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AI and machine learning

Computational advances have enabled the deployment of increasingly complex models, which are applied now to a broad-ranging set of fields. This editorial showcase aims at providing a snapshot of the current tools and challenges that are currently holding the promise to change lives in several ways. Herein, we also highlight research on the underlying pursuit of developing the concept of Artificial Intelligence.

Featured articles

Tests for autonomous vehicles are usually made in the naturalistic driving environment where safety-critical scenarios are rare. Feng et al. propose a testing approach combining naturalistic and adversarial environment which allows to accelerate testing process and detect dangerous driving events.

Article | Open Access | | Nature Communications

In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.

Article | Open Access | | Nature Communications

The quality of human language translation has been thought to be unattainable by computer translation systems. Here the authors present CUBBITT, a deep learning system that outperforms professional human translators in retaining text meaning in English-to-Czech news translation, and validate the system on English-French and English-Polish language pairs.

Article | Open Access | | Nature Communications

One challenge that faces artificial intelligence is the inability of deep neural networks to continuously learn new information without catastrophically forgetting what has been learnt before. To solve this problem, here the authors propose a replay-based algorithm for deep learning without the need to store data.

Article | Open Access | | Nature Communications

Bellec et al. present a mathematically founded approximation for gradient descent training of recurrent neural networks without backwards propagation in time. This enables biologically plausible training of spike-based neural network models with working memory and supports on-chip training of neuromorphic hardware.

Article | Open Access | | Nature Communications

Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.

Article | Open Access | | Nature Communications

Pathogenicity scores are instrumental in prioritizing variants for Mendelian disease, yet their application to common disease is largely unexplored. Here, the authors assess the utility of pathogenicity scores for 41 complex traits and develop a framework to improve their informativeness for common disease.

Article | Open Access | | Nature Communications

Deep learning is becoming a popular approach for understanding biological processes but can be hard to adapt to new questions. Here, the authors develop Janggu, a python library that aims to ease data acquisition and model evaluation and facilitate deep learning applications in genomics.

Article | Open Access | | Nature Communications

Artificial neural networks have been successfully used for language recognition. Tsai et al. use the same techniques to link between language processing and prediction of molecular trajectories and show capability to predict complex thermodynamics and kinetics arising in chemical or biological physics.

Article | Open Access | | Nature Communications

The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.

Article | Open Access | | Nature Communications

Do artificial neural networks, like brains, exhibit individual differences? Using tools from systems neuroscience, this study reveals substantial variability in network-internal representations, calling into question the neuroscientific practice of using single networks as models of brain function.

Article | Open Access | | Nature Communications

Theories of human categorization have traditionally been evaluated in the context of simple, low-dimensional stimuli. In this work, the authors use a large dataset of human behavior over 10,000 natural images to re-evaluate these theories, revealing interesting differences from previous results.

Article | Open Access | | Nature Communications

In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve prediction performance of tryptophan metabolism in baker’s yeast.

Article | Open Access | | Nature Communications

The potential for accidental or deliberate misuse of biotechnology is of concern for international biosecurity. Here the authors apply machine learning to DNA sequences and associated phenotypic data to facilitate genetic engineering attribution and identify country-of-origin and ancestral lab of engineered DNA sequences.

Article | Open Access | | Nature Communications