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Medical research is one of the most impactful areas for machine learning applications, but access to large and diverse health datasets is needed for models to be useful. Winning trust from patients by demonstrating that data are handled securely and effectively is key.
In the current wave of excitement about applying large vision–language models and generative AI to robotics, expectations are running high, but conquering real-world complexities remains challenging for robots.
Personalized LLMs built with the capacity for emulating empathy are right around the corner. The effects on individual users need careful consideration.
Research papers can make a long-lasting impact when the code and software tools supporting the findings are made readily available and can be reused and built on. Our reusability reports explore and highlight examples of good code sharing practices.
After several decades of developments in AI, has the inspiration that can be drawn from neuroscience been exhausted? Recent initiatives make the case for taking a fresh look at the intersection between the two fields.
One of the most successful areas for deep learning in scientific discovery has been protein predictions and engineering. We take a closer look at four studies in this issue that advance protein science with innovative deep learning approaches.
Further progress in AI may require learning algorithms to generate their own data rather than assimilate static datasets. A Perspective in this issue proposes that they could do so by interacting with other learning agents in a socially structured way.
AI-generated media are on the rise and are here to stay. Regulation is urgently needed, but in the meantime creators, users and content distributors need to pursue various ways, and adopt various tools, for responsible generation, sharing and detection of AI-generated content.
Advances in DNA nanoengineering promise the development of new computing devices within biological systems, with applications in nanoscale sensing, diagnostics and therapeutics.
Machine learning and quantum computing approaches are converging, fuelling considerable excitement over quantum devices and their capabilities. However, given the current hardware limitations, it is important to push the technology forward while being realistic about what quantum computers can do, now and in the near future.
The development of large language models is mainly a feat of engineering and so far has been largely disconnected from the field of linguistics. Exploring links between the two directions is reopening longstanding debates in the study of language.
Virtual worlds are typically encountered through simulated visual and auditory perceptions. Incorporating touch can create more immersive experiences with a sense of agency.
As many authors are experimenting with using large language models in writing articles, some guidelines are becoming clear, but these will need to evolve as the capabilities and integration of such tools develop further.
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.
A recent data competition steers clear from leaderboard chasing and promotes the use of a diverse range of metrics to develop rounded, practical algorithms.
2022 has seen eye-catching developments in AI applications. Work is needed to ensure that ethical reflection and responsible publication practices are keeping pace.
