Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid to compute for randomized low-rank approximation algorithms such as the randomized SVD and Nystr\"om, and they provide useful information that can be used to assess the quality of the computed output and guide algorithmic parameter choices.

As businesses, consumers and government agencies look for ways to take advantage of artificial intelligence tools, experts this week called on Congress to craft AI regulations addressing challenges facing the technology. AI concerns run the gamut from bias in algorithms that could affect decisions such as who is selected for housing and employment opportunities, to the use of deep fake AI that can artificially generate images and sounds that can imitate real human beings' appearances and voices. Yet AI has also led to the development of lifesaving drugs, advanced manufacturing and self-driving cars. Indeed, the increased adoption of artificial intelligence has led to the rapid growth of advanced technology in "virtually every sector," said Sen. Gary Peters (D-Mich.), chairman of the U.S. Senate Committee on Homeland Security and Governmental Affairs. Peters spoke during a committee hearing on AI risks and opportunities Wednesday.

Researchers from New York University surveyed professionals in the field of Natural Language Processing to gather opinions on the current state of AI and what the types of ethical concerns they may have for it in the future. Not only did the respondents conclude that AI could overturn our current society, they also predicted that its mishandling could result in something as catastrophic as nuclear war. And perhaps the most interesting part of this is the fact that the survey was conducted last May -- before the infamous ChatGPT was even in the public eye. Out of 480 researchers polled, a full 73% said that AI as an automation in the workforce could lead to "revolutionary societal change" during this century. They compared its impact as that of the Industrial Revolution when steam power and communications advancements completely changed the world.

Identifying healthcare fraud, waste and abuse is a highly evolved practice that is best done with a marriage of artificial intelligence (AI) and human intelligence (HI) capabilities. As the losses attributed to fraud continues to grow, unfortunately, we all share the responsibility of paying for it. The National Health Care Anti-Fraud Association (NHCAA) estimates that the financial losses due to health care fraud are in the tens of billions of dollars each year.1 The payment integrity review process of analyzing a healthcare claim can be strengthened by implementing a hybrid approach of both HI and AI. However, it's important to understand the benefits and limitations of each to avoid pitfalls that can arise. On July 20, 2022, the Department of Justice announced criminal charges against 36 defendants in 13 federal districts across the United States for more than $1.2 billion in alleged fraudulent telemedicine, cardiovascular and cancer genetic testing, and durable medical equipment (DME) schemes.

Generative AI's two-edged sword - more information made available. Well before the birth of the internet, I had become an infomaniac. I devoured tech magazines and books on tech to try and stay up to date on current trends and issues related to my job as a tech analyst. I am also a techie at heart, and my first tech-related job in 1975 was semiconductor-related. I would also go to specialized tech user groups and mini-conferences as my interest in tech evolved and helped me when I joined Creative Strategies in 1982 as their first PC analyst.

Self-driving cars, also known as autonomous vehicles, have been a topic of interest for many decades. The idea of a car that can drive itself has been a futuristic dream for many people, but it wasn't until recently that the technology to make it a reality began to develop. In the early 2000s, self-driving cars were still in the experimental stages, with most of the focus being on developing sensors and software that could accurately detect and respond to road conditions. One of the first notable examples of self-driving technology came from the Defense Advanced Research Projects Agency (DARPA) in the United States, which held a series of competitions in which teams of engineers and computer scientists would create autonomous vehicles that could navigate a set course. By the mid-2010s, self-driving technology had advanced significantly, with companies like Google, Tesla, and Uber investing heavily in the development of autonomous vehicles.

This week, WIRED debuted its joint investigation with Lighthouse Reports into the questions of bias and equity that are inherent in governments' use of algorithms to oversee financial assistance programs and identify alleged welfare fraud. The investigation included an unprecedented look inside the system used by the city of Rotterdam, in the Netherlands, and the training data that was used on the algorithm. We looked closely at how flaws in the algorithm's conclusions and wrongful accusations have impacted people's lives in Rotterdam. And we examined the global role of the private fraud-detection industry in these systems as well as urgent concerns about the pervasive surveillance that is now inherent in Denmark's national welfare scheme. The United States FBI admitted for the first time this week that it has purchased location data about people in the US from private data brokers rather than obtaining a warrant for the information.

Cybercriminals are turning to ChatGPT to generate extremely convincing phishing emails, researchers have warned - so how can internet users spot the scams? Cybersecurity company Norton warned that criminals are turning to AI tools such as ChatGPT to create'lures' to rob victims. A report in New Scientist suggested that using ChatGPT to generate emails could cut costs for cybercrime gangs by up to 96 percent. ChatGPT also completely removes the language barrier for cybercriminal gangs around the world, warns Julia O'Toole, CEO of MyCena Security Solutions. O'Toole said there are still ways to spot scam emails generated by AI tools, but the technology makes it far more difficult to spot scam emails.

In recent years, the term "deepfake" has become increasingly popular in the tech world, with cybersecurity experts warning of its growing threat. Deepfakes are realistic images, audio or video content created with the help of machine learning and artificial intelligence techniques that manipulate or replace existing content to create convincing but false media. Deepfake technology can be used for a wide range of purposes, from harmless pranks to malicious activities such as fraud, blackmail, propaganda, or cyberbullying. In this article, we will explore the concept of deepfake, why and how it poses a threat to people, and the pros and cons of deepfake in terms of cybersecurity. Deepfake is a portmanteau of the words "deep learning" and "fake." It is a type of synthetic media that is created using deep learning algorithms, specifically generative adversarial networks (GANs), which learn to generate new data that is similar to existing data.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Belarusian government is struggling to thwart an internal guerrilla group that opposes Minsk's assistance to Russia and has engaged in a sabotage campaign since the war in Ukraine kicked off. Earlier this week, opposition activists from the Association of Security Forces of Belarus (BYPOL), a group formed following the 2020 political turmoil in Belarus, attacked a Russian warplane outside the capital city using drones. "Belarusians will not allow the Russians to freely use our territory for the war with Ukraine, and we want to force them to leave," one retired Belarusian serviceman, who joined a group of saboteurs and goes by the name Anton, told The Associated Press in a report Friday.