Breakthroughs, discoveries, and DIY tips sent every weekday. As social flying mammals, bats typically live in colonies (or cauldrons) of up to 100 individual bats. While many species work together to forage, spectral bats (Vampyrum spectrum were previously believed to be more solitary when finding food. However, that may not be the case. Not only do they appear to forage in groups, they also display affectionate greetings to one another and provide food to feed their families.

In this article we address the colony maintenance problem, where a team of robots are tasked with continuously maintaining the energy supply of an autonomous colony. We model this as a global game, where robots measure the energy level of a central nest to determine whether or not to forage for energy sources. We design a mechanism that avoids the trivial equilibrium where all robots always forage. Furthermore, we demonstrate that when the game is played iteratively a negative feedback term stabilizes the number of foraging robots at a non-trivial Nash equilibrium. We compare our approach qualitatively to existing global games, where a positive positive feedback term admits threshold-based decision making, and encourages many robots to forage simultaneously. We discuss how positive feedback can lead to a cascading failure in the presence of a human who recruits robots for external tasks, and we demonstrate the performance of our approach in simulation.

When we think of artificial intelligence, we might think of robots like the ones in "Ex Machina," who are scarily smarter, closer to humans, and more perceptive than we think. The reality is that artificial intelligence is an innovative, growing field that offers creative opportunities for those who want to revolutionize the way we use technology. So, what is artificial intelligence, and what does a career in the field look like? Artificial intelligence (AI) is a branch of computer science concerning machines that can synthesize and process information to problem-solve. The concept first came into public view with Alan Turing's 1950 paper, "Computing Machinery and Intelligence," which explored whether we could train machines to think like humans.

Computer scientists at Stanford University created the first AI, an algorithm called Forage. Using a dataset of 60,000 recipes, they trained Forage to craft new recipes using whatever you tell it is in your fridge. When given a list of typical holiday leftovers to work with, Forage came up with recipes for a Spanish potato casserole, turkey croquettes, and a spicy seafood casserole. The BBC reports that they were, respectively, "well-received," "not too bad to eat," and "about as close to a bout of food poisoning as we were prepared to get." After reading the recipe for that last one, in fact, they couldn't even bring themselves to cook it.

Figure 1 illustrates grasshopper infestation densities in the western United States during 2000, a fairly typical year. In years of heavy infestation, grasshopper densities and economic losses might be much higher. For example, during the 1986 to 1987 outbreak, over 20 million acres of rangeland were treated for grasshoppers in the western United States at a cost of more than $75 million. In Wyoming, the estimated total annual loss to grasshoppers is roughly $19 million. The southeastern quadrant of the state is particularly prone to grasshopper infestations, with significant areas of high-grasshopper densities in 30 of the last 34 years.

Lying in a bath in Bristol is a robotic scavenger, gorging itself on its surroundings. It's able to get just enough energy to take in another stomach full of food, before ejecting its waste and repeating the process. This is no ordinary robot. Developed by a Bristol-based collaboration, this robot imitates the life of salps – squishy tube-shaped marine organisms. Salps have an opening at each end, one for food to enter and one for waste to leave.

CARMA is an advisory system for rangeland grasshopper infestations that demonstrates how AI technology can deliver expert advice to compensate for cutbacks in public services. CARMA uses two knowledge sources for the key task of predicting forage consumption by grasshoppers: (1) cases obtained by asking a group of experts to solve representative hypothetical problems and (2) a numeric model of rangeland ecosystems. These knowledge sources are integrated through the technique of model-based adaptation, in which case-based reasoning is used to find an approximate solution, and the model is used to adapt this approximate solution into a more precise solution. CARMA has been used in Wyoming counties since 1996. The combination of a simple interface, flexible control strategy, and integration of multiple knowledge sources makes CARMA accessible to inexperienced users and capable of producing advice comparable to that produced by human experts. Moreover, because CARMA embodies diverse forms of expertise, it has been used in ways that its developers did not anticipate, including pest management research, development of industry strategies, and in-state and federal pest-management policy decisions.