New media can be defined as a highly interactive digital technology which allows people to interact anywhere anytime. This has evolved as a non-tangible channel for communication on the preset of growth in Information Technology. The ability to transform content to a digitized format allowed new-age media to take shape within the internet. Accessibility through hand-held devices like mobile platforms, personal computers, digital devices, and virtual computing machines has aided the growth of new-age media. The medium of new media is not just restricted to social networking platforms, blogs, online newspapers, digital games and virtual reality, but any aspect of communication that can be communicated real-time, processed, stored and delivered in formats of data instantaneously.

To help process 7.3 billion packages a year – 231 per second – the U.S. Postal Service is using artificial intelligence.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Every summer, I get the travel itch. Before you head out, make sure your home is locked down. The bad news is security cameras, from video doorbells to a full-fledged security system, aren't always hack-proof out of the box.

Artificial Intelligence (AI) is a technology that is closely related to computer science.

In order to develop artificial general intelligence (AGI), the sort of all-encompassing AI that we see in science fiction, we might need to merely sit back and let a simple algorithm develop on its own. Reinforcement learning, a kind of gamified AI architecture in which an algorithm "learns" to complete a task by seeking out preprogrammed rewards, could theoretically grow and learn so much that it breaks the theoretical barrier to AGI without any new technological developments, according to research published by the Google-owned DeepMind last month in the journal Artificial Intelligence and spotted by VentureBeat. While reinforcement learning is often overhyped within the AI field, it's interesting to consider that engineers could have already built all the tech needed for AGI and now simply need to let it loose and watch it grow. The kind of artificial intelligence that we encounter every day of our lives, whether it's machine learning or reinforcement learning, is narrow AI: an algorithm designed to accomplish a very specific task like predicting your Google search, spotting objects in a video feed, or mastering a video game. By contrast, AGI -- sometimes called human-level AI intelligence -- would be more along the lines of C-3PO from "Star Wars," in the sense that it could understand context, subtext, and social cues.

Spotify's mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the opportunity to enjoy and be inspired by these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world's most popular audio streaming subscription service with a community of more than 345 million users. We are looking for an outstanding Data Scientist to join the band and build core analytics to support our Music organization. The Music organization is responsible for editorial music recommendations, programming of music content on Spotify, relationships with the music industry, and artist marketing.

Artificial Intelligence (AI) is a transformative technology. It may undoubtedly prove beneficial for the future but a complete AI takeover is also highly likely, if due measures aren't taken now. AI is creating fear and excitement by disrupting several industries. Technology taking over humans has always been a very common theme in science fiction movies for as long as we can remember. In the movie I, Robot starring Will Smith, for instance, it is portrayed that robots become intelligent enough to take over humans entirely.

An erudite study of Global Artificial Intelligence Products Market has been published by The Research Insights.

There are now two ways of creating digital images with a camera. You can either follow a software-centric computational photography approach. The other way is to stick to traditional hardware-centric optical photography. The former is used with AI to help enhance the final image, the latter relies on the quality of the camera's components (e.g. The two techniques may differ, but they are not at all on a collision course.

Despite the high accuracy offered by state-of-the-art deep natural-language models (e.g. LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a black-box to the end-user. Hence, explainability is rapidly becoming a fundamental requirement of future-generation data-driven systems based on deep-learning approaches. Several attempts to fulfill the existing gap between accuracy and interpretability have been done. However, robust and specialized xAI (Explainable Artificial Intelligence) solutions tailored to deep natural-language models are still missing. We propose a new framework, named T-EBAnO, which provides innovative prediction-local and class-based model-global explanation strategies tailored to black-box deep natural-language models. Given a deep NLP model and the textual input data, T-EBAnO provides an objective, human-readable, domain-specific assessment of the reasons behind the automatic decision-making process. Specifically, the framework extracts sets of interpretable features mining the inner knowledge of the model. Then, it quantifies the influence of each feature during the prediction process by exploiting the novel normalized Perturbation Influence Relation index at the local level and the novel Global Absolute Influence and Global Relative Influence indexes at the global level. The effectiveness and the quality of the local and global explanations obtained with T-EBAnO are proved on (i) a sentiment analysis task performed by a fine-tuned BERT model, and (ii) a toxic comment classification task performed by an LSTM model.