During his PhD at UMass, Nikhil Malvankar was laser-focused on quantum mechanics and the movement of electrons in superconductors. Now a professor at Yale, the native of Mumbai, India, has pivoted towards biology to explain how bacteria breathe deep underground without the aid of oxygen. 

To date, his lab at the Yale Microbial Sciences Institute has uncovered the evolutionary trick used by bacteria to breathe through tiny protein filaments, called nanowires, to dispose of excess electrons from the conversion of organic waste to electricity. The adaptation has enabled bacteria to send electrons over distances 100-times their size through what the scholars refer to as bacterial “snorkeling.”

Birds flock in order to forage and move more efficiently. Fish school to avoid predators. And bees swarm to reproduce. Recent advances in artificial intelligence have sought to mimic these natural behaviors as a way to potentially improve search-and-rescue operations or to identify areas of wildfire spread over vast areas—largely through coordinated drone or robotic movements. However, developing a means to control and utilize this type of AI—or “swarm intelligence”—has proved challenging. In a newly published paper, an international team of scientists describes a framework designed to advance swarm intelligence—by controlling flocking and swarming in ways that are akin to what occurs in nature.

The paper proposes a generic risk theory that treats risk as information produced by human cognition. It introduces a quantitative descriptive model linking spontaneous risk perception and analytical risk cognition through disparities between target and realistic value expectations, outlines conditions for when perception occurs, and connects the framework to decision-making and potential AI-enabled implementations.