Researchers sorted through 38 million tweets from more than 7 million users to better understand how climate change disinformation forms and spreads. Meeting a deaf woman while volunteering at a homeless shelter inspired a student to advocate for including sign language in language technology research. And subterranean search-and-rescue efforts could get a boost from the lessons of a fleet of robots and drones exploring the most remote corners of caves, tunnels and the underground world. The latest issue of The Link, the magazine of Carnegie Mellon University's School of Computer Science, is now online and features these stories and more. The magazine highlights the work of faculty and students from across the school.

Sustainability has become an increasingly key issue in the retail industry over the last few years. While it's promising to hear retailers making the right noises, they won't be able to follow through on their good intentions if they don't use technology to help them to put their words into practice. This is where artificial intelligence (AI) and machine learning (ML) will become increasingly important, as they can drive the change, putting more sustainable behaviour within reach for many retailers. In a nutshell, AI enables businesses to measure their environmental and social impact, while ML helps take the next step by recommending tangible ways to adapt behaviour in line. As a result, AI and ML can help retailers make huge strides towards sustainability in their supply chains, through from transporting products to stores in the most intelligent way possible, to making sure they don't order too much stock.

The use of artificial intelligence (AI) and machine learning to drive innovation across all industries has increased significantly in recent years. Indeed, the proliferation of data science applications from genome sequencing for better disease diagnosis and prevention, to advances in leading edge engineering for autonomous driving, and climate modelling to combat Climate Change, has led to an exponential demand for High Performance Computing (HPC). AI for sustainability is one of the most promising new fields of study, with a recent report by PwC and Microsoft reporting that using AI for environmental applications in four key sectors could reduce global greenhouse gas emissions by 4% in just 10 years' time. Recent efforts include international non-profit organisation, Global Fishing Watch, using AI and satellite data to prevent overfishing, and wind companies using AI to get each turbine's propeller to produce more electricity per rotation by incorporating real time weather and operational data. But alongside worries about AI bias or human jobs being replaced by machines, concerns about the environmental impact of AI itself should be at the fore.

Millennials are projected to soon overtake baby boomers as the largest adult population group, bringing demands for sustainability to the front of every grocery checkout aisle. Fresh retailers, or retailers in the business of providing highly perishable foods like fresh produce and meat to consumers, can strengthen sustainability efforts and combat the 1.3bn tonnes of food wasted annually with the support of artificial intelligence (AI) and machine learning (ML) solutions. AI and ML not only help fresh retailers reduce their environmental footprint through waste reduction, but the technologies allow them to respond to market conditions in real-time and offer more personalised assortments in line with the core values of their key consumers, resulting in more efficient and eco-friendly supply chains. So what does this look like in practice? Food waste is a worldwide issue.

Donnelly Centre researchers have developed a deep learning algorithm that can track proteins, to help reveal what makes cells healthy and what goes wrong in disease. "We can learn so much by looking at images of cells: how does the protein look under normal conditions and do they look different in cells that carry genetic mutations or when we expose cells to drugs or other chemical reagents? People have tried to manually assess what's going on with their data but that takes a lot of time," says Benjamin Grys, a graduate student in molecular genetics and a co-author on the study. Dubbed DeepLoc, the algorithm can recognize patterns in the cell made by proteins better and much faster than the human eye or previous computer vision-based approaches. In the cover story of the latest issue of Molecular Systems Biology, teams led by Professors Brenda Andrews and Charles Boone of the Donnelly Centre and the Department of Molecular Genetics, also describe DeepLoc's ability to process images from other labs, illustrating its potential for wider use.