The COVID-19 pandemic has both created and exacerbated a series of cascading and interrelated crises whose impacts continue to reverberate. From the immediate effects on people's health to the pressures on healthcare systems and mass unemployment, millions of people are suffering. For many of us who work in the digital technology industry, our first impulse may be to devise technological solutions to what we perceive as the most urgent problems when faced by crises such as these. Although the desire to put our expertise to good use is laudable, technological solutions that fail to consider broader social, political, and economic contexts can have unintended consequences, undermining their efficacy and even harming the very communities that they are intended to help.10 To ensure our contributions achieve their intended results without causing inadvertent harm, we must think carefully about which projects we work on, how we should go about working on them, and with whom such work should be done.

To see the divide between the best artificial intelligence and the mental capabilities of a seven-year-old child, look no further than the popular video game Minecraft. A young human can learn how to find a rare diamond in the game after watching a 10-minute demonstration on YouTube. Artificial intelligence (AI) is nowhere close. But in a unique computing competition ending this month, researchers hope to shrink the gap between machine and child--and in doing so, help to reduce the computing power needed to train AIs. Competitors may take up to four days and use no more than eight million steps to train their AIs to find a diamond.

We don't know how quickly machines will displace people's jobs, or how many they'll take, but we know it's happening -- not just to factory workers but also to money managers, dermatologists and retail workers. The logical response seems to be to educate people differently, so they're prepared to work alongside the robots or do the jobs that machines can't. But how to do that, and whether training can outpace automation, are open questions. Pew Research Center and Elon University surveyed 1,408 people who work in technology and education to find out if they think new schooling will emerge in the next decade to successfully train workers for the future. Two-thirds said yes; the rest said no.

At universities, "people learn how to approach new things, ask questions and find answers, deal with new situations," wrote Uta Russmann, a professor of communications at the FHWien University of Applied Sciences in Vienna. "All this is needed to adjust to ongoing changes in work life. Special skills for a particular job will be learned on the job." Schools will also need to teach traits that machines can't yet easily replicate, like creativity, critical thinking, emotional intelligence, adaptability and collaboration. The problem, many respondents said, is that these are not necessarily easy to teach.

Machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. It's the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognise emotions on people's faces. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Robert Schapire, a principal researcher in Microsoft's New York City research lab, in a new Microsoft blog post by Allison Linn. It's a far cry from Schapire's early days in the field, when he said some of the hard problems were things like getting a computer to accurately read handwritten digits. "Bit by bit, we've really been building this field from the bottom up, starting with basic problems," Schapire said.

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

Intelligent machines won't be ruling the world anytime soon – but what happens when they turn you down for a loan, crash your car or discriminate against you because of your race or gender? On one level, the answer is simple: "It depends," says Bryant Walker Smith, a law professor at the University of South Carolina who specializes in the issues raised by autonomous vehicles. But that opens the door to a far more complex legal debate. "It seems to me that'My Robot Did It' is not an excuse," says Oren Etzioni, CEO of the Seattle-based Allen Institute for Artificial Intelligence, or AI2. The rapidly rising challenges that face America's legal system and policymakers were the focus of today's first-ever White House public workshop on artificial intelligence, presented at the University of Washington School of Law. For a full afternoon, Smith, Etzioni and other experts debated the options in an auditorium that was filled to capacity.

Regardless of the analysis of smartphone market share, Office and cloud revenues, quarterly profits, gaming consoles sales, Surface hardware, or search engine relevance, Microsoft remains a large company employing diversified and talented individuals. Often, the individuals under Microsoft's employ do double duty as the brilliant minds creating projects and industries for the company while also pioneering advancements, groundbreaking achievements, and technologies in fields outside of their immediate obligations. Recently, Yuval Peres and Robert Schapire joined the long list of Microsoft employees and researchers who have been recognized by their peers for their achievements in their respective fields. Peres and Schapire's are receiving perhaps the highest honor in science and research by being elected to the Nation Academy of Sciences. Peres and Schapire's recognition will see them become part of new 84 members and 21 foreign associate groups that will be joining the National Academy this year.