It has been more than two years in the making, but 13 new astronauts have finally joined NASA under the mission that will bring the first female to the moon -and some may be the first humans to step on Mars. The candidates, who have been training since 2017, participated in the first public graduation ceremony for astronauts on Friday at the American space Agency's Johnson Space Center in Houston. The group includes six women and seven men, two of them were Canadian Space Agency (CSA) astronauts, and all were chosen from record-setting pool of more than 18,000 applicants. During the ceremony, each of the bright-eyed graduates were given a silver pin that symbolizes the Mercury 7 – NASA's first astronaut group that was selected in 1959. They will then be awarded a gold pin once they completed their first spaceflights.

Wheat Ridge, Colorado-based Lutheran Medical Center, which is part of Broomfield, Colorado-based SCL Health, wanted to improve its operations. "We determined a few years ago that for a hospital like ours that has a very challenging payer mix, … running an extremely cost-efficient operation was necessary for stability," said Lutheran Medical Center president and CEO Grant Wicklund in a phone interview. "One of the ways we identified we could become even more cost-efficient was to be absolutely world-class at having the appropriate length of stay." Noomi Hirsch, the medical center's vice president of operations, took the lead on the effort. In a phone interview, she explained that the organization was able to hit low-hanging fruit areas, but eventually started looking at options in the technology world to tackle the problem.

By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of non-verbal communication such as pointing and guiding when language communication is unavailable.