Machu Picchu, the remains of a 15th Century Inca city, is Peru's most popular tourist destination, and a Unesco world heritage site. Yet a continuing dispute over the buses that take visitors up to the mountain-top site recently saw some 1,400 stranded tourists needing to be evacuated. Cristian Alberto Caballero Chacón is head of operations for bus company Consettur, which for the past 30 years has transported some 4,500 people every day to Machu Picchu from the local town of Aguas Calientes. It is a 20-minute journey, and the only alternative is an arduous, steep, two-hour walk. He admits that in the past few months there have been some conflicts between people from different communities here.

OpenAI has released the best automatic speech recognition model yet: Whisper is open source and could hold the key to GPT-4.

Greyparrot, a start-up which uses computer vision for waste management, has been voted the winner of the Innovation Factory Grand Finale held as part of the year-round AI for Good Summit 2020. The Innovation Factory is AI for Good's platform to showcase startups which use artificial intelligence to tackle global challenges, providing them with feedback, mentorship and potential partnerships in social impact entrepreneurship. Greyparrot and three other start-ups received the highest scores for their innovative, scalable AI solutions for waste management, air quality, child malnutrition and agriculture. Meet the expert jury During the live Innovation Factory Grand Finale, these four startups recognized as Innovation Champions presented their solutions to a jury of experts and a public audience who then voted for a winner. Greyparrot seeks to resolve the waste crisis by using AI-based computer vision to provide actionable insights for the 530 billion-dollar global waste management industry.

In the service of business, of society at large, artificial intelligence (AI) can be effective. Can it also be ethical? The wisdom of crowds, gleaned from social media, can paint a gestalt picture of how a government agency's, bank's or retailer's efforts are being received on the ground, so to speak. And it can also (perhaps), fed through models and analytics, can bolster decision-making for the greater, common good. Public opinion matters, after all, but across the social media platforms, the chatrooms -- the chatbots, even -- making sense of qualitative data is a challenge for most enterprises.

In the service of business, of society at large, artificial intelligence (AI) can be effective. Can it also be ethical? The wisdom of crowds, gleaned from social media, can paint a gestalt picture of how a government agency's, bank's or retailer's efforts are being received on the ground, so to speak. And it can also (perhaps), fed through models and analytics, can bolster decision-making for the greater, common good. Public opinion matters, after all, but across the social media platforms, the chatrooms -- the chatbots, even -- making sense of qualitative data is a challenge for most enterprises.

We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps. The networks in $\Sigma$-net are trained in a supervised way, including content and GAN losses, and with various ways of data consistency, i.e., proximal mappings, gradient descent and variable splitting. A semi-supervised finetuning scheme allows us to adapt to the k-space data at test time, which, however, decreases the quantitative metrics, although generating the visually most textured and sharp images. For this challenge, we focused on robust and high SSIM scores, which we achieved by ensembling all models to a $\Sigma$-net.