Ancient rock carvings have been uncovered near the Amazon River amid drought conditions in Brazil. A set of ancient texts burned by the volcanic eruption on Mount Vesuvius in 79 A.D. have been deciphered thanks to a team of researchers using AI. The nearly 2,000-year-old texts were unreadable after being charred in a villa in Herculaneum, a Roman town near Pompeii. The texts were discovered in an ancient villa in the town of Herculaneum. Believed to have been owned by the father-in-law of Julius Caesar, the texts were carbonized by the heat of the volcanic debris.

A robot manipulating objects while, say, working in a kitchen, will benefit from understanding which items are composed of the same materials. With this knowledge, the robot would know to exert a similar amount of force whether it picks up a small pat of butter from a shadowy corner of the counter or an entire stick from inside the brightly lit fridge. Identifying objects in a scene that are composed of the same material, known as material selection, is an especially challenging problem for machines because a material's appearance can vary drastically based on the shape of the object or lighting conditions. Scientists at MIT and Adobe Research have taken a step toward solving this challenge. They developed a technique that can identify all pixels in an image representing a given material, which is shown in a pixel selected by the user. The method is accurate even when objects have varying shapes and sizes, and the machine-learning model they developed isn't tricked by shadows or lighting conditions that can make the same material appear different.

Fox News' Eben Brown reports on how more companies are using A.I. technology to set retail prices based on data-driven supply-and-demand. A new artificial intelligence (AI) tool could help African countries better track and predict crop rotations and yields, providing a key tool to help mitigate food crisis across the continent. "Relying on conventional analytic techniques alone will not deliver the effective decision-making we need to meet these challenges," Racine Ly, the director of data management for the project, told Science X. "Since this is data that researchers and decision-makers most importantly will use to make decisions, we needed to make sure that the data is correct, and the predictions are accurate," he added. AKADEMIYA2063, a research organization, said the Africa Agriculture Watch (AAgWa) tool will help prioritize and maximize the production of staple foods such as maize, cassava and sorghum. WILL AI EVER BE SMART ENOUGH TO DECIPHER FEDERAL REGULATIONS?

This study was a peek into the internal processes of diffusion models, the researchers concluded, saying that the study was the first to provide a quantitative interpretation of the model from a biological perspective. For example, there is a diagram that the researchers created showing the correlation between stimuli and noise levels in the brain. The higher-level the stimuli, the higher-level the noise level would be, and the higher resolution the image would be. In another diagram, the researchers show the engagement of different neural networks in the brain and how it would de-noise an image to reconstruct it.

Any overreliance on foreign inputs in drug supply chains could leave the U.S. open to dire shortages in the event of conflict or natural catastrophe. The White House has flagged the potential disruption of the pharmaceutical supply chain as a national-security issue, saying these drugs are essential to the health and prosperity of the country. "The ever-changing threat environment, both natural and man-made, gives rise to numerous unforeseen challenges, such as to the pharmaceutical supply chain," said Jennifer Foley, a deputy director in DHS's science and technology directorate. Quantifind Inc., a company that normally does risk screening for financial institutions, will do the work, looking into supply chains for the Cross-Border Threat Screening and Supply Chain Defense Center of Excellence, a government-backed research center connected with Texas A&M University. Our Morning Risk Report features insights and news on governance, risk and compliance.

For once, algorithms that predict crime might be used to uncover bias in policing, instead of reinforcing it. A group of social and data scientists developed a machine learning tool it hoped would better predict crime. The scientists say they succeeded, but their work also revealed inferior police protection in poorer neighborhoods in eight major U.S. cities, including Los Angeles. Instead of justifying more aggressive policing in those areas, however, the hope is the technology will lead to "changes in policy that result in more equitable, need-based resource allocation," including sending officials other than law enforcement to certain kinds of calls, according to a report published Thursday in the journal Nature Human Behavior. The tool, developed by a team led by University of Chicago professor Ishanu Chattopadhyay, forecasts crime by spotting patterns amid vast amounts of public data on property crimes and crimes of violence, learning from the data as it goes.

In a new study, Florida State University researchers explore the challenges of recruiting and retaining older adults to participate in research. The study also marks the first step of a broad, interdisciplinary FSU effort to increasingly use artificial intelligence in research. In the study, published in The Gerontologist, Associate Professor of Sociology Dawn Carr identified core "motivation clusters" among older adults for research participation. Along with her 12 FSU-based co-authors, Carr suggests that identifying those clusters--"fun seekers" and "research helpers," for example--can guide recruitment and retention strategies. "There is a lack of representation of older adults in research that leads to findings that are skewed," Carr said.

Using artificial intelligence (AI) researchers have found that between 2007 and 2016 online sentiments around climate change were uniform, but this was not the case with vaccination. Climate change and vaccinations might share many of the same social and environmental elements, but that doesn't mean the debates are divided along the same demographics. A research team from the University of Waterloo and the University of Guelph trained a machine-learning algorithm to analyze a massive number of tweets about climate change and vaccination. The researchers found that climate change sentiment was overwhelmingly on the pro side of those that believe climate change is because of human activity and requires action. There was also a significant amount of interaction between users with opposite sentiments about climate change.

Using artificial intelligence (AI) researchers have found that between 2007 and 2016 online sentiments around climate change were uniform, but this was not the case with vaccination. Climate change and vaccinations might share many of the same social and environmental elements, but that doesn't mean the debates are divided along the same demographics. A research team from the University of Waterloo and the University of Guelph trained a machine-learning algorithm to analyze a massive number of tweets about climate change and vaccination. The researchers found that climate change sentiment was overwhelmingly on the pro side of those that believe climate change is because of human activity and requires action. There was also a significant amount of interaction between users with opposite sentiments about climate change.

Researchers are using artificial intelligence (AI) to detect behavioural signs of anxiety with more than 90 per cent accuracy, and suggest that AI could have future applications for addressing mental health and wellbeing. Their research is published in the journal Pervasive and Mobile Computing. "In the two years since the onset of COVID-19, and one climate disaster after another, more and more people are experiencing anxiety," says Simon Fraser University visiting professor and social psychologist Gulnaz Anjum. "Our research appears to show that AI could provide a highly reliable measurement for recognizing the signs that someone is anxious." Anjum and collaborators Nida Saddaf Khan and Sayeed Ghani from the Institute of Business Administration in Karachi, Pakistan collected an extensive range of data from adult participants for their Human Activity Recognition (HAR) study.