The media industry is facing the biggest changes in its history. Constantly evolving technologies are transforming the media landscape at the speed of light. Media companies are moving towards agile workflows and user-centric design. Journalists support quality journalism and are starting to understand the importance of moving from print to digital. Producing and publishing across multiple platforms and services becomes the norm for many publishers, and ever-declining advertising revenue must be offset by attracting new paying subscribers and reducing overall costs. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The publisher must transform itself to adapt to the changing landscape and now provides integrated and compelling experiences for both advertisers and readers. Depending on who you ask about the future of media and journalism, it's the best of times or the worst of times. (Kaul, 2012) Recent and emerging technology trends in the media industry Artificial intelligence, machine learning and deep learning Artificial intelligence (AI) is a branch of computer science in which computers are programmed to do things that normally require intelligence human. (Russell and Norvig, 2003) This includes problem solving, pattern recognition, learning, perceiving situations or the environment, and understanding language. Artificial intelligence uses its own computer languages, special types of computer networks modeled similarly to the human brain. Machine learning programs run on neural networks and analyze data to help computers find new things without being explicitly programmed where to look. Machine learning is useful because it allows computers to predict and make decisions in real time without human intervention. (Schmidhuber, 2015) Deep learning is a relatively new branch of machine learning. Such systems are trained to learn on their own. This means that more and more human processes will be automated. Automation and Augmented Journalism Companies like Arria NLG, a UK-based company offering artificial intelligence technologies, have built working systems that can transform raw data into stories indistinguishable from text written by humans. Summaries, crime reports or financial reports are today written by automated systems and published by media companies. For now, these systems are only able to independently tell the story of the "what". Other AI systems can help increase journalists' workflow. By working alongside such systems, journalists gain new skills to understand the “why.” However, we can assume that future systems will also be able to do this autonomously. Voice InterfacesEthical Issues and TradeoffsThe problem with AI machine learning is that the data and models used are coded with bias. This flaw can be traced back to the people who built the models. These same people are subject to homogeneous working and learning environments that give rise to unconscious biases. Please note: this is just an example. Get a custom paper from our expert writers now. Get a Custom Essay Studies have been undertaken by ProPublica (Mattu et al., 2016), Princeton, MIT, Harvard, University of California-Berkeley show explicit biases in algorithms in most industries. Systems are trained using limited data sets and human-made training programs. Training sets often reveal biases.