The 2020s have been witnessing a very significant advance in the development of generative artificial intelligence tools, including text generation systems based on large language models. These tools have been increasingly used to generate texts in the most diverse domains -- from technical texts to literary texts --, which might eventually lead to a lower volume of written text production by humans. This article discusses the possibility of a future in which human beings will have lost or significantly decreased their ability to write due to the outsourcing of this activity to machines. This possibility parallels the loss of the ability to write in other moments of human history, such as during the so-called Greek Dark Ages (approx. 1200 BCE - 800 BCE).

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It is vital that we develop appropriate statistical models to represent and extract valuable insights from these large datasets, since the bridges constitute critical infrastructure within modern transportation networks. The process of monitoring engineered systems via streaming data is typically referred to as Structural Health Monitoring (SHM) and while successful applications have been emerging in recent years, a number of challenges remain for practical implementation [5]. During model design, these concerns usually centre around low variance data: that is, measurements are not available for the entire range of expected operational, environmental, and damage conditions. Consider a bridge following construction, this will have a relatively small dataset that should only be associated with normal operation. On the other hand, a structure with historical data might still not experience low-probability events - such as extreme weather or landslides. An obvious solution considers sharing data (or information) between structures; this has been the focus of a large body of recent work [6-8].

The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision support systems. We adapted a boosted Statistical Relational Learning (SRL) framework to learn probabilistic rules from clinical hospital records for the management of physiologic parameters of children with severe cardiac or respiratory failure who were managed with extracorporeal membrane oxygenation. In this preliminary study, the results were promising. In particular, the algorithm returned logic rules for medical actions that are consistent with medical reasoning.

The British glove manufacturer Cornelia James is often in the public eye. Since 1979, it has provided gloves by royal appointment to the Queen of England, and more recently it has dressed megastars including Rihanna, Taylor Swift and Madonna. All those high-profile customers are great for publicity, but nobody drives sales quite like Kate Middleton, said Genevieve James, the company's creative director. "We get far more of a spike in sales when someone like Kate wears them. But when it's somebody like the Duchess of Cambridge, the public, particularly in the US, can relate to that," James told IBM. James first confirmed that fact in 2012 when, at a ceremony for Remembrance Sunday, Middleton wore a pair of Cornelia James' merino wool gloves.

"The sensationalist coverage these stories receive isn't very helpful and obscures the main issues," author Chris Middleton told Sputnik. "We're not talking about C-3P0 sitting at your desk, we're talking about smart devices, industrial machines, drones, autonomous vehicles, plus software that automates technology." Accountancy firm PwC analysed 200,000 jobs across 29 countries and suggests the first wave of job losses will begin in the early 2020s. "When you look at financial services, a lot of jobs are relatively routine jobs such as data analysis," said PwC chief economist John Hawksworth who suggests the closure of bank branches is indicative of the current situation. Around six and eight percent of positions in the financial sector could be lost because "a lot of jobs are relatively routine jobs such as data analysis," he said.

Our entire bodies could be swapped out with robotic parts as soon as 2070, says robotics journalist and expert Chris Middleton. He says we're not far from a future where anyone can buy upgraded body parts that provide superhuman powers. 'Biohackers' are already upgrading their bodies with implants such as chips that let them open doors with a wave of the hand, so the predictions aren't too far-fetched. Our entire body could be swapped out with robot parts as soon as 2070, says robotics journalist and expert Chris Middleton. He says we're not far from a future where anyone can buy upgraded body parts that provide superhuman powers (stock image) 'At some point, 50 or 100 years in the future, might a whole human body become replaceable, editable or upgradable?

Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. Researchers at the University of Edinburgh's Roslin Institute used software that compares genetic information from bacterial samples isolated from both animals and people. The software learns the DNA signatures that are associated with E. coli samples that have caused outbreaks of infection in people. It can then pick out the animal strains that have these signatures, which are therefore likely to be a threat to human health.