NewsBreak, a popular free news app in the US, has been publishing fictitious stories written by AI since 2021, according to Reuters. The app publishes licensed content from legitimate news sources, such as CNN, AP and Reuters itself, but it also uses artificial intelligence tools to rewrite press releases and local news. One of the most egregious examples of a false news story by NewsBreak was published on Christmas Eve last year. The app's writeup claimed that there was a shooting in Bridgeton, New Jersey when no such incident took place. New Jersey's police department dismissed the claims made in the article before the app, which said it got the information from another website, took it down four days later.

Last Christmas Eve, NewsBreak, a free app with roots in China that is the most downloaded news app in the United States, published an alarming piece about a small town shooting. It was headlined "Christmas Day Tragedy Strikes Bridgeton, New Jersey Amid Rising Gun Violence in Small Towns." The problem was, no such shooting took place. The Bridgeton police department posted a statement on Facebook on Dec. 27 dismissing the article -- produced using AI technology -- as "entirely false."

Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI). There is a wide range of strategies that can be employed to make progress on this challenge. This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions. The basic idea is that there are several types of commonsense reasoning: one is manifested at the logical level of physical actions, the other deals with the understanding of the essence of human-human interactions. Existing approaches, based on formal logic and artificial neural networks, allow for modeling only the first type of common sense. To model the second type, it is vital to understand the motives and rules of human behavior. This model is based on real-life heuristics, i.e., the rules of thumb, developed through knowledge and experience of different generations. Such knowledge base allows for development of an expert system with inference and explanatory mechanisms (commonsense reasoning algorithms and personal models). Algorithms provide tools for a situation analysis, while personal models make it possible to identify personality traits. The system so designed should perform the function of amplified intelligence for interactions, including human-machine.

A novel machine learning model could help predict mortality and neurological outcomes post-cardiac arrest, according to a new Johns Hopkins study. Presented at the Society of Critical Care Medicine's 49th Annual Critical Care Congress in Orlando, FL, study results indicate the new model demonstrated significantly improved prediction capabilities compared to the reference APACHE model. "The objectives of our study were to first predict the neurological outcome and mortality at discharge using data only from the first 24 hours of ICU admission and the second objective was to determine whether utilizing physiologic time series (PTS) data, specifically just features from the bedside monitoring data, are useful in terms of model performance," said lead investigator Hanbiehn Kim, MBE, of Johns Hopkins University, during his presentation. Using the Philips eICU database, which includes over 200,000 patients from 208 hospitals, Kim and colleagues from Johns Hopkins Hospital extracted data on cardiac arrest patients who were mechanically ventilated. Of note, this database includes PTS data from patient bedside bio-monitors that recorded heart rate, oxygen saturation, blood pressure, and respiratory rate at 5-minute intervals.