The New York police department (NYPD) announced it will begin using drones to respond to reports of robberies and alerts from a city-wide gunshot detection system. The drones will fly to the scene, piloted by an NYPD officer, and record video and audio that will be sent to police officers' smartphones in real time, according to a press release. The integration of these two surveillance technologies is part of a broader "Drone as First Responder" program that has existed since 2018. The New York city mayor, Eric Adams, and the city's interim police commissioner, Tom Donlan, announced the expansion on Wednesday afternoon. It will be initially rolled out to five precincts in Brooklyn, the Bronx and Manhattan.

In a society where traffic accidents frequently occur, fatigue driving has emerged as a grave issue. Fatigue driving detection technology, especially those based on the YOLOv8 deep learning model, has seen extensive research and application as an effective preventive measure. This paper discusses in depth the methods and technologies utilized in the YOLOv8 model to detect driver fatigue, elaborates on the current research status both domestically and internationally, and systematically introduces the processing methods and algorithm principles for various datasets. This study aims to provide a robust technical solution for preventing and detecting fatigue driving, thereby contributing significantly to reducing traffic accidents and safeguarding lives.

Spotify's royalty model will get a massive revamp next year to give "working artists" a bigger cut, according to Music Business Worldwide. Starting in the first quarter of 2024, Spotify will reportedly implement three changes meant to "combat three drains on the royalty pool." The first one is establishing a minimum number of annual streams a track must reach before it starts generating royalties, which is supposed to demonetize tracks that earn less than 5 cents a month. Apparently, while these tracks make up a tiny percentage of music on the platform -- 99.5 percent of all monetized content will still be earning money after this change -- their royalties still cost Spotify tens of millions of dollars a year. Based on Music Business Worldwide's computations, a track has to generate 200 plays a year to be able to earn 5 cents.

A team of engineering and health researchers has developed a tool that improves the ability of electronic devices to detect when a human patient is coughing, which has applications in health monitoring. The new tool relies on an advanced artificial intelligence (AI) algorithm that helps the AI better identify uncertainty when faced with unexpected data in real-world situations. The paper, "Robust Cough Detection with Out-of-Distribution Detection," is published in the IEEE Journal of Biomedical and Health Informatics. "When AI is being trained to identify the sound of coughing, this is usually done with'clean' data--there is not a lot of background noise or confusing sounds," says Edgar Lobaton, corresponding author of a paper on the work and an associate professor of electrical and computer engineering at North Carolina State University. "But the real world is full of background noise and confusing sounds. So previous cough detection technologies often struggled with'false positives'--they would say that someone was coughing even if nobody was coughing. "We've developed an algorithm that helps us address this problem by allowing an AI to express uncertainty.

Are you searching for AI content detector tools to find AI-written articles? When analyzing material to find whether it was created by human writers or artificial intelligence (AI) writing software, AI content identification technologies can be helpful. Although there are several of these tools available, none are the same. No matter what program was used to create the material, these technologies will aid in its detection as AI-generated. And the most significant thing is that you do not need to be concerned about money because most of them are free. An article may be checked for infringement or to see if it was authored by a human or an artificial intelligence (AI) writer using the program Originality.ai.

Imagine an app on your phone or computer that can tell if you are being dishonest, just by processing affective features of your facial expressions, body movements, and voice. People could ask about your political preferences, your sexual orientation, and immediately determine which of your responses are honest and which are not. In this paper we argue why artificial intelligence-based, non-invasive lie detection technologies are likely to experience a rapid advancement in the coming years, and that it would be irresponsible to wait any longer before discussing its implications. Legal and popular perspectives are reviewed to evaluate the potential for these technologies to cause societal harm. To understand the perspective of a reasonable person, we conducted a survey of 129 individuals, and identified consent and accuracy as the major factors in their decision-making process regarding the use of these technologies. In our analysis, we distinguish two types of lie detection technology, accurate truth metering and accurate thought exposing. We generally find that truth metering is already largely within the scope of existing US federal and state laws, albeit with some notable exceptions. In contrast, we find that current regulation of thought exposing technologies is ambiguous and inadequate to safeguard civil liberties. In order to rectify these shortcomings, we introduce the legal concept of mental trespass and use this concept as the basis for proposed regulation.

Estonia-based Sentinel, which is developing a detection platform for identifying synthesized media (aka deepfakes), has closed a $1.35 million seed round from some seasoned angel investors -- including Jaan Tallinn (Skype), Taavet Hinrikus (TransferWise), Ragnar Sass & Martin Henk (Pipedrive) -- and Baltics early-stage VC firm, United Angels VC. The challenge of building tools to detect deepfakes has been likened to an arms race -- most recently by tech giant Microsoft, which earlier this month launched a detector tool in the hopes of helping pick up disinformation aimed at November's U.S. election. "The fact that [deepfakes are] generated by AI that can continue to learn makes it inevitable that they will beat conventional detection technology," it warned, before suggesting there's still short-term value in trying to debunk malicious fakes with "advanced detection technologies." Sentinel co-founder and CEO Johannes Tammekänd agrees on the arms race point -- which is why its approach to this "goal-post-shifting" problem entails offering multiple layers of defence, following a cybersecurity-style template. He says rival tools -- mentioning Microsoft's detector and another rival, Deeptrace, aka Sensity -- are, by contrast, only relying on "one fancy neural network that tries to detect defects," as he puts it.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Microsoft announced new technology to combat disinformation for the 2020 U.S. presidential election, including deepfakes. The technology can detect manipulated content with the goal of assuring people that the content they're viewing is real. It is part of Microsoft's Defending Democracy Program, targeted at keeping campaigns secure and protecting the voting process.

Microsoft has added to the slowly growing pile of technologies aimed at spotting synthetic media (aka deepfakes) with the launch of a tool for analyzing videos and still photos to generate a manipulation score. The tool, called Video Authenticator, provides what Microsoft calls "a percentage chance, or confidence score" that the media has been artificially manipulated. "In the case of a video, it can provide this percentage in real-time on each frame as the video plays," it writes in a blog post announcing the tech. "It works by detecting the blending boundary of the deepfake and subtle fading or greyscale elements that might not be detectable by the human eye." If a piece of online content looks real but'smells' wrong chances are it's a high tech manipulation trying to pass as real -- perhaps with a malicious intent to misinform people.

Microsoft has launched a new tool to identify'deepfake' photos and videos that have been created to trick people into believing false information online. Deepfakes – also known as synthetic media – are photos, videos or audio files that have been manipulated using AI to show or say something that isn't real. There were at least 96 'foreign influenced' deep fake campaigns on social media targeting people in 30 countries between 2013 and 2019, according to Microsoft. To combat campaigns using this manipulated form of media, the tech giant has launched a new'Video Authenticator' tool that can analyse a still photo or video and provide a percentage chance that the media source has been manipulated. It works by detecting the blending boundary of the deepfake, and subtle fading or greyscale elements that might not be detectable by the human eye.