In the desert researchers demonstrate that an artificial neural network can pinpoint new fossil-rich sites, paving the way for more efficient digs
On blisteringly hot desert sands, researchers crawled on their hands and knees avoiding fist-size cacti littering the ground. Their goal: collecting bones and teeth of some of the earliest known primates to shed light on the adaptations at the root of the evolutionary lineage that led to humans. The fossils, though, are the size of a fingernail or smaller, and they are scattered over an area of about 10,000 square kilometers in the rocky desert of Wyoming’s Great Divide Basin.
That’s a lot of ground to cover, especially on all fours and in searing heat. So the scientists are relying on a tool never tried before in paleontology: artificial intelligence. Such an approach might be able to pinpoint fossil troves in their giant needle-in-a-haystack quest and suggest new strategies for fossil hunting. It then remained for them to wander to the middle of the desert to see if their innovation led them on a wild goose chase or not.
Normally, discovering fossils depends largely on luck. Paleontologists can take educated guesses as to where to search—trekking down dry stream beds to look for bones that might have eroded off slopes, for instance—but they mostly depend on walking around to see what catches the eye. If they are lucky, they can cover ground in bucking and bouncing jeeps down dirt roads set up by oil and gas companies. In any case, traditional approaches can be challenging, lengthy—and fruitless.
Increasingly, paleontologists are relying on technology to narrow their search for fossils. For instance, Google Earth has helped identify sites in South Africa containing fossils of the ancient hominid Australopithecus sediba.
But instead of inspecting satellite imagery by eye for potential sites, paleontologist Robert Anemone and remote-sensing specialist Jay Emerson of Western Michigan University and their colleagues have developed a way to automate the operation using an artificial neural network, a computer system that imitates how the brain learns. Their aim was to take advantage of how brains, both natural and artificial, quickly learn and recognize patterns, such as what fossils look like.
via Scientific American – Charles Q. Choi
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