Where there are organisations counting years of existence in decades or centuries, there should not be much to celebrate for an organisation that only reaches as few as five years of existence. But there are years and years – even days and days – like in one day as a lion or a hundred years as a sheep.
The last five were not the years of a sheep but as one day as a lion.
We started with the idea of an organisation dedicated to standards for AI-based data coding because we thought that standards would bring benefits to a domain mostly alien to it. Not like some standards that look more like legal tools designed to oppress users but standards offering fair opportunities to all parties in the chain extending from innovators to end users.
An ambitious organisation like MPAI could not operate like four friends in a bar. The MPAI operation rules were developed and are now enshrined in the MPAI Patent Policy. The ambitions of MPAI were further enhanced by the definition of the MPAI Ecosystem extending from MPAI to implementers, integrators, and end users with the introduction of a new actor called MPAI Store, now incorporated in Scotland as a company limited by guarantee. There is a standard – Governance of the MPAI Ecosystem (MPAI-GME) setting the rules of operation of the Ecosystem.
The idea of a mission was there but what about implementing it? We acted as lions and posited that opaque monolithic AI should become component-based AI. Now a large share of our standards are based on the AI Framework (MPAI-AIF) standard, specifying an environment where AI Workflows composed of AI Modules can be initialised, dynamically configured, and controlled. MPAI-AIF also provided a stimulus to adoption of JSON Schema as a “language” to represent data types, AI Modules, and AI Workflows in MPAI standards. Today there is virtually no MPAI standard that does not use that language.
Having laid down the technical foundations, we started the buildings. One was designed to host the quite representative area of human and machine conversation extending beyond the “word” to cover other sometimes ethereal but information-carrying sensations and feelings. The standard called Multimodal Conversation (MPAI-MMC) is the first attempt at digitally representing this ethereal information with the Personal Status data type and Human-Machine Communication (MPAI-HMC) is an excellent example of its application.
Another investigation stream since the early MPAI days is audio sitting at the MPAI table as “Context-based Audio Enhancement” leading to the Context-based Audio Enhancement – Use Cases (MPAI-CAE) standard. Finally, with Compression and Understanding of Industrial Data (MPAI-CUI), MPAI demonstrated that data from so far unexplored domains like finance could benefit from standards.
Just one year after its establishment, MPAI could claim success by publishing its first three standards: MPAI-CUI, MPAI-GME, and MPAI-MMC and, by the end of 2021, another two: MPAI-AIF and MPAI-CAE.
Since its early days, MPAI was convinced that standards should have as much visibility as possible. For this reason, it established a successful cooperation with the Institute of Electric and Electronic Engineers (IEEE) – Standard Association (SA). Today, starting from three standards in 2022, nine MPAI standards have been adopted by IEEE without modifications and three more are in the pipeline.
The creation of MPAI Development Committees and Working Groups and their activity continued unrelenting. The use of watermarking and then fingerprinting to trace the use of neural networks let to the development of Neural Network Watermarking – Traceability (NNW-NNT). Connected Autonomous Vehicles was started in late 2020 and is now a standard with the name Connected Autonomous Vehicle – Technologies (CAV-TEC). MPAI was probably the first to engage in activities leading to a metaverse standard and now it can claim to have a solid candidate to lead the move to interoperable metaverses with MPAI Metaverse Model – Technologies (MMM-TEC). Since its early days, MPAI worked on online gaming, producing the Server-based Predictive multiplayer Gaming – Mitigation of Data Loss Effects (SPG-MDL) standard where a set of AI Modules predicts the game state of an online multiplayer game.
MPAI abhors the attitude of other standards bodies who develop unnecessarily “siloed” standards where technologies are treated exclusively from the point of view of the domain of that standard without considering similar technologies in other domains. Object and Scene Description (MPAI-OSD) and Portable Avatar Format (MPAI-PAF) do specify AI Workflows specific to their domains but their AI Modules and Data Types were specified for wide reuse in many other MPAI standards. This attitude is not confined to these two standards as the same can be said of MPAI-CAE and MPAI-MMC.
Atypical – but no less important – standards are AI Module Profiles (MPAI-PRF) establishing a machine-readable description to identify AI Module Profiles and Data Types, Formats, and Attributes (MPAI-TFA) providing a standard way to add information about data for processing by a machine.
Last comes a standard that embodies probably the very first activity – AI for video. AI-Enhanced Video Coding – Up-sampling Filter for Video applications (EVC-UFV) offers an AI super-resolution filter vastly superior to currently used filters.
Five years ago, MPAI was very bold in targeting standards for AI, then just a nice technology to talk about. In five years, however, AI is all over the place and much talked about. What will the future offer for MPAI?
Some answers are clear:
- With its impressive portfolio of 15 standards, there will be much maintenance and enhancement work to do.
- Two new standards are being developed and should be completed in a short time: AI for Health – Health Secure Platform and XR Venues – Live Theatrical Performance.
- One project – End-to-End Video coding has still to go through the Call for Technologies phase
- A Call for Technologies is open, and responses are expected: Neural Network Watermarking – Technologies.
- A new Call for Technologies on Pursuing Goals in the metaverse is being prepared. This will require the development of a significant number of “behaviours” on top of a “baseline” Small Language Model.
- Development of reference implementations to enhance the value and attractiveness of existing standards.
AI continues its lightning speed of development and MPAI will continue watching and identifying standardisation opportunities in different domains.
Long live MPAI!
