Feb 15, 2021 05:51 AM
https://www.wired.co.uk/article/ai-predi...arthquakes
EXCERPT: Seismologists will unequivocally tell you that anyone claiming they can predict future earthquakes are false prophets. But that’s not to say that earthquake prediction is impossible. Scientists have invested a tremendous amount in achieving this hallowed goal, it's just that so far they’ve come up short.
In December 2018, a coalition of researchers decided to try something new. They announced an online competition, open to anyone, in which participants had to predict future earthquakes being generated by a vice-like device in a laboratory. The twist? They had to design their own rudimentary artificial intelligences to make the predictions.
Thousands of people from across the world threw their hats into the ring. As reported by an article published in the Proceedings of the National Academy of Sciences earlier this month, the winning teams managed to come up with collections of code that managed to predict the timing of future laboratory earthquakes with striking precision.
It still isn’t clear how applicable this is to a real-life fault zone. But the promise of these new machine learning models implies that earthquake prediction isn’t a pipe dream, but a plausible possibility. And seeing as none of the victors had a background in seismology, this competition shows the benefits of casting an extremely wide net to find otherwise hidden talent – the sort that may one day save millions of lives.
[...] machine learning. Crudely put, this describes the ability of a computer code to absorb data, identify patterns, make choices or predictions, then learn from its mistakes to correct itself – all without significant human intervention. For seismologists, it’s a novelty; they have only begun discussing its potential and their work with it at major scientific conferences in the past few years.
And yet it’s already being used in the real world. A project that Ross was part of used machine learning to find millions of earthquakes buried in the seismological records of southern California. After being exposed to reams of seismic data, their software was able to quickly distinguish between random rumbles and the genuine grumbled of earthquakes, the sort imperceivable by humans... (MORE - details)
EXCERPT: Seismologists will unequivocally tell you that anyone claiming they can predict future earthquakes are false prophets. But that’s not to say that earthquake prediction is impossible. Scientists have invested a tremendous amount in achieving this hallowed goal, it's just that so far they’ve come up short.
In December 2018, a coalition of researchers decided to try something new. They announced an online competition, open to anyone, in which participants had to predict future earthquakes being generated by a vice-like device in a laboratory. The twist? They had to design their own rudimentary artificial intelligences to make the predictions.
Thousands of people from across the world threw their hats into the ring. As reported by an article published in the Proceedings of the National Academy of Sciences earlier this month, the winning teams managed to come up with collections of code that managed to predict the timing of future laboratory earthquakes with striking precision.
It still isn’t clear how applicable this is to a real-life fault zone. But the promise of these new machine learning models implies that earthquake prediction isn’t a pipe dream, but a plausible possibility. And seeing as none of the victors had a background in seismology, this competition shows the benefits of casting an extremely wide net to find otherwise hidden talent – the sort that may one day save millions of lives.
[...] machine learning. Crudely put, this describes the ability of a computer code to absorb data, identify patterns, make choices or predictions, then learn from its mistakes to correct itself – all without significant human intervention. For seismologists, it’s a novelty; they have only begun discussing its potential and their work with it at major scientific conferences in the past few years.
And yet it’s already being used in the real world. A project that Ross was part of used machine learning to find millions of earthquakes buried in the seismological records of southern California. After being exposed to reams of seismic data, their software was able to quickly distinguish between random rumbles and the genuine grumbled of earthquakes, the sort imperceivable by humans... (MORE - details)