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A new international competition aims to speed up the development of AI models that can assist radiologists in detecting suspicious lesions from hundreds of millions of pixels in 3D mammograms. The top three winning teams compare notes.
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.
Large language models, which are increasingly used in AI applications, display undesirable stereotypes such as persistent associations between Muslims and violence. New approaches are needed to systematically reduce the harmful bias of language models in deployment.
A white paper from Partnership on AI provides timely advice on tackling the urgent challenge of navigating risks of AI research and responsible publication.
We spoke with Mariarosaria Taddeo, an associate professor and senior research fellow at the Oxford Internet Institute and Dstl Ethics Fellow at the Alan Turing Institute working on digital and AI ethics about two recent reports from the UK and the US on using AI in national defence and security.
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.
The COVID-19 pandemic has highlighted key challenges for patient care and health provider safety. Adaptable robotic systems, with enhanced sensing, manipulation and autonomy capabilities could help address these challenges in future infectious disease outbreaks.
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.
To truly understand the societal impact of AI, we need to look beyond the exclusive focus on quantitative methods, and focus on qualitative methods like ethnography, which shed light on the actors and institutions that wield power through the use of these technologies.
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.
We invited authors of selected Comments and Perspectives published in Nature Machine Intelligence in the latter half of 2019 and first half of 2020 to describe how their topic has developed, what their thoughts are about the challenges of 2020, and what they look forward to in 2021.
A research paper makes the most impact when its methods, data and code are available for others to use and build on. We highlight the benefits of good sharing practices with a new type of article, reusability reports.
Artificial intelligence can be defined as intelligence demonstrated by machines. But what counts as intelligence, and how intelligence is implemented in different kinds of machines, robots and software varies across disciplines and over time.
Synthesizing robots via physical artificial intelligence is a multidisciplinary challenge for future robotics research. An education methodology is needed for researchers to develop a combination of skills in physical artificial intelligence.
