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FREIGHTER GAP, Wyo.—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.
Training the artificial brain
Artificial neurons are components of computer programs that mimic real neurons in that each neuron can send, receive and process information. Researchers first train the networks by feeding data to the artificial neurons and letting them know when their computations solve a given problem, such as reading handwriting or recognizing speech. The networks then alter the patterns of connections among these neurons to change the way they communicate with one another and work together. With such practice, the networks figure out which arrangements among neurons are best at computing desired answers.
The neural network presented the promise of locating fossil-rich sites "without walking over miles and miles of barren outcrops," says paleontologist John Fleagle of Stony Brook University. "It could save lots and lots of time and expense in the field."
That's why Anemone and his colleagues were out in the Wyoming desert with a neural network running on a laptop computer. It analyzed visible- and infrared-light satellite and aerial images of the Great Divide Basin, which included 100 known fossil sites. They first let the network know that 75 of these areas were fossil-rich so it could learn what this kind of site looked like. When they had it search for the other 25 sites, it correctly spotted 20 of them, raising hopes that it could identify new candidates.