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A research paper makes the most impact when its methods, data and code are available for others to use and build on. We highlight the benefits of good sharing practices with a new type of article, reusability reports.
Bertens and Lee propose an evolvable neural unit, a recurrent neural network-based module that can evolve individual somatic and synaptic compartment models of neurons. By constructing networks of these evolvable neural units, they can evolve agents that learn synaptic update rules and the spiking dynamics of neurons.
The popularity of deep learning is leading to new areas in biomedical applications. Wang and colleagues summarize in this Review the recent development and future directions of deep neural networks for superior image quality in the tomographic imaging field.
A set of predictive models can exist that predict equally well; however, the specific variables underlying these models may be important to some of them but not to others. Jiayun Dong and Cynthia Rudin demonstrate a method to visualize and quantify this effect of variable importance.
There is much interest in ‘explainable’ AI, but most efforts concern post hoc methods. Instead, a neural network can be made inherently interpretable, with an approach that involves making human-understandable concepts (aeroplane, bed, lamp and so on) align along the axes of its latent space.
The wealth of data generated by single-cell RNA sequencing can be used to identify gene sets across cells, as well as to identify specific cells. Lukassen and colleagues propose a method combining matrix factorization and variational auto encoders that can capture both cross-cell and cell-specific information.
Inspired by many examples in nature where organisms change shape to concur environments, there is much interest in designing robots that are capable of shape change. Shah et al. demonstrate a method for automatically discovering shape and gait changes for soft robots that can adapt to different terrains.
DNN classifiers are vulnerable to small, specific perturbations in an input that seem benign to humans. To understand this phenomenon, Buckner argues that it may be necessary to treat the patterns that DNNs detect in these adversarial examples as artefacts, which may contain predictive information.
Autonomous drones can help find injured or missing people when a large or hard to traverse area has to be searched, but their view can be obscured in dense forests. David Schedl and colleagues have developed a method to reveal humans in thermal imaging recordings, even in the presence of dense foliage.
Neoantigens play a critical role in cancer immunotherapy. Tran et al. show how training a personalized deep learning model for each individual patient can improve the accuracy and identification rate of mutated neoantigens.
A hallmark of intelligent behaviour is the ability to learn abstract strategies that can be transferred across different tasks, but it has been challenging to incorporate this ability in artificial systems. The authors present a modular architecture for the learning of algorithmic solutions, and demonstrate generalization and scaling on 11 diverse algorithms.
Artificial intelligence can be defined as intelligence demonstrated by machines. But what counts as intelligence, and how intelligence is implemented in different kinds of machines, robots and software varies across disciplines and over time.
Microrobots can interact intelligently with their environment and complete specific tasks by well-designed incorporation of responsive materials. Recent work demonstrates how swarms of microbots with specifically tuned surface chemistry can remove a hormone pollutant from a solution by coalescing it into a web.
Synthesizing robots via physical artificial intelligence is a multidisciplinary challenge for future robotics research. An education methodology is needed for researchers to develop a combination of skills in physical artificial intelligence.