Review on Digital Technolgies

The media industry is facing the biggest changes in its history. Constantly evolving technologies are transforming the media landscape at lightning speed. Media companies are shifting to agile workflows, user-centric design. Journalist advocate quality journalism and start to understand the importance to shift from print to digital. Producing and publishing on multiple platforms and services becomes the norm for many publishers and ever falling ad revenues need to be compensated with attracting new paying subscribers while reducing overall costs.

The publisher has to transform to adapt to the changing landscape and are now providing integrated and compelling experiences for advertisers and readers alike. Depending on whom you ask about the future of media and journalism, it’s either 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 human intelligence. (Russell and Norvig, 2003)

This includes problem-solving, pattern recognition, learning, the perception of situations or environment and understanding language. AI uses its own computer languages special kinds of computer networks that are modeled similarly to human brains. Machine learning programs run on neural networks and analyze data in order to help computers find new things without being explicitly programmed where to look. Machine learning is useful because it enables computers to predict and make real-time decisions 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 journalismCompanies like Arria NLG, a UK-based company offering AI-technologies, built working systems that can transform raw data into stories that are indistinctable from the human-written text. Recaps, crime reports or financial summaries are nowadays written by automated systems and published by media companies. For now, such systems are only capable of telling the story of “what” autonomously. Other AI systems can help to augment the workflow of journalists. Working alongside which such systems, journalists gain new abilities to understand the “why”. However, we can assume that future systems will be able to do that autonomously too. Voice interfacesEthical issues and trade-offsThe problem with AI machine learning is that the data and models used are encoded with bias. This fault can be traced back to the people who built the models. These people themselves are subjected to homogeneous working and learning environments resulting in unconscious bias.

Studies were undertaken by ProPublica (Mattu et al., 2016), Princeton, MIT, Harvard, University of California-Berkeley shows explicit bias in algorithms across most industries. Systems are trained using limited datasets and human-built training programs. Often, the training sets reveal unacknowledged bias hidden within us. As machine learning and augmented journalism become reality in most newsrooms, a journalist must learn how to investigate the data itself. Furthermore, they need to understand the models the systems use to learn and interpret such data.