News & Comment

We are trying to keep up with the torrent of developments and discussions in AI and language models since ChatGPT was unleashed on the world.

In the next phase of space exploration, human crews will be sent on missions beyond the low Earth orbit. Artificial intelligence (AI) is expected to play a main role in autonomous biomonitoring, research and Earth-independent healthcare.

We explore the intersection between algorithms and the State from the perspectives of legislative action, public perception and the use of AI in public administration. Taking India as a case study, we discuss the potential fallout from the absence of rigorous scholarship on such questions for countries in the Global South.

A recent data competition steers clear from leaderboard chasing and promotes the use of a diverse range of metrics to develop rounded, practical algorithms.

Despite the promise of medical artificial intelligence applications, their acceptance in real-world clinical settings is low, with lack of transparency and trust being barriers that need to be overcome. We discuss the importance of the collaborative process in medical artificial intelligence, whereby experts from various fields work together and tackle transparency issues and build trust over time.

The organizers of the EvalRS recommender systems competition argue that accuracy should not be the only goal and explain how they took robustness and fairness into account.

To fully leverage big data, they need to be shared across institutions in a manner compliant with privacy considerations and the EU General Data Protection Regulation (GDPR). Federated machine learning is a promising option.

Guidelines are urgently needed for the use of generative AI tools like ChatGPT in scientific writing.

A recent case of a flawed medical AI system that was backed by public funding provides an opportunity to discuss the impact of government policies and regulation in AI.

2022 has seen eye-catching developments in AI applications. Work is needed to ensure that ethical reflection and responsible publication practices are keeping pace.

The notion of ‘interpretability’ of artificial neural networks (ANNs) is of growing importance in neuroscience and artificial intelligence (AI). But interpretability means different things to neuroscientists as opposed to AI researchers. In this article, we discuss the potential synergies and tensions between these two communities in interpreting ANNs.

The implementation of ethics review processes is an important first step for anticipating and mitigating the potential harms of AI research. Its long-term success, however, requires a coordinated community effort, to support experimentation with different ethics review processes, to study their effect, and to provide opportunities for diverse voices from the community to share insights and foster norms.

Artificial intelligence systems are used for an increasing range of intellectual tasks, but can they invent, or will they be able to do so soon? A recent series of patent applications for two inventions that are claimed to have been made by an artificial intelligence program are bringing these questions to the fore.

A design contest for object detection with deep learning on embedded small devices leads to winning hardware–software co-design approaches.

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.

The use of decision-support systems based on artificial intelligence approaches in antimicrobial prescribing raises important moral questions. Adopting ethical frameworks alongside such systems can aid the consideration of infection-specific complexities and support moral decision-making to tackle antimicrobial resistance.

Indigenous peoples are under-represented in genomic datasets, which can lead to limited accuracy and utility of machine learning models in precision health. While open data sharing undermines rights of Indigenous communities to govern data decisions, federated learning may facilitate secure and community-consented data sharing.