Nature Machine Intelligence
Nature Machine Intelligence will publish high-quality original research and reviews in a wide range of topics in machine learning, robotics and AI. The journal will also explore and discuss the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where machine intelligence can augment human capabilities and knowledge in fields such as scientific discovery, healthcare, medical diagnostics and safe and sustainable cities, transport and agriculture. At the same time, many important questions on ethical, social and legal issues arise, especially given the fast pace of developments Nature Machine Intelligence will provide a platform to discuss these wide implications — encouraging a cross-disciplinary dialogue — with Comments, News Features, News & Views articles and also Correspondence.
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© 2024 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Nature Machine Intelligence
© 2024 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
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Nature Machine Intelligence
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https://www.nature.com/articles/s42256-024-00826-6
Nature Machine Intelligence, Published online: 22 March 2024; doi:10.1038/s42256-024-00826-6After 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.]]>
doi:10.1038/s42256-024-00826-6
Nature Machine Intelligence, Published online: 2024-03-22; | doi:10.1038/s42256-024-00826-6
2024-03-22
Nature Machine Intelligence
10.1038/s42256-024-00826-6
https://www.nature.com/articles/s42256-024-00826-6
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https://www.nature.com/articles/s42256-024-00809-7
Nature Machine Intelligence, Published online: 22 March 2024; doi:10.1038/s42256-024-00809-7AI methods can discover new antibiotics but existing methods have limitations. Swanson et al. develop a generative AI model that learns to design molecules that are easy to synthesize. The authors apply the model to design and validate novel antibiotics against the bacterial pathogen Acinetobacter baumannii.]]>
Kyle SwansonGary LiuDenise B. CatacutanAutumn ArnoldJames ZouJonathan M. Stokes
doi:10.1038/s42256-024-00809-7
Nature Machine Intelligence, Published online: 2024-03-22; | doi:10.1038/s42256-024-00809-7
2024-03-22
Nature Machine Intelligence
10.1038/s42256-024-00809-7
https://www.nature.com/articles/s42256-024-00809-7
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https://www.nature.com/articles/s42256-024-00800-2
Nature Machine Intelligence, Published online: 22 March 2024; doi:10.1038/s42256-024-00800-2An emerging research area in AI is developing multi-agent capabilities with collections of interacting AI systems. Andrea Soltoggio and colleagues develop a vision for combining such approaches with current edge computing technology and lifelong learning advances. The envisioned network of AI agents could quickly learn new tasks in open-ended applications, with individual AI agents independently learning and contributing to and benefiting from collective knowledge.]]>
Andrea SoltoggioEseoghene Ben-IwhiwhuVladimir BravermanEric EatonBenjamin EpsteinYunhao GeLucy HalperinJonathan HowLaurent IttiMichael A. JacobsPavan KantharajuLong LeSteven LeeXinran LiuSildomar T. MonteiroDavid MuslinerSaptarshi NathPriyadarshini PandaChristos PeridisHamed PirsiavashVishwa ParekhKaushik RoyShahaf ShperbergHava T. SiegelmannPeter StoneKyle VedderJingfeng WuLin YangGuangyao ZhengSoheil Kolouri
doi:10.1038/s42256-024-00800-2
Nature Machine Intelligence, Published online: 2024-03-22; | doi:10.1038/s42256-024-00800-2
2024-03-22
Nature Machine Intelligence
10.1038/s42256-024-00800-2
https://www.nature.com/articles/s42256-024-00800-2
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https://www.nature.com/articles/s42256-024-00805-x
Nature Machine Intelligence, Published online: 20 March 2024; doi:10.1038/s42256-024-00805-xAs the impacts of AI on everyday life increase, guidelines are needed to ensure ethical deployment and use of this technology. This is even more pressing for technology that interacts with groups that need special protection, such as children. In this Perspective Wang et al. survey the existing AI ethics guidelines with a focus on children’s issues, and provide suggestions for further development.]]>
Ge WangJun ZhaoMax Van KleekNigel Shadbolt
doi:10.1038/s42256-024-00805-x
Nature Machine Intelligence, Published online: 2024-03-20; | doi:10.1038/s42256-024-00805-x
2024-03-20
Nature Machine Intelligence
10.1038/s42256-024-00805-x
https://www.nature.com/articles/s42256-024-00805-x
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https://www.nature.com/articles/s42256-024-00813-x
Nature Machine Intelligence, Published online: 18 March 2024; doi:10.1038/s42256-024-00813-xAlthough 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.]]>
Marieke BakVince I. MadaiLeo Anthony CeliGeorgios A. KaissisRonald CornetMenno MarisDaniel RueckertAlena BuyxStuart McLennan
doi:10.1038/s42256-024-00813-x
Nature Machine Intelligence, Published online: 2024-03-18; | doi:10.1038/s42256-024-00813-x
2024-03-18
Nature Machine Intelligence
10.1038/s42256-024-00813-x
https://www.nature.com/articles/s42256-024-00813-x
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https://www.nature.com/articles/s42256-024-00807-9
Nature Machine Intelligence, Published online: 15 March 2024; doi:10.1038/s42256-024-00807-9Foundation models have transformed artificial intelligence by training on vast amounts of broad unlabelled data. Pai et al. present a foundation model leading to more accurate, efficient and robust cancer imaging biomarkers, especially in use cases with small training datasets.]]>
Suraj PaiDennis BontempiIbrahim HadzicVasco PrudenteMateo SokačTafadzwa L. ChaunzwaSimon BernatzAhmed HosnyRaymond H. MakNicolai J. BirkbakHugo J. W. L. Aerts
doi:10.1038/s42256-024-00807-9
Nature Machine Intelligence, Published online: 2024-03-15; | doi:10.1038/s42256-024-00807-9
2024-03-15
Nature Machine Intelligence
10.1038/s42256-024-00807-9
https://www.nature.com/articles/s42256-024-00807-9
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https://www.nature.com/articles/s42256-024-00812-y
Nature Machine Intelligence, Published online: 13 March 2024; doi:10.1038/s42256-024-00812-yAI tools such as ChatGPT can provide responses to queries on any topic, but can such large language models accurately ‘write’ molecules as output to our specification? Results now show that models trained on general text can be tweaked with small amounts of chemical data to predict molecular properties, or to design molecules based on a target feature.]]>
Glen M. Hocky
doi:10.1038/s42256-024-00812-y
Nature Machine Intelligence, Published online: 2024-03-13; | doi:10.1038/s42256-024-00812-y
2024-03-13
Nature Machine Intelligence
10.1038/s42256-024-00812-y
https://www.nature.com/articles/s42256-024-00812-y
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https://www.nature.com/articles/s42256-024-00819-5
Nature Machine Intelligence, Published online: 11 March 2024; doi:10.1038/s42256-024-00819-5Author Correction: A challenge for rounded evaluation of recommender systems]]>
Jacopo TagliabueFederico BianchiTobias SchnabelGiuseppe AttanasioCiro GrecoGabriel de Souza MoreiraPatrick John Chia
doi:10.1038/s42256-024-00819-5
Nature Machine Intelligence, Published online: 2024-03-11; | doi:10.1038/s42256-024-00819-5
2024-03-11
Nature Machine Intelligence
10.1038/s42256-024-00819-5
https://www.nature.com/articles/s42256-024-00819-5