Artificial Intelligence (AI) offers more efficient ways to implement processes formerly carried out with Data Processing (DP). However, AI has often been used in an ad hoc way. Machines use AI to perform extremely complex functions, but the value of the result depends on the training data sets, which are typically known only to the implementer. In information services, this data issue may have potentially devastating social impacts due to expected bias in the training, but in other applications, such as in autonomous vehicles, the issue is the inability to trace processes leading to a particular decision.
Data Processing standards have played a major role in promoting use of digital technologies for products, services, and applications. However, few examples are known of AI standards with an approach comparable to that of DP standards. MPAI (Moving Picture, Audio, and Data Coding by Artificial Intelligence) has taken on the mission of developing AI-based data coding standards. The group has already developed several Technical Specifications using AI Modules (AIMs) that attempt to break monolithic applications into components with known functions and interfaces and implementable using AI or DP technologies: connected autonomous vehicles, audio enhancement, prediction of company performance, multimodal human-machine conversation, metaverse architecture, neural network watermarking], and portable avatars.
By incorporating these modules (AIMs), applications can be implemented as AI Workflows (AIWs), themselves with known functions and external interfaces, composed of interconnected AIMs.
MPAI Technical Specifications offer the ability to implement AI applications whose operation is more traceable and explainable and the ability to create a competitive market of components – AIMs – with standardised functions and interfaces and potentially providing competitive performance. As an organisation, MPAI offer a rigorous though fast route to standardisation: less than a year for a technology standard.