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COVID-19 Dashboard Uses Machine Learning to Track…. Anodot, the company pioneering the autonomous monitoring space, has launched a public service including a machine learning driven analytics dashboard that monitors locally reported COVID-19 cases, and notifies users when a particular region’s numbers change significantly.

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How AI Is Tracking the Coronavirus Outbreak

With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere.

Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel.

A company called BlueDot used a similar approach-minus the social media sources-to spot the coronavirus in late December, before Chinese authorities acknowledged the emergency.

The rate of new infections has slowed slightly in recent days, from 3,900 new cases on Wednesday to 3,700 cases on Thursday to 3,200 cases on Friday, according to the World Health Organization.

Brownstein says colleagues tracking Chinese social media and news sources were alerted to a cluster of reports about a flu-like outbreak on December 30.

Beyond identifying new cases, Brownstein says the technique could help experts learn how the virus behaves.

Alessandro Vespignani, a professor at Northeastern University who specializes in modeling contagion in large populations, says it will be particularly challenging to identify new instances of the coronavirus from social media posts, even using the most advanced AI tools, because its characteristics still aren’t entirely clear.

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AI-powered robots will be the next big work revolution in warehouses

For the bot’s creators, Californian AI and robotics startup Covariant, the installation in Germany is a big step forward, and one that shows the firm has made great strides with a challenge that’s plagued engineers for decades: teaching robots to pick things up.

Google has run a stable of robot arms in an attempt to learn how to reliably grasp things, while Amazon holds an annual competition challenging startups to stock shelves with robots in the hope of finding a machine good enough for its warehouses.

This is particularly true in the world of warehouses and logistics, where experts say it’s difficult to find human workers and they need all the robots they can get.

Covariant uses a variety of AI methods to train its robots, including reinforcement learning: a trial and error process where the robot has a set goal and has to solve it itself.

“Non-AI robots can pick around 10 percent of the products used by our customers, but the AI robot can pick around 95 to 99 percent,” says Puchwein.

In Germany, Covariant’s picking robot is packing electronics components for a firm named Obeta, but the company says it’s eager for more robots to compensate for a staff shortage – a situation common in logistics.

What about the employees that Covariant’s robots now operate alongside – do they mind the change? According to Pultke, they don’t see it as a threat, but an opportunity to learn how to maintain the robots and get a better type of job.

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