An automated system that listens for the sounds of gunfire with arrays of sensors can be used to pinpoint where gunshots came from and alert the authorities within 45 seconds of the trigger being pulled. The ShotSpotter system uses 15 to 20 acoustic sensors per square mile to detect the distinctive “pop” of a gunshot, using the time it takes to reach each sensor to and uses algorithms to reveal the location to within 25 metres.

Machine learning technology is used to confirm the sound is a gunshot and count the number of them, revealing whether police might be dealing with a lone shooter or multiple perpetrators and if they are using automatic weapons.

There are 90 cities - many in the US but some in South Africa and South America - now using ShotSpotter with others in discussions. Smaller systems have also been deployed on nine college campuses in the US in response to recent campus shootings while the US Secret Service has installed it at the White House.

But Ralph Clark, chief executive of ShotSpotter, believes the system could in the future be used for more than simply responding to incidents.

“We are keen to see how our data can inform more predictive policing opportunities,” he says. “Machine learning can combine it with weather, traffic data, property crime data to inform the deployment of police patrols more precisely.”

Keeping famine from the door



Around 800 million people worldwide rely upon cassava roots as their main source of carbohydrate. The starchy vegetable, which is similar to yam, is often eaten much like potatoes, but can also be ground into a flour for making bread and cakes. Its ability to grow where other crops do not has turned it into sixth-most-produced food plant in the world. But the woody shrub is also highly vulnerable to disease and pests, which can devastate entire fields of the vegetable.

Researchers at Makerere University in Kampala, Uganda, have teamed up with plant disease experts to develop an automated system aimed at combating cassava diseases. The Mcrops project allows local farmers to take pictures of their plants using cheap smartphones and uses computer vision that has been trained to spot the signs of the four main diseases that are responsible for ravaging cassava crops.

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“Some of these diseases are really hard to recognise and require different action,” explains Ernest Mwebaze, a computer technology researcher leading the project. “We are giving the farmers an expert in their pocket so they know if they need to spray their crops or rip them up and plant something else.”

The system can now diagnose cassava diseases with 88% accuracy. Normally farmers have to call the government-employed experts to visit their farms to identify diseases, which can take days and even weeks, allowing pests and blights to spread.

MCrops also uses the uploaded images to look for patterns in disease outbreaks, something that could allow officials to halt epidemics that can lead to famine. Mwebaze and his colleagues are hoping to use technology to also look at banana diseases and to automate the detection of other crop pests.

Fighting cancer and sight loss

