News Release

Special Issue: A machine-intelligent world

Reports and Proceedings

American Association for the Advancement of Science (AAAS)

In this special issue of Science, nine pieces – including Perspectives, Policy Forums, and Reviews – highlight recent advancements in artificial intelligence (AI) technologies and how they’re being used to answer novel questions in topics ranging from human health to animal behavior. However, the recent widespread adoption of AI in these areas is not without unique ethical concerns and policy challenges. “By looking to the forefront of how AI is being used in science and society, many grand challenges and benefits appear,” writes Gemma Alderton, deputy editor at Science.


AI-predicted race variables from medical images pose risks and opportunities for studying health disparities, say James Zou and colleagues in a Perspective. Hundreds of AI-assisted medical devices are currently used in diverse medical tasks, such as assessing health risks and diagnosing diseases like cancer. Some studies have shown that AI models can infer race variables – albeit in crude, simplistic categories – directly from medical images like chest x-rays and cardiac ultrasounds, despite no known human-readable race correlates in the images. “Although race variables are not a generally meaningful category in medicine, the ability of AI to predict race variables from medical images could be useful for monitoring health care disparity and ensuring that algorithms work well across diverse populations,” Zou et al. write. In a second Perspective, Matthew DeCamp and Charlotta Lindvall highlight how examination of bias in AI and healthcare has tended toward removing bias from datasets, analyses, or in AI development teams. However, DeCamp and Lindvall argue that it will also require reducing biases in how clinicians and patients use AI-based algorithms, which could be more challenging than reducing biases in the algorithms themselves.


AI technologies also show great promise in expanding our understanding of animal behaviors. In a third Perspective, Christian Rutz and colleagues review how machine learning (ML) methods are being used to decode animal communication systems. Understanding how animals communicate presents a host of challenges – animals use a wide range of communication adaptations, including visual, acoustic, tactile, chemical, and electrical signals, often in ways beyond humans’ perceptive abilities. Here, Rutz et al. review ways in which increasingly powerful ML tools are being used to reveal previously hidden complexity in animals’ communicative behavior, with insights that could lead to potential benefits for animal welfare and conservation. “…it is essential that future advances are used to benefit the animals being studied,” write Rutz et al.


In a fourth Perspective, Peter Wurman and colleagues highlight how games provide controlled opportunities to isolate and practice many problem-solving skills that are more broadly transferable to real-world applications, which makes them valuable training grounds for intelligent machines. While the recent dominance of AI in classic strategy games has largely been achieved, Wurman and colleagues argue that video games pose new types of challenges for AI to conquer. Making progress in these arenas will represent a substantial step toward much more capable and flexible AI systems that operate in the physical world. 


Generative AI – a type of AI technology that can produce a wide variety of content such as images, videos, audio, and text – has rapidly become widely adopted by the general public, scientists, and technologists. However, a growing number of professional artists, writers, and musicians have raised objections to the use of their creations as training data for these systems. In a Policy Forum, Pamela Samuelson highlights this emerging issue and discusses how several copyright lawsuits, now underway in the U.S., could have substantial implications for the future of generative AI systems. If the plaintiffs in these cases prevail, the only material generative AI systems could lawfully be trained on would be public domain works or those under licenses, which would affect everyone who uses the technology, including for scientific research. In a second Policy Forum, Ajay Agrawal and colleagues discuss how task automation via AI innovations could reverse current trends of increasing income inequality. Given the rapid development of AI technologies that enable automation of cognitive and creative endeavors once reserved for humans with specialized education and experience, some economists have raised concerns that that AI has the potential to substantially disrupt the labor market and further increase inequality, albeit with little benefit to productivity and standard of living. Here, Agrawal et al. argue that, by considering how tasks can be automated, AI developers could create tools that enhance the overall productivity of workers. What’s more, AI automation could also reduce income inequality by offering innovations that allow lower-wage and less skilled workers to perform at levels that would previously require specialized training.


In one Review, Felix Wong and colleagues discuss how advances in AI are empowering medical and biotechnological research in the fight against infectious disease. According to Wong et al., AI technologies, like ML, have led to rapid advancements in anti-infective drug discovery, our understanding of infection biology, and the development of new diagnostics. Further applications could also improve our ability to forecast and control infectious disease outbreaks and pandemics. A second Review by Bing Huang and colleagues focuses on the crucial role “Destiny Functional Theory” – pivotal in chemical and materials science because of its relatively high predictive power – has played in the development of ML-based models used to navigate chemical compound space. Huang et al. argue that continued advancements in this space pave the way toward software control solutions that can routinely handle exotic chemistries and formulations within self-driving laboratories.


Lastly, a series of Vignettes by various authors highlight AI’s applications in advanced medical robots. AI technologies used in these devices, including computer vision, medical imaging analysis, precise manipulation, and ML, could enable autonomous robots to perform diagnostic imaging and assist in complex surgical procedures. Furthermore, AI in wearable rehabilitation devices and advanced prosthetics could enable more personalized patient care and even AI-powered prosthetics that operate seamlessly with the human user.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.