Reviews & Analysis

The metaverse is gaining prominence in industry, academia and social media. Wang and colleagues envision a medical technology and AI ecosystem, and present this perspective on the future of healthcare in the metaverse.

Tool use is one of the defining traits of human cognition that sets our species apart from other animals. A novel computational framework may enable robots to use tools as intelligently as humans do.

In animals, both body and neural control have co-evolved to be adaptable to the environment. While a newborn foal learns quickly how to use its legs, traditional robotic approaches require careful engineering and calibration for stable walking robots. Bio-inspired robotics aims to bridge this gap.

It has become rapidly clear in the past few years that the creation, use and maintenance of high-quality annotated datasets for robust and reliable AI applications requires careful attention. This Perspective discusses challenges, considerations and best practices for various stages in the data-to-AI pipeline, to encourage a more data-centric approach.

Designing viable molecular candidates is pivotal to devising low-cost and sustainable storage systems. A reinforcement learning framework has been developed that can identify stable candidates for redox flow batteries in the large search space of organic radicals.

Directed, active transport of cargo is essential for life on all length scales. A new system of artificial microtubules — consisting of a fibre with an embedded periodic array of magnetic inclusions — provides controlled active transport of microcargo by a rotating magnetic field, even under adverse flow conditions.

Deep learning models for sequential data can be trained to make accurate predictions from large biological datasets. New tools from computer vision and natural language processing can help us make these models interpretable to biologists.

Microscopy-based drug screens with fluorescent markers can shed light on how drugs affect biological processes. Without adding markers and imaging channels, which is cumbersome and costly, a new generative deep-learning method extracts new fluorescence channels from images, potentially improving the drug-discovery pipeline.

Neural networks can be implemented by using purified DNA molecules that interact in a test tube. Convolutional neural networks to classify high-dimensional data have now been realized in vitro, in one of the most complex demonstrations of molecular programming so far.

Unsupervised machine learning algorithms reduce the dependence on curated, labeled datasets that are characteristic of supervised machine learning. The authors argue that the developmental science of infant cognition could inform the design of unsupervised machine learning approaches.

Both proteins and natural language are essentially based on a sequential code, but feature complex interactions at multiple scales, which can be useful when transferring machine learning models from one domain to another. In this Review, Ferruz and Höcker summarize recent advances in language models, such as transformers, and their application to protein design.

Despite the growing number of initiatives that employ AI to counter corruption, few studies empirically tackle the political and social consequences of embedding AI in anti-corruption efforts. The authors outline the societal and technical challenges that need to be overcome for AI to fight corruption.

Machine reading and knowledge extraction methods can be used to mine the scientific literature and reveal the direction and robustness of discoveries. Such efforts now point to the importance of independent tests of reported claims.

Behavioural big data and algorithmic behaviour modification technologies controlled by commercial platforms have become difficult for academic researchers to access. Greene et al. describe barriers to academic research on such data and algorithms, and make a case for enhancing platform access and transparency.

It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.

Graph convolutional networks have become a popular tool for learning with graphs and networks. We reflect on the reasons behind the success story.

Predicting the performance of a tactile sensor from its composition and morphology is nearly impossible with traditional computational approaches. Machine learning can not only predict device-level performance, but also recommend new material compositions for soft machine applications.

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.