AI is already here and is pervasive, with many applications in the media sector, from media news research and production, to game development, music generation, and media asset management. Europe is home to numerous research labs and universities that are exploring the vast possibilities and bounds of AI, as well as to a vibrant ecosystem of media companies that want to use AI to improve their products, services, and operations. But bridging the gap between the AI scientists and researchers and the actual end-users of the AI algorithms has always been a challenge. In AI4Media, we seek to narrow this gap, by publishing a set of white papers as part of AI4Media’s effort to align AI research with the industrial needs of media companies, describing the most important challenges and requirements for AI uptake in each use case area within the media industry.
The seven AI4Media white papers deal with the use of AI in several media domains throughout the media and content value chain, spanning from disinformation detection and analysis; news research, production, and publication; media production; data-driven research with media content in social sciences and humanities; to video game testing and music processing, music composition, and media asset organisation and management.
Below we provide an overview of the key messages and insights from each white paper.
AI Support Needed to Counteract Disinformation
- Most fact checking and verification specialists regard AI technologies as highly valuable and important to support them in the task of counteracting disinformation, despite shortcomings associated with some existing tools.
- New AI support functions are needed in two main areas of fact checking and verification work:
- Detection of synthetic media items or synthetic elements, and identification of content manipulation,
- Detection of disinformation narratives in online/ social media, including respective content, actors, or networks.
- The user group of fact checkers and verification specialists has a high need for trustworthy, understandable AI support functions, especially in terms of explainability, transparency, and robustness.
AI for News. The smart news assistant
- There is a clear opportunity for AI tooling to facilitate mundane and burdensome journalistic tasks, giving more space to creativity and original investigative and informative work.
- Because of the fragmented information landscape, monitoring assistance is of interest to journalists.
- The fact that journalists are increasingly confronted with disinformation results in a need for an understandable, accessible and easy-to-use AI tools for fact-checking.
AI in Vision: High Quality Video Production and Content Automation
- Several crucial tasks of the media value chain are not well covered by existing tools, new AI-driven tools are needed to fill this gap.
- Trustworthy AI features are one of the key factors that affect the wide adoption of AI in the news media sector, especially those related to Privacy Protection and Legal Compliance. The research community should push as much as possible to build trustworthy AI tools that respect user privacy and comply with relevant regulations.
AI Techniques for Social Sciences and Humanities Research
- While many researchers are well-versed and are technically supported in textual analysis, AI tools for multimodal content analysis of still images, moving images and sounds fall short to meet the requirements by end-users. This is due to algorithmic limitations and UI/UX considerations not being fully taken on board.
- To fully integrate AI tools into their workflows, researchers require flexible, easily configurable, transparent and explainable solutions that could be adopted in a variety of research scenarios.
AI for Video Game Testing and Music Processing
- AI-powered tools shouldn’t replace Quality Assurance and music analysis/synthesis processes done by humans but rather enhance existing practices and help humans in achieving their tasks.
- Industry partners don’t mind spending more time to get AI-powered tools working but they must be able to easily integrate them into their production pipeline.
- It is important to have fine control over the input of the automated AI systems and provide a variety of methods to showcase their output.
AI music composition tools for humans
- Tools for music co-creation go beyond learning models and should include the architectural requirements that a user needs to execute a full application. This means access to powerful computing infrastructure.
- A creative process cannot be formalized, and a key element is the balance between powerful tools with the freedom to use and combine them. This is the basic requirement for the co-creative process.
AI Technology in Image & Video Organisation
- AI-enhanced automated organisation of large media collections significantly aids media companies in reducing costs and, at the same time provides new opportunities for visual content monetisation.
- Media companies have realised the importance of implementing AI-enhanced image and video (re)organisation technologies but have lagged in implementing such technologies as part of their workflows.
A common theme in almost all use case areas is the demand for trustworthy AI tools that are explainable and easily understandable by their end-users. User experience aspects are also quite important naturally; a smooth user experience and intuitive interfaces are a key requirement for most media professionals. Finally, maintaining control over the AI results and any subsequent decision making process is an important factor for media professionals.
Author: Danae Tsabouraki (ATC)