The current pace of development in artificial intelligence (AI) is remarkable, achieving milestones in computer vision, speech recognition, and natural language processing that were, until recently, considered many years away. In combination with large-scale data collection and analysis, AI is also increasingly taking on some operational roles in media production and distribution.
However, much of what is branded as AI in media should be understood as the evolution of data analysis. First, the use of unstructured and structured data, coming from multiple sources, combined with configurable data science algorithms, allows for the development of powerful prediction engines that are galvanising the data analytics and business intelligence market. New knowledge graphs and correlations can be found and refined in real time by data scientists.
“This is where most value can be delivered in the pay-TV industry in the short term, as the players need to drive their business using more relevant data that they often don’t have, as legacy TV systems are not always two-way connected,” explains Simon Trudelle, senior director, product marketing, Nagra. “Social platforms and other external systems can offer extensive sources of data intelligence that can benefit service providers.”
A second dimension to consider is the development of machine learning (ML) predictive algorithms that use a feedback loop to improve the relevance of the prediction engine over time. Then there are AI/ML platforms that go further and leverage neural networks. These have been proven to outperform human beings for repetitive skill-based tasks, such as speech or image recognition.
AI, ML, and robotic process automation can be seen as an integrated suite of solutions that has already proven successful at dramatically improving back-office and operational-level processes. Specifically, robotic process automation is directly applicable to routine rules-based back-office and operational processes, which currently consume enormous amounts of human resources and time.
“Automation of routine and rules-based tasks like report generation, account entry, account closure, media uploads, and a wide variety of financial processes, have three immediate and direct benefits,” explains Chris Hodges, managing director, Accenture – communications, media,
and technology. He prefers to term this “intelligent automation”.
Firstly, he states, automation makes the process go faster. Secondly, it reduces the errors ubiquitous in “swivel-chair” processes, where a person goes back-and-forth between multiple systems with mind-numbing repetition. Thirdly, automating these back-office processes frees up human capital for more creative and “human-level” tasks.
Some specific examples in media include media uploads, reformatting, file renaming, and responding to network alarm interruptions and outages. All of these are well suited to the application of intelligent automation, according to Hodges.
“Once processes are automated, they produce an enormous amount of real-time data, which can be analysed for patterns and trends, and allow for pre-emptive process changes or adjustments,” he says.
“AI can analyse successful video or media projects based on specified criteria. These criteria might include the number of people, shot angle, gender, movement, sound levels, and so on. All of these can be analysed by AI/ML, producing patterns of production for specific criteria. Today, this is done largely through a combination of producers with particular individual personal styles. AI/ML will allow this to become more systematic, repeatable, and scalable across multiple projects at once.”
Machine intelligence provides an important toolkit to business, with applications ranging from insight – how systems are actually being used – to augmentation, which invloves assisting human effort and oversight, and automation, via autonomous system monitoring and intervention.
Ericsson’s CTO, Steve Plunkett, identifies examples including rethinking the quality control (QC) process by analysing errors found in manual and auto QC activity and identifying higher risk material, based on the supplier, technical properties, and so on, where more time can be spent, rather than a uniform approach to all content.
Video management software and services company, Piksel, incorporates AI/ML into its technology. It is able to conduct automated inspection of content to provide deeper metadata identification and linkage. “Machine learning can be used to match shows and movies with a greater than 95% accuracy, so service providers can use this improved consistency to give their customers better search and UI provision,” says Kristan Bullett, head of solutions. “Machine learning can also assist with matching to third-party metadata providers to further improve accuracy.”
In terms of cybersecurity and the threat of malicious attack, if the system is monitoring all the logs and knows what ‘normal’ looks like, then unusual internet traffic coming from a new device on the network could be flagged as it happens, enabling early intervention with preventative measures.
“Rather than just providing intrusion detection in the network layer, our automation application software might become aware of where control commands are expected to come from,” says Pebble Beach Systems’ CTO, Ian Cockett.
Another important area is in network management. Bandwidth is not limitless and is a significant portion of the expense of operating, especially for over-the-top (OTT) content.
“Today’s CDNs (content delivery networks) and protocols are very wasteful of bandwidth – your device will always pick the highest available bandwidth, whether it’s needed or not at that particular time,” says Tim Child, co-founder at media asset management (MAM) vendor, Cantemo.
“Analysing the video as it is transmitted and then using the data to determine the required bandwidth can help network operators make more efficient use of bandwidth, lowering costs and improving quality at the same time.”