When the novel coronavirus reached Togo in March, its leaders, like those of many countries, responded with stay-at-home orders to suppress contagion and an economic assistance program to replace lost income. But the way Togo targeted and delivered that aid was in some ways more tech-centric than many larger and richer countries. No one got a paper check in the mail. Instead, Togo's government quickly assembled a system to support its poorest people with mobile cash payments--a technology more established in Africa than in the rich nations supposedly at the forefront of mobile technology. The most recent payments, funded by nonprofit GiveDirectly, were targeted with help from machine learning algorithms, which seek signs of poverty in satellite photos, and cellphone data.

Wanted: 10,000 New Yorkers interested in advancing science by sharing a trove of personal information, from cellphone locations and credit-card swipes to blood samples and life-changing events. Researchers are gearing up to start recruiting participants from across the city next year for a study so sweeping it's called'The Human Project .' It aims to channel different data streams into a river of insight on health, aging, education and many other aspects of human life. Pictured are people walking inside the Oculus, the new transit station at the World Trade Center in New York. Researchers are gearing up to start recruiting 10,000 New Yorkers early next year for a study so sweeping it's called'The Human Project' 'That's what we're all about: putting the holistic picture together,' says project director Dr Paul Glimcher, a New York University neural science, economics and psychology professor.

This is all very promising. Before we can use these new sources of data to inform clinical decision-making, we need to overcome some significant technical challenges. While we have recently seen important advancements in machine learning and artificial intelligence, this work has been largely driven by consumer applications, with Google, IBM, Facebook, Microsoft, and Amazon leading the way. These companies have developed deep learning models that achieve near-human performance on certain tasks, even as their workings are largely incomprehensible to human users. Most people use the Amazon Echo without an understanding of the A.I. algorithms that underlie the speech recognition engine.