Use of technologies based on Artificial Intelligence (AI) is extending to diverse applic­ations yielding one of the fastest-grow­ing markets in the data analysis and service sector. However, AI is developing more slowly than necessary because it lacks operational reference standards compar­able to those that have propelled digital media. Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) has identified data coding as a promising area for standardisation.

	MPAI considers the “AI module” (AIM) and its inter­faces as the AI building block. The syn­tax and sem­antics of interfaces determine what AIMs should per­form, not how. AIMs can be implemented in hard­ware or software, with AI, Machine Learn­ing or legacy Data Proces­sing (DP).
	The AIM is part of the MPAI “Emotion Enhan­ced Speech” Use Case where AI is used to add the desired emotion to an em­ot­ion-less speech. The Emotion Know­ledge Base (KB) may be absent if the relevant knowledge is moved to a neural network in the Speech analysis AIM.

So far, MPAI has collected some fifteen Use Cases spanning in the following categories: Context-based Audio Enhancement; Multi­modal Conver­sation; AI-Enhanced Video Cod­ing; Compression and Under­standing of Financial Data; Integrative Gen­omic/Sensor Analysis; and Server-based Predictive Multiplayer Gam­ing. The first two areas are about to issue Calls for Technologies.

AIMs need a proper environment to enable creation, execution, com­pos­ition and update of AIM-based work­flows. The MPAI-AI Framework (AIF) is the envir­onment for building potentially high-com­plexity sol­utions intercon­nec­ting multi-vendor AIMs trained for spec­ific tasks and exchanging data encoded in standard formats via stan­dard interfaces.

MPAI has issued a Call for Technologies to develop the MPAI-AIF standard. Called-for tech­nologies should support a set of requirements, including the following:

1. ML/legacy DP life cycles (Single AIM)
   1. instantiate-configure- start-suspend-stop-renove
   2. dump/retrieve internal state | enforce resource limits
2. ML/legacy DP life cycles (Multiple AIMs)
   1. Initialise | instantiate-remove-config­ure AIMs
   2. configure interfaces in manual-automatic-dynamic-adaptive ways
   3. 1- and 2-way signal for workflow initialisation and control, communic­ation and security policies between AIMs
3. Machine learning
   1. train-retrain-update AIMs
   2. auto-configure/reconfigure ML-based computational models
   3. dynamic update of ML models
   4. support supervised, unsupervised and reinfor­cement-based learning paradigms
4. Workflows
   1. hierarchical execution of work­flows
   2. computational graphs, such as Direct Acyclic Graph
   3. AIM topologies synchronised accor­ding to time based & full ML life cycles

MPAI is mindful of Intellectual Property Rights (IPR)-related problems accompanying high-tech standardisation. Unlike standards developed by other bodies, which are based on vague and con­tention-prone Fair, Reasonable and Non-Discriminatory (FRAND) declarations, MPAI standards are based on Framework Licences that set out in advance agreed guidelines for the IPR holders.