In media news today, Bill Maher panics about 2024 Democratic presidential prospects, The New York Times says Biden is contributing to his own'political woes' by downplaying inflation, and voters sound off on the economy under Biden. In past years, news anchors, reporters, commentators, and late-night hosts have unabashedly gushed over Republican senators who've broken ranks their party, hailing them as "mavericks" and "heroes" for casting politics aside and voting with their "conscience." However, when Democrats like Kyrsten Sinema, D-Ariz., and Joe Manchin, D-W.Va., buck the party line, the same liberal talking heads criticize and lecture them. Democrat Sens. Manchin and Sinema have come under attack by their own party for bucking President Biden and voting against the Democrat caucus in opposing the efforts to change the Senate's filibuster threshold from 60 votes to 51. The move sparked a full-scale media assault, exposing a stark contrast between the coverage afforded to Republican and Democratic senators who vote out of line with their party.

The breaking ensues whenever AI encounters ambiguity or volatility. And in our hazy, unstable world, that's all the time: Either the data can be interpreted another way or it's obsolesced by new events. At which point, AI finds itself looking at life through errant eyes, seeing left as right or now as yesterday. Yet because AI lacks self-awareness, it doesn't realize that its worldview has cracked. So, on it whirs, unwittingly transmitting the fracture to all the things plugged into it.

When it comes to manufacturing new lightweight, yet strong components for new passenger jets, scientists are treating the process like trying to brew the most delicious cup of coffee. By using artificial intelligence (AI) and machine learning, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory are intelligently and automatically selecting the perfect settings for a different kind of hot brew -- the process of friction stir welding, a common ingredient needed to manufacture airplane components. In a new collaboration with GE Research, Edison Welding Institute and GKN Aerospace, Argonne computer scientists are putting the power of the laboratory's automated machine learning expertise and supercomputers to use. By reducing the number of costly experiments and time-consuming simulations with a new machine learning approach, they can generate accurate models that provide valuable information about the welding process in much less time and at a fraction of the cost. This approach, called DeepHyper, is a scalable automated machine learning package developed by Argonne computational scientist Prasanna Balaprakash and his colleagues at Argonne.

Dupin's pinned a homicide on you." Special Agent Dinah Carter and I had worked on only a couple of cases as partners in the FBI. Now she was programming me for survival mode, to elude the bureau's AI-based crime-solving system, Dupin. "Lose your phone," Dinah told me. Dupin had named me prime suspect in a crime that had occurred just moments before and miles away because I had guessed that the AI was fabricating evidence. I didn't realize then how soon I'd be declared dead. I took the emergency stairs to street level, dropping my cell phone behind a fire hose. With my hood up and watching out for security cameras, I headed onto 10th Street Northwest. When I gave the barista cash for my half-hour online and thimbleful of espresso, he looked at me as if I was something he needed to wipe off his shoe. That left me with a dollar and some loose change. I sat down at a screen and pulled out the AI Primer that Dinah had given me. In the back were the author's details--Professor Francesca Adriaco from Georgetown University. If I had any chance of surviving this, I needed her help. I need to speak with you urgently about artificial intelligence. I guess she had a system monitoring her emails; she replied in minutes and invited me to her office. If I ever survived this, I needed to exercise more. "Hey, Saskia, good to meet you.

How to fit the history of computing into a book that can be picked up without needing a forklift truck? That was my challenge in writing A New History of Modern Computing5 (hereafter the "new history") with Paul Ceruzzi. Now we had to tell the story of billions of them, drawing on the work of an ever-expanding research community to help us find a story hiding among all the model numbers. I should be clear up front that this is an academic history of computing. Trade books are the ones that get stocked in bookstores, reviewed in newspapers, and so on. Their editors will select and rewrite manuscripts with a mass audience in mind. Trade publishers appear to have decided, perhaps correctly, the only way to sell books on the history of computing is to stuff them with people and stories that readers already know about while nevertheless insisting they are tragically forgotten. Their books feature a lot of Charles Babbage, Alan Turing, and other "geniuses." They obsess over the question of the "first computer" and spend a lot of time in the 1940s laboriously weighing evidence for the primacy of one invention or another before awarding the crown.

According to the World Health Organization (WHO), the world is going hungry. WHO data shows that in 2018, the most recent year for which data is available, 820 million people lacked enough food to eat, an increase of nine million people over the year before. Hunger kills plenty of people worldwide. It also impacts those who survive, causing serious childhood development issues like stunting, where children are too short for their age, and wasting, where they're too thin for their age. The explosion in our planet's population is a major factor in there not being enough food to go around.

Neil Savage is a science and technology writer based in Lowell, MA, USA.

Husain claims that Darwin can uncover problems like missing data while suggesting solutions to problems in an AI training dataset, such as malformed or missing data. Darwin can also ostensibly deliver "explainable" model results that spotlight important aspects of a dataset, he says. On the cybersecurity side, SparkCognition offers DeepArmor, which leverages AI to attempt to mitigate executable-based cyberattacks. Meanwhile, the company's DeepNLP service automates workflows of unstructured data to simplify tasks like information retrieval, document classification, and analytics. SparkCognition's SparkPredict and Ensemble are AI-powered asset management and predictive maintenance platforms, built to detect suboptimal production yields and equipment failures proactively.

Artificial intelligence has infiltrated practically every civilian industry imaginable. It has changed the way people and businesses work, and it is now swiftly becoming a necessary component of modern combat. One of the criteria that determines how powerful a country is the strength of its army. When compared to other parts, investment in this industry is the largest in some of the most developed countries. A significant portion of this investment is dedicated to rigorous research and development in current technologies, such as artificial intelligence (AI) in military applications.

In 2021, the UK has found itself under the bright lights of the world stage many times. A global audience has watched our pandemic response, the fruition of BREXIT and, most recently, the UK's COP26 presidency. So, why has the UK's AI and wider tech scene still not made it close to global superpower status that we see from China, Russia and the US? The results of the government's recent National AI Strategy are yet to be seen, but I predict there is deeper change needed. A thriving AI industry needs a combination of education, ambition, and nurtured innovation.