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