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.
《自然机器智能》将发表高质量的原创研究和评论，涉及机器学习、机器人和人工智能等广泛的主题。该杂志还将探讨和讨论这些领域开始对其他科学学科以及社会和工业的许多方面产生的重大影响。在科学发现、医疗保健、医疗诊断、安全和可持续发展的城市、交通和农业等领域，机器智能可以增强人类的能力和知识，这是无数的机会。与此同时，许多重要的伦理、社会和法律的问题也随之出现，特别是在快速发展的情况下，自然机器智能将提供一个平台来讨论这些广泛的影响-鼓励跨学科对话-评论、新闻特写、新闻与观点文章以及通信。
The global landscape of AI ethics guidelines
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0088-2
Long short-term memory networks in memristor crossbar arrays
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-018-0001-4
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0057-9
Reinforcement learning in artificial and biological systems
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0025-4
Principles alone cannot guarantee ethical AI
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0114-4
Learning with Known Operators reduces Maximum Training Error Bounds
来源期刊：Nature machine intelligenceDOI：10.1038/s42256-019-0077-5
Evolving embodied intelligence from materials to machines
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-018-0009-9
Causal deconvolution by algorithmic generative models
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-018-0005-0
Solving the Rubik’s cube with deep reinforcement learning and search
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0070-z
Principles alone cannot guarantee ethical AI
来源期刊：Nature Machine IntelligenceDOI：10.2139/SSRN.3391293
Benchmarks for progress in neuromorphic computing
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0097-1
Robots and the return to collaborative intelligence
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-018-0008-X
Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0113-5
The Animal-AI Olympics
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0050-3
Increasing generality in machine learning through procedural content generation
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-020-0208-z
Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0067-7
When seeing is no longer believing
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0085-5
Autonomous Functional Movements in a Tendon-Driven Limb via Limited Experience
来源期刊：Nature machine intelligenceDOI：10.1038/s42256-019-0029-0
Distributed sensing for fluid disturbance compensation and motion control of intelligent robots
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0044-1
Protein structure prediction beyond AlphaFold
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0086-4
Constructing energy-efficient mixed-precision neural networks through principal component analysis for edge intelligence
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0134-0
Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0119-z
Developing the Knowledge of Number Digits in a child like Robot
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0123-3
Consumer protection requires artificial intelligence
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0042-3
Automated abnormality detection in lower extremity radiographs using deep learning
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0126-0
A role for analogue memory in AI hardware
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-018-0007-Y
Apply rich psychological terms in AI with care
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0039-Y
A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0098-0
A universal information theoretic approach to the identification of stopwords
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0112-6
Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0068-6
Improved fragment sampling for ab initio protein structure prediction using deep neural networks
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0075-7
A portable three-degrees-of-freedom force feedback origami robot for human–robot interactions
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0125-1
Waking up to data challenges
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-018-0011-2
Solidarity should be a core ethical principle of AI
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0115-3
Gazing into Clever Hans machines
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0032-5
Author Correction: Learnability can be undecidable
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0023-6
Picking the right robotics challenge
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0031-6
Computing with a camera
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0124-2
Origami for the everyday
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0129-x
Bringing robustness against adversarial attacks
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0116-2
Taking robots shopping
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0118-0
Author Correction: Reconstructing quantum states with generative models
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0045-0
The Algonauts Project
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0127-z
Code of conduct for using AI in healthcare
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0056-X
A probabilistic challenge for object detection
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0094-4
Robotics on a mission
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0081-9
Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0082-8
Publisher Correction: Democratic classification of free-format survey responses with a network-based framework
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0090-8
A web of tidings
来源期刊：Nature Machine IntelligenceDOI：10.1038/s42256-019-0027-2
Moving beyond reward prediction errors
来源期刊：Nature Machine IntelligenceDOI：10.1038/S42256-019-0053-0
