Reviews & Analysis

Drug resistance in tropical diseases such as malaria requires constant improvement and development of new drugs. To find potential candidates, generative machine learning methods that can search for novel bioactive molecules can be employed.

Machine learning applications in agriculture can bring many benefits in crop management and productivity. However, to avoid harmful effects of a new round of technological modernization, fuelled by AI, a thorough risk assessment is required, to review and mitigate risks such as unintended socio-ecological consequences and security concerns associated with applying machine learning models at scale.

Machine learning performs well at predictive modelling based on statistical correlations, but for high-stakes applications, more robust, explainable and fair approaches are required. Cui and Athey discuss the benefits of bringing causal inference into machine learning, presenting a stable learning approach.

Although artificial reinforcement learning agents do well when rules are rigid, such as games, they fare poorly in real-world scenarios where small changes in the environment or the required actions can impair performance. The authors provide an overview of the cognitive foundations of hierarchical problem-solving, and propose steps to integrate biologically inspired hierarchical mechanisms to enable problem-solving skills in artificial agents.

Digitally recreating the likeness of a person used to be a costly and complex process. Through the use of generative models, AI-generated characters can now be made with relative ease. Pataranutaporn et al. discuss in this Perspective how this technology can be used for positive applications in education and well-being.

Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

Newly sequenced organisms present a challenge for protein function prediction, as they lack experimental characterisation. A network-propagation approach that integrates functional network relationships with protein annotations, transferred from well-studied organisms, produces a more complete picture of the possible protein functions.

Substantial advances have been made in the past decade in developing high-performance machine learning models for medical applications, but translating them into practical clinical decision-making processes remains challenging. This Perspective provides insights into a range of challenges specific to high-dimensional, multimodal medical imaging.

The development of extra fingers and arms is an exciting research area in robotics, human–machine interaction and wearable electronics. It is unclear, however, whether humans can adapt and learn to control extra limbs and integrate them into a new sensorimotor representation, without sacrificing their natural abilities. The authors review this topic and describe challenges in allocating neural resources for robotic body augmentation.

AI has impacted many fields, but hearing has lagged behind. This Perspective calls for a collaboration between the AI and hearing communities, with the goal of transforming hearing healthcare and research.

The radiomics features of disease lesions can be learned from medical imaging data, but is it possible to identify interpretable biomarkers that can help make clinical predictions across heterogeneous diseases and data from different modalities?

Functional subsystems of the macroscale human brain connectome are mapped onto a recurrent neural network and found to perform optimally in a critical regime at the edge of chaos.

Neuromorphic chips that use spikes to encode information could provide fast and energy-efficient computing for ubiquitous embedded systems. A bio-plausible spike-timing solution for training spiking neural networks that makes the most of sparsity is implemented on the BrainScaleS-2 hardware platform.

When the training data for machine learning are highly personal or sensitive, collaborative approaches can help a collective of stakeholders to train a model together without having to share any data. But there are still risks to the privacy of the data. This Perspective provides an overview of potential attacks on collaborative machine learning and how these threats could be addressed.

The ethical use of publicly available datasets with human data for which consent has not been explicitly given needs urgent attention from researchers, funders, research institutes and publishers. A specific challenging case is research involving hacked data and this Perspective discusses whether and under what conditions it is morally and ethically justified to conduct such research.

Selecting interesting proton–proton collisions from the millions taking place each second in the Large Hadron Collider is a challenging task. A neural network optimized for a field-programmable gate array hardware enables 60 ns inference and reduces power consumption by a factor of 50.

In the past few years, AI approaches have been used to enhance Earth and climate modelling. This Perspective examines the opportunity to go further, and build from scratch hybrid systems that integrate AI tools and models based on physical process knowledge to make more efficient use of daily observational data streams.

Finding the optimum design of a complex auction is a challenging and important economic problem. Multi-agent deep learning can help find equilibria by making use of inherent symmetries in bidding strategies.

Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

As highly automated systems become pervasive in society, enforceable governance principles are needed to ensure safe deployment. This Perspective proposes a pragmatic approach where independent audit of AI systems is central. The framework would embody three AAA governance principles: prospective risk Assessments, operation Audit trails and system Adherence to jurisdictional requirements.