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A Summer Camp With a Long Plan: Keeping Bias Out of Artificial Intelligence

#artificialintelligence

Anaya Bussey didn't know much about "artificial intelligence" when she arrived at a camp at Princeton University earlier this summer other than that "it was definitely blowing up." But after just three weeks here she and other students--all incoming high school juniors--teamed up to use the technology to help diagnose melanoma by looking at skin lesions. Bussey, 15, who is from the Bronx borough in New York City, has been interested in computer science since she was in elementary school. But there have been times when she's been one of only a handful of girls--or black students--in a computer class or program. That wasn't the case at the Princeton summer camp, run by AI4ALL, a two-year-old nonprofit that seeks to increase diversity and inclusion in AI education, research, and policy.


A Case-Based Rangeland Management Adviser

AI Magazine

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.


Accelerated processing teaches autonomous cars to drive in minutes - SiliconANGLE

#artificialintelligence

Speed is the name of the game in the processor world, and the latest competitive sprint down the innovation track involves field programmable gate arrays, known as FPGAs. Since Intel Corp. announced FPGA acceleration platforms operating with Xeon CPUs early last month, several companies have been showcasing a number of use cases for the lightning-fast technology. At a computing conference last month, one firm created buzz among attendees with a machine learning-based demonstration where simulated cars were taught to drive in less time than it takes to eat a sandwich. "Within a few minutes and about 15 million simulations, the cars start driving better than humans," said John Lockwood (pictured), chief executive officer at Algo-Logic Systems Inc. "You can give [machines] man-years of experience in a few minutes with these scale-out computer systems." Lockwood visited theCUBE, SiliconANGLE Media's mobile livestreaming studio, and spoke with host Jeff Frick (@JeffFrick) during the recent Supercomputing event in Denver, Colorado.


How to build the video game you wanted when you were 13

Popular Science

Lockwood grew up in Brooklyn, gaming with his two brothers. The siblings also spent a lot of time watching movies--he insists they wore out no less than 20 Star Wars tapes from repeated viewings--and playing outside. In fact, as entry fee for running around in the open air, his mother made them write a page-long short story. Lockwood gives a lot of credit to these stories, and his mother. He thinks they were what got him thinking about storytelling in video games.


CARMA: A Case-Based Rangeland Management Adviser

AI Magazine

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.