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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.
The effects of novel antibodies are hard to predict owing to the complex interactions between antibodies and antigens. Zhang and colleagues use a graph-based method to learn a dynamic representation that allows for predictions of neutralization activity and demonstrate the method by recommending probable antibodies for human immunodeficiency virus, severe acute respiratory syndrome coronavirus 2, influenza and dengue.
To be useful in human life, robots need to learn the social rules of human society. Zhou et al. investigate the social rules that apply in spaces mutually occupied by humans and robots. The authors develop a social locomotion model for a mobile robot and implement it for socially aware navigation.
Image noise is a common problem in light microscopy, and denoising is a key step in microscopic imaging pipelines. Lequyer et al. propose a self-supervised denoising method and apply it to diverse imaging and analysis pipelines.
Single-cell multi-omics technologies have increased dramatically in biomedical research. Lakkis et al. develop a deep learning method to address computational challenges in CITE-seq and single-cell RNA-seq datasets.
Recognition of speech from lip movements is still a challenging problem and much effort is concentrated on the English language. Ma et al. have used auxiliary tasks to train a model such that it works for a range of different languages, including Mandarin, Spanish, Italian, French and Portuguese.
We introduced reusability reports, an article type to highlight code reusability, almost two years ago. On the basis of the results and positive feedback from authors and referees, we remain enthusiastic about the format.
Identifying epidemic hotspots in a timely way with syndromic surveillance can provide highly valuable information for public health policy. A machine learning early indicator model that uses highly granular data from digitalized healthcare-seeking behaviour, including from Google Trends and National Health Service Pathways calls, can identify SARS-CoV-2 risk at small geographic scales. The model can retrospectively identify hotspots in the United Kingdom for various variants in 2020 and 2021 before the wider spread and growth of these variants being confirmed by clinical data.
To deliver value in healthcare, artificial intelligence and machine learning models must be integrated not only into technology platforms but also into local human and organizational ecosystems and workflows. To realize the promised benefits of applying these models at scale, a roadmap of the challenges and potential solutions to sociotechnical transferability is needed.
The haptic interface is an essential part of human–machine interfaces where tactile information is delivered between human and machine. Yao et al. develop a soft, ultrathin, miniaturized and wireless electrotactile system that allows virtual tactile information to be reproduced over the hand.
Predicting patient-specific clinical drug responses from cell-line screens using machine learning is challenging. He and colleagues develop a deep learning method to predict patient-specific clinical responses from cell-line and other disease models for drug discovery and personalized medicine.
Saliency methods are used to localize areas of medical images that influence machine learning model predictions, but their accuracy and reliability require investigation. Saporta and colleagues evaluate seven saliency methods using different model architectures, and find that saliency maps perform worse than a human radiologist benchmark.
Earth system models (ESMs) are powerful tools for simulating climate fields, but weather forecasting and in particular precipitation prediction with ESMs are challenging. A generative adversarial network, constrained by the sum of global precipitation, is developed that substantially improves ESM predictions of spatial patterns and intermittency of daily precipitation.
There is a tendency among AI researchers to use the concepts of democracy and democratization in ways that are only loosely connected to their political and historical meanings. We argue that it is important to take the concept more seriously in AI research by engaging with political philosophy.
Cell type annotation is a core task for single cell RNA-sequencing, but current bioinformatic tools struggle with some of the underlying challenges, including high dimensionality, data sparsity, batch effects and a lack of labels. In a self-supervised approach, a transformer model called scBERT is pretrained on millions of unlabelled public single cell RNA-seq data and then fine-tuned with a small number of labelled samples for cell annotation tasks.
The space of possible proteins is vast, and optimizing proteins for specific target properties computationally is an ongoing challenge, even with large amounts of data. Castro and colleagues combine a transformer-based model with regularized prediction heads to form a smooth and pseudoconvex latent space that allows for easier navigation and more efficient optimization of proteins.
The public release of ‘Stable Diffusion’, a high-quality image generation tool, sets new standards in open-source AI development and raises new questions.