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关键词： Technology   Computer science   Sustainability   Machine learning   Immunology  

摘要： Sustainability and artificial intelligence dominate our seventh annual round-up of exciting innovations.

关键词： Machine learning   Computer science   Technology  

摘要： Developers have tricks to stop artificial intelligence from making things up, but large language models are still struggling to tell the truth, the whole truth and nothing but the truth.

关键词： Language   Computer science  

摘要： Machine-learning system can process words spoken in 101 languages, spitting out voice-synthesized translations in 36 target languages.

关键词： Machine learning   Brain   Evolution   Computer science  

关键词： Computer science   Machine learning   Scientific community  

摘要： Experimental model’s record-breaking performance on science and maths tests wows researchers.

关键词： Computer science   Quantum information  

摘要： Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware.

关键词： biological sciences   computer science   natural sciences  

摘要： We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity by contrasting its higher-order structure to a random control and confirmed its relevance in several openly available connectomes. Using a biologically detailed cortical model and an electron microscopic dataset, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neural networks, due to the heterogeneity of their connectivity.

关键词： Applied sciences   Computer science   Signal processing  

摘要： A key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes. Symmetry coding was negligible in the earliest DNN layers. The strongest discriminability occurred in the first fully connected layer, FC6, plausibly analogous to the human lateral occipital complex (LOC), matching many structural properties of human symmetry processing. These results support the homology between the feedforward DNN trained on natural images and the global processing of the extended visual hierarchy as it has evolved in the human brain.