However, a new study has described how its status in science fact could actually be employed as another, and perhaps unlikely, form of entertainment -- live music. Dr Alexis Kirke, Senior Research Fellow in the Interdisciplinary Centre for Computer Music Research at the University of Plymouth (UK), has for the first time shown that a human musician can communicate directly with a quantum computer via teleportation. The result is a high-tech jamming session, through which a blend of live human and computer-generated sounds come together to create a unique performance piece. Speaking about the study, published in the current issue of the Journal of New Music Research, Dr Kirke said: "The world is racing to build the first practical and powerful quantum computers, and whoever succeeds first will have a scientific and military advantage because of the extreme computing power of these machines. This research shows for the first time that this much-vaunted advantage can also be helpful in the world of making and performing music. No other work has shown this previously in the arts, and it demonstrates that quantum power is something everyone can appreciate and enjoy."

Audio content consumption has grown exponentially in the past few years. Statista reports that podcast ad revenue will exceed a billion dollars in 2021. For the publishing industry and content providers, providing audio as an alternative option to reading could improve engagement with users and be an incremental revenue stream. Given the shift in customer trends to audio consumption, Amazon Polly launched a new speaking style focusing on the publishing industry: the Newscaster speaking style. This post discusses how the Newscaster voice was built and how you can use the Newscaster voice with your content in a few simple steps.

A network of fictional journalists, analysts, and political consultants has been used to place opinion pieces favorable to certain Gulf states in a range of media outlets, an investigation from The Daily Beast has revealed. At least 19 fake personas were used to author op-eds published in dozens of mainly conservative publications, with AI-generated headshots of would-be authors used to trick targets into believing the writers were real people. It's not the first time AI has been used in this way, though it's unusual to see machine learning tech deployed for online misinformation in the wild. Last year, a report from The Associated Press found a fake profile on LinkedIn, part of a network of likely spies trying to make connections with professional targets, that also used an AI-generated headshot. AI-generated profile pictures created by sites like ThisPersonDoesNotExist.com have some unique advantages when it comes to building fake online personas.

A customer checks an iPhone 7 inside the Apple store Saint-Germain during the first opening day on December 03, 2016 in Paris, France - file photo. At this point, digital privacy is long gone. There's always another device, feature or service tracking what we say, what we look at online and the places we go. Some devices are more intrusive than others, and you may be feeding digital assistants more information than you realize. Tap or click here to stop all the smart tech in your home from listening.

… Partner to Bring Artificial Intelligence-Powered Contract Management to the … ContractPodAi offers customers intelligent AI functionality, built on the …

Three forums were held in Korea, Singapore and Germany prior to the official kickoff of the World Artificial Intelligence Conference 2020 on Thursday, …

What comes to mind when you think of Artificial Intelligence (AI)? Maybe you think of robots taking over the world, like in the movies, or self-driving cars. Merriam-Webster defines artificial intelligence as 1) a branch of computer science dealing with the simulation of intelligent behavior in computers and 2) the capability of a machine to imitate intelligent human behavior. Did you know that AI is gradually changing the way consumers shop at various stages of the buyer's journey? In subtle ways, artificial intelligence affects the way a potential buyer searches for product or brand information during the awareness stage.

We know that Artificial Intelligence (AI) is the main force moving the society into the future described in the movies in the past couple of decades. There are new heights achieved every day in different fields using Artificial Intelligence methods. Artificial Intelligence is a huge field and contains a lot of subfields, that contain a lot of subfields themselves. There are huge amounts of sources that claim that can help you learn Artificial Intelligence. These sources come in different forms and types, from books, blogs, projects, videos, etc. Today we are going to talk about the video sources, more precisely YouTube videos.

Okay so I'm gonna get out front that while I've studied artificial neural networks, I don't know a whole lot about them. So I hope that these questions are not extremely noobish. I know a little about activation functions, which are (as I understand) really important in most or all forms of neural networks, as they introduce a meaningful non-linearity into the network. Otherwise the network would just be one complicated linear function. And there are several common activation functions like rectified linear, softplus, and sigmoid.

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference. To address these issues, we incorporate both word-oriented and entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization to align the word-level and entity-level semantic spaces. Based on the aligned semantic representations, we further develop a KG-enhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive experiments have demonstrated the effectiveness of our approach in yielding better performance on both recommendation and conversation tasks.