Citizen Data Scientists Needed to Save the Planet – RTInsights

The Earth Challenge 2020 initiative overcomes AI model training challenges using citizen data scientists to collect data for environment and healthcare apps.

A small army of citizen data scientists is being mobilizedto collect data to help train machine learning algorithms that will be embeddedin a range of environment and healthcare applications.

As part of an Earth Challenge 2020 initiative sponsored by the Earth Day Network, the Wilson Center, and the U.S. State Department, applications that tackle everything from food safety and the tracking of insect populations to plastics pollution and air quality are now being made available.

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Earth Challenge 2020 is an arm of an Earth School coalitionspearheaded by the United Nations Environment Programme and TED-Ed, which iscommitted to providing free educational science content to students, parents,and teachers.

The goal of the Earth Challenge 2020 initiative is to enable citizen data scientists to collect, label, and tag data using mobile computing applications that is then fed into an analytics database from Kinetica that runs on graphical processor units (GPUs). That approach among other applications will enable school children to take photos of insects using a Picture Pile application from Applied Systems Analysis to train machine learning algorithms to recognize not just different types of insects, but where they are also found at different times of the year.

The labeled insect images that collected are then added to adata set collected by the European Space Agency that is being created to betterunderstand how insects such as bees impact food production. Once enough imagesare labeled the machine learning algorithms eventually start to recognizedifferent images, which then allows them to automatically label and tag themwithout any further human assistance required.

All the data sets being collected by the mobile applicationscreated as part of the Earth Challenge 2020 initiativewill be made available for free to data scientists via a Citizen Science Cloud serviceor Kinetica REST application programming interfaces (APIs), says Daniel Raskin,chief marketing officer for Kinetica.

It will all be in the public domain, says Raskin.

Kinetica is participating in this effort as part of aneffort to spur adoption of an analytics database that runs natively on the sameGPUs that are being widely employed to train artificial intelligence (AI)models. Machine learning algorithms run considerably faster of GPUs, whichreduces the time and costs associated with training AI models.

The challenge many organizations building AI models face iscollecting all the data needed to train an AI model. The Earth Challenge 2020initiative helps address that issue by enlisting what will hopefully become anarmy of citizen data scientists to help collect data for what will become a broadportfolio of environment and healthcare-related applications, says Raskin.

Its too early to tell just what impact individuals armedwith smartphones capable of capturing high-quality images might have on data science.With more individuals of all ages spending more time at home to combat theCOVID-19 pandemic the opportunity to potentially motivate people around theworld to participate in various initiatives has never been greater. Thechallenge, of course, is finding a way to let all those potential citizen datascientists that the opportunity to participate exists in the first place.

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Citizen Data Scientists Needed to Save the Planet - RTInsights