steven Gilmour, Stephen Roberts, Chris Holmes
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Leveraging large language models for predictive chemistry Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-06 Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit
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What comparing deep neural networks can teach us about human vision Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-02-06 Katja Seeliger, Martin N. Hebart
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Is it five already? Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-24
We reflect on five years of Nature Machine Intelligence and on providing a venue for discussions in AI.
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Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-24 Milena Pavlović, Ghadi S. Al Hajj, Chakravarthi Kanduri, Johan Pensar, Mollie E. Wood, Ludvig M. Sollid, Victor Greiff, Geir K. Sandve
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Anniversary AI reflections Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-24 Noelia Ferruz, Marinka Zitnik, Pierre-Yves Oudeyer, Emmie Hine, Nandana Sengupta, Yiyu Shi, Diana Mincu, Sebastian Porsdam Mann, Payel Das, Francesco Stella
For our fifth anniversary, we reconnected with authors of recent Comments and Perspectives in Nature Machine Intelligence and asked them how the topic they wrote about developed. We also wanted to know what other topics in AI they found exciting, surprising or worrying, and what their hopes and expectations are for AI in 2024—and the next five years. A recurring theme is the ongoing developments in
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Variational autoencoder for design of synthetic viral vector serotypes Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-23 Suyue Lyu, Shahin Sowlati-Hashjin, Michael Garton
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The dangers of using proprietary LLMs for research Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-17 Étienne Ollion, Rubing Shen, Ana Macanovic, Arnault Chatelain
The release of ChatGPT at the end of 2022 thrust large language models (LLMs) into the limelight. By enabling its users to query the model directly in natural language, ChatGPT democratized access to these models — a welcome development. Since then, ChatGPT and similar tools such as Bard, Claude and Bing AI have shown their versatility and efficiency on a wide variety of tasks.
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Guidelines for study protocols describing predefined validations of prediction models in medical deep learning and beyond Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-15 Andreas Kleppe, Ole-Johan Skrede, Knut Liestøl, David J. Kerr, Håvard E. Danielsen
In a recent issue of Nature Machine Intelligence, Dhiman et al.1 highlight the importance of planning evaluations of deep learning systems in advance by predefining study protocols. We applaud this objective as such planning should prevent the evaluation of multiple systems and presentation of only the best result, which would almost certainly be overoptimistic for the intended application of the system
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Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-15 Aubin Ramon, Montader Ali, Misha Atkinson, Alessio Saturnino, Kieran Didi, Cristina Visentin, Stefano Ricagno, Xing Xu, Matthew Greenig, Pietro Sormanni
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Generation of 3D molecules in pockets via a language model Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-15 Wei Feng, Lvwei Wang, Zaiyun Lin, Yanhao Zhu, Han Wang, Jianqiang Dong, Rong Bai, Huting Wang, Jielong Zhou, Wei Peng, Bo Huang, Wenbiao Zhou
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Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-12 Arvin Tashakori, Zenan Jiang, Amir Servati, Saeid Soltanian, Harishkumar Narayana, Katherine Le, Caroline Nakayama, Chieh-ling Yang, Z. Jane Wang, Janice J. Eng, Peyman Servati
Accurate real-time tracking of dexterous hand movements has numerous applications in human–computer interaction, the metaverse, robotics and tele-health. Capturing realistic hand movements is challenging because of the large number of articulations and degrees of freedom. Here we report accurate and dynamic tracking of articulated hand and finger movements using stretchable, washable smart gloves with
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Autonomous 3D positional control of a magnetic microrobot using reinforcement learning Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-10 Sarmad Ahmad Abbasi, Awais Ahmed, Seungmin Noh, Nader Latifi Gharamaleki, Seonhyoung Kim, A. M. Masum Bulbul Chowdhury, Jin-young Kim, Salvador Pané, Bradley J. Nelson, Hongsoo Choi
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Multi-animal 3D social pose estimation, identification and behaviour embedding with a few-shot learning framework Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-08 Yaning Han, Ke Chen, Yunke Wang, Wenhao Liu, Zhouwei Wang, Xiaojing Wang, Chuanliang Han, Jiahui Liao, Kang Huang, Shengyuan Cai, Yiting Huang, Nan Wang, Jinxiu Li, Yangwangzi Song, Jing Li, Guo-Dong Wang, Liping Wang, Yaping Zhang, Pengfei Wei
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Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization Nat. Mach. Intell. (IF 23.8) Pub Date : 2024-01-08 Simone Ciceri, Lorenzo Cassani, Matteo Osella, Pietro Rotondo, Filippo Valle, Marco Gherardi
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Catching up with missing particles Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-27 Séverine Atis, Lionel Agostini
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A year of racing ahead with AI and not breaking things Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-18
Looking back at a year of escalating, divisive debates in AI safety and who determines the agenda.
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Deconstructing the generalization gap Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-18 Andrey Gromov
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Spatially embedded neuromorphic networks Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-18 Filip Milisav, Bratislav Misic
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A challenge for the law and artificial intelligence Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-18 Thomas Burri
Borrowing the format of public competitions from engineering and computer science, a new type of challenge in 2023 tested real-world AI applications with legal assessments based on the EU AI Act.
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Multi-modal molecule structure–text model for text-based retrieval and editing Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-18 Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Animashree Anandkumar
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A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-18 R. Pacelli, S. Ariosto, M. Pastore, F. Ginelli, M. Gherardi, P. Rotondo
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Artificial intelligence-powered electronic skin Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-12-18 Changhao Xu, Samuel A. Solomon, Wei Gao