New XPrize Challenge: Predicting Covid-19's Spread and Prescribing Interventions

Slashdot reader the_newsbeagle shares an article from IEEE Spectrum:
Many associate XPrize with a $10-million award offered in 1996 to motivate a breakthrough in private space flight. But the organization has since held other competitions related to exploration, ecology, and education. And in November, they launched the Pandemic Response Challenge, which will culminate in a $500,000 award to be split between two teams that not only best predict the continuing global spread of COVID-19, but also prescribe policies to curtail it...

For Phase 1, teams had to submit prediction models by 22 December... Up to 50 teams will make it to Phase 2, where they must submit a prescription model... The top two teams will split half a million dollars. The competition may not end there. Amir Banifatemi, XPrize's chief innovation and growth officer, says a third phase might test models on vaccine deployment prescriptions. And beyond the contest, some cities or countries might put some of the Phase 2 or 3 models into practice, if Banifatemi can find adventurous takers.

The organizers expect a wide variety of solutions. Banifatemi says the field includes teams from AI strongholds such as Stanford, Microsoft, MIT, Oxford, and Quebec's Mila, but one team consists of three women in Tunisia. In all, 104 teams from 28 countries have registered. "We're hoping that this competition can be a springboard for developing solutions for other really big problems as well," Miikkulainen says. Those problems include pandemics, global warming, and challenges in business, education, and healthcare. In this scenario, "humans are still in charge," he emphasizes. "They still decide what they want, and AI gives them the best alternatives from which the decision-makers choose."


But Miikkulainen hopes that data science can help humanity find its way. "Maybe in the future, it's considered irresponsible not to use AI for making these policies," he says.


For the Covid-19 competition, Banifatemi emphasized that one goal was "to make the resulting insights available freely to everyone, in an open-source manner — especially for all those communities that may not have access to data and epidemiology divisions, statisticians, or data scientists."

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