Volume 5 Issue 11, November 2023

The rise of artificial intelligence (AI) has relied on an increasing demand for energy, which threatens to outweigh its promised positive effects. To steer AI onto a more sustainable path, quantifying and comparing its energy consumption is key.

New algorithms allow researchers to decode words the brain is hearing with a non-invasive method, outside the scalp.

Advances in machine intelligence often depend on data assimilation, but data generation has been neglected. The authors discuss mechanisms that might achieve continuous novel data generation and the creation of intelligent systems that are capable of human-like innovation, focusing on social aspects of intelligence.

Traditionally, 3D graphics involves numerical methods for physical and virtual simulations of real-world scenes. Spielberg et al. review how deep learning enables differentiable visual computing, which determines how graphics outputs change when the environment changes, with applications in areas such as computer-aided design, manufacturing and robotics.

Computational methods for analysing single 2D tissue slices from spatial transcriptomics studies are well established, but their extension to the 3D domain is challenging. Wang et al. develop a deep learning framework that can perform 3D reconstruction of cellular structures in tissues as well as whole organisms.

It is widely known that AI-based recommendation systems on social media and news websites can isolate humans from diverse information, eventually trapping them in so-called information cocoons, where they are exposed to a narrow range of viewpoints. Li et al. introduce an adaptive information dynamics model to uncover the origin of information cocoons in complex human–AI interaction systems, and test their findings on two large real-world datasets.

Halide perovskites are promising materials for light-emitting devices, given their narrowband emission and solution processability. However, detailed information on device degradation during operation is required to improve their stability, and this is challenging to obtain. Ji et al. propose a self-supervised deep learning method to capture multi-dimensional images of such devices in their operating regime faster than allowed by conventional imaging techniques.

Deconvolution of cell types in tissue proteomic data is a challenging computational task for the bioinformatics community. A deep-learning method termed scpDeconv is introduced that makes efficient use of single-cell proteomics data to deconvolve cell types and states from bulk proteomics measurements.

Identifying unknown peptides in tandem mass spectrometry is challenging as fragmentation of precursor peptides can be incomplete. Mao and colleagues present a method based on graph neural networks and a path-searching model to create more stable sequence predictions.

Prime editors are innovative genome-editing tools, but selecting guide RNAs with high efficiency remains challenging and requires costly experimental efforts. Liu and colleagues develop a method to design prime-editing guide RNAs based on transfer learning for in silico prediction of editing efficacy.

Contact prediction between two proteins is still computationally challenging, but is vital for understanding multi-protein complexes. Lin et al. use a geometric deep learning approach to provide accurate predictions of inter-protein residue–residue contacts.

Organisms show complex behaviour resulting from a trade-off between obtaining information (explore) and using current information (exploit). Biswas et al. observe a mode-switching strategy modulated by sensory salience in a diverse range of organisms, including electric fish and humans, and argue that the observed heuristic could inform the design of active-sensing behaviours in robotics.

Deep learning methods in natural language processing generally become more effective with larger datasets and bigger networks. But it is not evident whether the same is true for more specialized domains such as cheminformatics. Frey and colleagues provide empirical explorations of chemistry models and find that neural-scaling laws hold true even for the largest tested models and datasets.

Learning causal relationships between variables in large datasets is an outstanding challenge in various scientific applications. Lagemann et al. introduce a deep neural network approach combining convolutional and graph models intended for causal learning in high-dimensional biomedical problems.

The reconstruction of dynamic, spatial fields from sparse sensor data is an important challenge in various fields of science and technology. Santos et al. introduce the Senseiver, a deep learning framework that reconstructs spatial fields from few observations using attention layers to encode and decode sparse data, enabling efficient inference.

The number of publications in artificial intelligence (AI) has been increasing exponentially and staying on top of progress in the field is a challenging task. Krenn and colleagues model the evolution of the growing AI literature as a semantic network and use it to benchmark several machine learning methods that can predict promising research directions in AI.