While AI itself does not have sensory capabilities to directly interact with the environment, it can interpret data gathered through various means and then analyze it. Here are a few ways that AI is currently being used to study and understand nature:
Using satellite imagery and other remotely sensed data, AI can analyze changes in land use, monitor deforestation, track wildlife, and even predict natural disasters like wildfires or floods. Machine learning algorithms are particularly useful in interpreting this kind of complex, multidimensional data.
AI is used to interpret sounds recorded in nature to monitor wildlife populations and understand ecosystems. For example, it can recognize the calls of specific bird species, count them, and infer information about biodiversity in a specific area.
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Environmental DNA (eDNA):
As organisms interact with their environment, they leave behind traces of DNA. Scientists can collect water or soil samples and use AI to analyze this DNA, learning about the organisms that are or were present without needing to see them directly.
AI is used to analyze vast amounts of climate data, to improve our understanding of climate change and potentially predict its future impacts.
AI, along with IoT devices, are used to monitor crops and livestock, optimize irrigation, detect disease, and improve yield while reducing environmental impact.
The key here is that AI is not “experiencing” nature the way humans or animals might. Instead, it’s analyzing data about nature collected through various means. And while AI can help us understand nature on a macro scale that would be impossible for a human to perceive, it also lacks the ability to appreciate the beauty of a sunset or the serenity of a forest, because it does not have subjective experiences or emotions. Not yet at least. Or not in a way we understand.
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