How ecology and AI could collaborate

2023-09-12
1 min read.
Could improve predictive understanding of complex systems and help block generative AI "hallucinations"
How ecology and AI could collaborate
An image generated by DALL-E using the prompt, “a synergistic future for artificial intelligence and complex ecological systems” (credit: Barbara Han)

A paper published September 11 in the open-access journal Proceedings of the National Academy of Sciences argues for a synergy between AI and ecology—one that could strengthen AI and also help to solve complex global challenges, such as disease outbreaks, loss of biodiversity, and climate-change impacts.

Dealing with complex systems

The paper argues that there are many more possibilities for applying AI in ecology, such as in "synthesizing big data and finding missing links in complex systems," said co-author Shannon LaDeau, a disease ecologist at Cary Institute.

Ecologists are also using AI to searching for patterns in large data sets and making more accurate predictions, such as whether new viruses might be capable of infecting humans and which animals are most likely to harbor those viruses.

Smarter AI

Inspired by ecological systems, a more robust AI might include feedback loops, redundant pathways, and decision-making frameworks, the researchers suggest. These flexibility upgrades could also contribute to a more "general intelligence" for AIs that could enable reasoning and connection-making beyond the specific data that the algorithm was trained on.

Ecology could also help the AI-driven large language models that power chatbots block "hallucinations" (when an AI generates false information). Because ecology examines complex systems at multiple levels and in holistic ways, it's good at capturing emergent properties and can help reveal the mechanisms behind such behaviors, the researchers suggest.

The research was funded by the National Science Foundation.

Citation: Barbara A. Han et al. September 11, 2023. A synergistic future for AI and ecology. https://www.pnas.org/doi/10.1073/pnas.2220283120 (open source)

https://doi.org/10.1073/pnas.2220283120


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