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The Creation Of A New AI Program Could Help Scientists Predict Future Catastrophic Disasters

The creation of a new artificial intelligence (AI) program could help scientists predict future catastrophic disasters, such as pandemics, power outages, financial crashes, and ecological collapse.
In the past, predicting when “tipping points” would occur was a pain in the butt for scientists, but AI can make the load a little easier.
Tipping points are described as sudden shifts to an undesirable state that is extremely difficult to reverse.
“If an upcoming critical transition can be forecast then we can prepare for the shift or perhaps even prevent the transition, and thus mitigate damage,” Gang Yan, senior study author and a professor of computer science at Tongji University in China, said.
“This motivated us to develop an AI approach to predict the onset of such sudden transitions far before it happens.”
For example, if the ice sheet in Greenland were to melt completely, it would reduce snowfall in the region and drastically raise global sea levels. The Earth would also rotate more slowly, and a day’s length would become about two milliseconds longer.
Previously, scientists used statistical methods to build models that predicted transformations. But, the models were oversimplified and did not result in accurate predictions. So, for the new study, the researchers wanted to find a more accurate way to make predictions.
They combined two types of neural networks, or algorithms, that imitate the way the brain processes information.
The first type broke down complex systems into interacting nodes and tracked the connections between the nodes. The second monitored how individual nodes changed over time.
Scott – stock.adobe.com – illustrative purposes only
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“For example, in a financial system, a node could be a single company; in an ecological system, a node could denote a user, and so forth,” Yan said.
Then, the researchers trained their model by feeding it data from simple theoretical systems. Afterward, they finally gave the model a real-world problem to analyze—the transformation of tropical forests into savannah.
They took more than 20 years of data on rainfall and tree coverage in three regions in Central Africa and fed the algorithm information on two of those regions.
The AI was able to accurately predict what happened in the third region. Now, the researchers hope to apply the model to other systems like pandemics, financial crashes and wildfires. They will focus on parts of human systems that cannot be affected by human intentions.
For instance, when drivers are aware of which roads are congested, they may alter their routes. Some roads may become less crowded, but others may get worse. It’s a challenge that comes with predicting systems.
“Using AI to capture these fundamental signals can be valuable for making predictions,” Yan said.
“Although predicting such systems is challenging, it is worthwhile because critical transitions in human-involved systems can have even more severe consequences.”
The study was published in the journal Physical Review X.

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