Researchers are using Darwin's theories to evolve AI, so only the strongest algorithms survive

Modern artificial intelligence is built to mimic nature--the field's main pursuit is replicating in a computer the same decision-making prowess that humankind creates biologically. For the better part of three decades, most of AI's brain-inspired development has surrounded "neural networks," a term borrowed from neurobiology that describes machine thought as the movement of data through interconnected mathematical functions called neurons. But nature has other good ideas, too: Computer scientists are now revisiting an older field of study that suggests putting AI through evolutionary processes, like those that molded the human brain over millennia, could help us develop smarter, more efficient algorithms. The concept of evolution, famously credited to Charles Darwin and refined by countless scientists since, states that slight, random changes in an organism's genetic makeup will give it either an advantage or disadvantage in the wild. If the organism's mutation allows it to survive and reproduce, that mutation is then passed along.