Monitoring environmental compliance is a particular challenge for governments in poor countries. A new machine learning approach that uses satellite imagery to pinpoint highly polluting brick kilns in Bangladesh could provide a low-cost solution.
Stanford Institute for Human-Centered Artificial Intelligence —
Applying machine learning to a U.S. Environmental Protection Agency initiative reveals how key design elements determine what communities bear the burden of pollution. The approach could help ensure fairness and accountability in machine learning used by government regulators.
Stanford Institute for Human-Centered Artificial Intelligence —
In traditional electronics, separate chips process and store data, wasting energy as they toss data back and forth over what engineers call a “memory wall.” New algorithms combine several energy-efficient hybrid chips to create the illusion of one mega–AI chip.
Stanford engineers investigated how people’s moods might affect their trust of autonomous products, such as smart speakers. They uncovered a complicated relationship.
Stanford Institute for Human-Centered Artificial Intelligence —
Over time, prediction algorithms become specialized for an increasingly narrow slice of the population, and the average quality of their predictions declines.
Stanford Institute for Human-Centered Artificial Intelligence —
Algorithms developed by Stanford researchers could one day help disabled people fluidly and intuitively control robot arms to help with everyday tasks.
