Editorials

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  • AI promises to bring many benefits to healthcare and research, but mistrust has built up owing to many instances of harm to under-represented communities. To amend this, participatory approaches can directly involve communities in AI research that will impact them. An important element of such approaches is ensuring that communities can take control over their own data and how they are shared.

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  • 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.

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  • The public release of ‘Stable Diffusion’, a high-quality image generation tool, sets new standards in open-source AI development and raises new questions.

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  • As with last summer, COVID-19 is still with us, but there is a semblance of what life was like before the pandemic. Here, we recommend AI podcasts from the past year that may inform, inspire or entertain, as we get an opportunity to travel or take time away from regular activities.

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  • Soon into the COVID-19 pandemic, civil-rights groups raised the alarm over the increase in digital surveillance infringing on individual rights. But there are other potential harms as tech companies accelerate their expansion into new areas essential to public-service provision.

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  • A biomimetic sense of touch could ground robots better in the physical world and improve their interactions with humans.

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  • Considering the potential for unintended harmful applications of AI tools can lead to deeply concerning findings. An urgent question is how to achieve the right balance between keeping science open and preventing misuse or malicious repurposing.

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  • The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

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  • Autonomous vehicle technologies need to be safer than humans by a considerable margin before they can be truly self-driving. But they can provide substantial benefits as assistive driving technology already today — provided their limitations are properly communicated.

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  • Growing criticisms of datasets that were built from user-generated data scraped from the web have led to the retirement or redaction of many popular benchmarks. Their afterlife, as copies or subsets that continue to be used, is a cause for concern.

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  • A well-known internet truth is that if the product is free, you are the product being sold. But with a growing range of regulations and web content tools, users can gain more control over the data they interact with.

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  • Although the initial inspiration of neural networks came from biology, insights from physics have helped neural networks to become usable. New connections between physics and machine learning produce powerful computational methods.

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  • Can the human brain cope with controlling an extra robotic arm or digit added to the body?

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  • Very large neural network models such as GPT-3, which have many billions of parameters, are on the rise, but so far only big tech has the resources to train, deploy and study such models. This needs to change, say Stanford AI researchers, who call for an investment in academic collaborations to build and study large neural networks.

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  • Health disparities need to be addressed so that the benefits of medical progress are not limited to selected groups. Big data and machine learning approaches are transformative tools for public and population health, but need ongoing support from insights in algorithmic fairness.

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  • The COVID-19 pandemic is not over and the future is uncertain, but there has lately been a semblance of what life was like before. As thoughts turn to the possibility of a summer holiday, we offer suggestions for books and podcasts on AI to refresh the mind.

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  • Accurate and fair medical machine learning requires large amounts and diverse data to train on. Privacy-preserving methods such as federated learning can help improve machine learning models by making use of datasets in different hospitals and institutes while the data stays where it is collected.

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  • A white paper from Partnership on AI provides timely advice on tackling the urgent challenge of navigating risks of AI research and responsible publication.

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  • Citizen scientists are empowered by mobile technology to collect data and crowdsource knowledge. Furthermore, automated machine learning tools allow non-experts in AI to analyse data. Ethical and regulatory questions arise, however, as data collection and AI technologies become enmeshed in people’s lives.

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