Machine reading comprehension (MRC) is an important area of conversation agents and draws a lot of attention. However, there is a notable limitation to current MRC benchmarks: The labeled answers are mostly either spans extracted from the target corpus or the choices of the given candidates, ignoring the natural aspect of high-quality responses. As a result, MRC models trained on these datasets can not generate human-like responses in real QA scenarios. To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios. Concretely, Penguin consists of 200k training data with high-quality fluent, and well-informed responses. Penguin is the first benchmark towards natural response generation in Chinese MRC on a relatively large scale. To address the challenges in Penguin, we develop two strong baselines: end-to-end and two-stage frameworks. Following that, we further design Prompt-BART: fine-tuning the pre-trained generative language models with a mixture of prefix prompts in Penguin. Extensive experiments validated the effectiveness of this design.

In March 2020, as businesses across the world sent non-essential workers home to slow the spread of the coronavirus, a 2.6 million-sq.-ft. General Motors plant in Kokomo, Ind., sat idle. At the same time, ventilators--the breathing machines essential to keeping critically ill COVID-19 patients alive--were in frighteningly short supply. And so within a week of pausing the plant's operations, GM CEO Mary Barra launched it back into action, quickly transforming a dormant engineering building into an assembly line that delivered 30,000 ventilators in five months. Barra says that approach, incubated in the crisis of the pandemic, is now a permanent cultural shift that has already led to faster timetables for GM's bet-the-company push to sell only electric vehicles by 2035.

Among the key factors driving the economic divide in America is the rise of technology that has eliminated many jobs through automating manufacturing tasks. A new report from the Brookings Institution warns that, thanks to the rise of artificial intelligence, economic disparity between coastal cities and heartland regions is about to get even worse. The 2016 Presidential election served as a wake-up call to the economic effect that the automation of many routine jobs is "massively rearranging the nation's economic geography," says the report, written by Brookings Senior Fellow Mark Muro. "The 2016 election may go down as the first time society began to grasp the full implications of automation's potential to transform the physical world," Muro wrote. "As big, techy cities like New York, Washington, and the Bay Area seemed to increasingly inhabit a different world from the rest of America, the people and places that were'left behind' revolted."