Science, Space and Technology News 2023
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The idea arose from the observation that AI can be shockingly good at certain tasks, but still far from useful at others — and that AI development is hitting walls that ecological principles could help it to overcome. Scientists typically try to understand the world by comparing two variables at a time — for example, how does population density affect the number of cases of an infectious disease? The problem is that, like most complex ecological systems, predicting disease transmission depends on many variables, not just one, explained co-author Shannon LaDeau, a disease ecologist at Cary Institute.
The incredible resilience of ecological systems could inspire more robust and adaptable AI architectures, the authors argue. In particular, Varshney said that ecological knowledge could help to solve the problem of mode collapse in artificial neural networks, the AI systems that often power speech recognition, computer vision, and more.
Ecology could also help to reveal why AI-driven large language models, which power popular chatbots such as ChatGPT, show emergent behaviors that are not present in smaller language models. These behaviors include ‘hallucinations’ — when an AI generates false information. Because ecology examines complex systems at multiple levels and in holistic ways, it is good at capturing emergent properties such as these and can help to reveal the mechanisms behind such behaviors.
Closer collaboration also promises to promote greater social responsibility in both fields. Ecologists are working to incorporate diverse ways of understanding the world from Indigenous and other traditional knowledge systems, and artificial intelligence could help to merge these different ways of thinking. Finding ways to integrate different types of data could help to improve our understanding of socio-ecological systems, de-colonize the field of ecology, and correct biases in AI systems.
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