Imagine walking into work, feeling the hum of the office around you and settling into your desk, all the while unaware of the unseen eyes monitoring your every move. It's happening on a grand scale as corporations use advanced software to keep tabs on their employees. Experts fear that this extensive data collection could be a stepping stone, a way to train AI to replace human roles in the workforce. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER It's a familiar story – the experienced worker trains the newbie, only to be replaced once the newcomer is up to speed. This age-old tale is on the verge of adding a new character to its plot: artificial intelligence.

For years now, Hakwan Lau has suffered from an inner torment. Lau is a neuroscientist who studies the sense of awareness that all of us experience during our every waking moment. How this awareness arises from ordinary matter is an ancient mystery. Several scientific theories purport to explain it, and Lau feels that one of them, called integrated information theory (IIT), has received a disproportionate amount of media attention. He's annoyed that its proponents tout it as the dominant theory in the press.

It's impossible to avoid Elvis Presley in Las Vegas: his image appears on street art and photographs, while impersonators can be found all over the strip. But starting on Friday, the King of Rock and Roll will get possibly his largest tribute yet: a video collage that renders him hundreds of times, projected hundreds of feet into the air, in incarnations young and old, gyrating and reclining, in bas relief and gold, all thanks to a technology created long after his death: generative AI. The video collage is the creation of the artist Marco Brambilla, the director of Demolition Man and Kanye West's "Power" music video, among many other art projects. Brambilla fed hours of footage from Presley's movies and performances into the AI model Stable Diffusion to create an easily searchable library to pull from, and then created surreal new images by prompting the AI model Midjourney with questions like: "What would Elvis look like if he were sculpted by the artist who made the Statue of Liberty?" The kaleidoscopic result, called "King Size," will make its debut as part of U2's concert performance at the opening night of the Sphere, a $2.3 billion entertainment venue that sits a block from the Las Vegas Strip and hopes to be the city's latest colossal entertainment mecca.

The 2023 AI Song Contest will take place on 4 November in A Coruña, Galicia (Spain). This international competition was inspired by the Eurovision Song Contest, and provides a platform for exploring the use of artificial intelligence in the songwriting process. A total of 35 songs have been submitted to the contest this year, and you can listen to them all here. The winner will be decided on the basis of votes from both the public and an expert panel of judges, and will be announced at the live event on 4 November.

There are countless science-fiction movies about humanoid robots, but so far robot actors are yet to step up and star in their own films. Luckily for human actors, that future may still be far away as'the world's most advanced' humanoid robot shows off its acting'skills' in this uncanny clip. Ameca, a product of the engineers at Cornwall-based startup Engineered Arts, was asked to provide an impression from a film. 'All those moments will be lost in time, like tears in rain', Ameca said, quoting Blade Runner as it moved through a series of human-like expressions. In the background, a faint movie soundtrack could even be heard playing, adding some much-needed drama to the robot's delivery.

The "America's Got Talent" judge tells Fox News Digital why he doesn't like AI technology in songwriting. Country music legend Lee Greenwood knows the importance of creating from the heart. The "God Bless the USA" singer has more than half a century of experience in the entertainment world, with dozens of hit songs and albums under his belt. When it comes to artificial intelligence and figuring out if AI has a place in the ever-changing landscape of the music industry, Greenwood took a cue from the past. "I approach this just like when guitar players first got a wah-wah pedal and the chorus on an organ – it's like, it's kind of a new thing," he told Fox News Digital.

ChatGPT has proven it can help students with their homework, but now it is helping teachers create those very courses, a computer science professor told Fox News. The popular artifical intelligence platform ChatGPT will now be able to respond to spoken words and images, causing concern among some experts who believe the application could lead to unwanted invasions of privacy. OpenAI, the company behind ChatGPT, released the new version of the chatbot on Monday, allowing it for the first time to interact with users with the spoken word, according to a report from the New York Times. "We're looking to make ChatGPT easier to use – and more helpful," Peter Deng, OpenAI's vice president of consumer and enterprise product, told the New York Times. GOOGLE'S AI IS TRYING TO ONE-UP CHATGPT AND BING WITH NEW EVERYDAY AI FEATURES Microsoft Bing Chat and ChatGPT AI chat applications are seen on a mobile device.

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe.

Universal source separation targets at separating the audio sources of an arbitrary mix, removing the constraint to operate on a specific domain like speech or music. Yet, the potential of universal source separation is limited because most existing works focus on mixes with predominantly sound events, and small training datasets also limit its potential for supervised learning. Here, we study a single general audio source separation (GASS) model trained to separate speech, music, and sound events in a supervised fashion with a large-scale dataset. We assess GASS models on a diverse set of tasks. Our strong in-distribution results show the feasibility of GASS models, and the competitive out-of-distribution performance in sound event and speech separation shows its generalization abilities. Yet, it is challenging for GASS models to generalize for separating out-of-distribution cinematic and music content. We also fine-tune GASS models on each dataset and consistently outperform the ones without pre-training. All fine-tuned models (except the music separation one) obtain state-of-the-art results in their respective benchmarks.

Digital audio effects are widely used by audio engineers to alter the acoustic and temporal qualities of audio data. However, these effects can have a large number of parameters which can make them difficult to learn for beginners and hamper creativity for professionals. Recently, there have been a number of efforts to employ progress in deep learning to acquire the low-level parameter configurations of audio effects by minimising an objective function between an input and reference track, commonly referred to as style transfer. However, current approaches use inflexible black-box techniques or require that the effects under consideration are implemented in an auto-differentiation framework. In this work, we propose a deep learning approach to audio production style matching which can be used with effects implemented in some of the most widely used frameworks, requiring only that the parameters under consideration have a continuous domain. Further, our method includes style matching for various classes of effects, many of which are difficult or impossible to be approximated closely using differentiable functions. We show that our audio embedding approach creates logical encodings of timbral information, which can be used for a number of downstream tasks. Further, we perform a listening test which demonstrates that our approach is able to convincingly style match a multi-band compressor effect.