Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
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.
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.
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.
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.
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.
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.
A white paper from Partnership on AI provides timely advice on tackling the urgent challenge of navigating risks of AI research and responsible publication.
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.
It has been a little over a year since a worldwide COVID-19 pandemic was declared. Science has moved fast to fight the virus but preparations need to be underway for fighting future outbreaks.
Hearing and vision are powerful and important senses for interacting with our surroundings. So far, advances in the area of machine vision have been the most prominent, but machine hearing research that closely mimics the complex sound processing in the human ear has exciting opportunities to offer.
