Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
A bibliometric analysis of the past and present of AI research suggests a consolidation of research influence. This may present challenges for the exchange of ideas between AI and the social sciences.
Preprints provide an efficient way for scientific communities to share and discuss results. We encourage authors to post preprints on arXiv, bioRxiv or other recognized community preprint platforms.
Artificial intelligence (AI) promises to be an invaluable tool for nature conservation, but its misuse could have severe real-world consequences for people and wildlife. Conservation scientists discuss how improved metrics and ethical oversight can mitigate these risks.
A survey of 300 fictional and non-fictional works featuring artificial intelligence reveals that imaginings of intelligent machines may be grouped in four categories, each comprising a hope and a parallel fear. These perceptions are decoupled from what is realistically possible with current technology, yet influence scientific goals, public understanding and regulation of AI.
Generative machine learning models are used in synthetic biology to find new structures such as DNA sequences, proteins and other macromolecules with applications in drug discovery, environmental treatment and manufacturing. Gupta and Zou propose and demonstrate in silico a feedback-loop architecture to optimize the output of a generative adversarial network that generates synthetic genes to produce ones specifically coding for antimicrobial peptides.
The annotation of medical imaging data requires biological expertise. A human–machine interface connects a deep learning image segmentation system with image viewing software to annotate images.
A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images. This dense motion model forms the input to a supervised system called 4Dsurvival that can efficiently predict human survival.
To attract and retain talent from all backgrounds, new educational models and mentorship programmes are needed in machine intelligence, says Shannon Wongvibulsin.
Neuromorphic processors promise to be a low-powered platform for deep learning, but require neural networks that are adapted for binary communication. The Whetstone method achieves this by gradually sharpening activation functions during the training process.
Arguably one of the most promising as well as critical applications of deep learning is in supporting medical sciences and decision making. It is time to develop methods for systematically quantifying uncertainty underlying deep learning processes, which would lead to increased confidence in practical applicability of these approaches.
Yuanfang Guan explains how taking part in data challenges has helped her learn new analytical techniques and creatively apply them on a variety of datasets.
As artificial intelligence, robotics and machine learning are high on the agenda everywhere, Nature Machine Intelligence launches to stimulate collaborations between different disciplines.
Debate about the impacts of AI is often split into two camps, one associated with the near term and the other with the long term. This divide is a mistake — the connections between the two perspectives deserve more attention, say Stephen Cave and Seán S. ÓhÉigeartaigh.
Ken Goldberg reflects on how four exciting sub-fields of robotics — co-robotics, human–robot interaction, deep learning and cloud robotics — accelerate a renewed trend toward robots working safely and constructively with humans.
