In a world where information technology, consumer electronics, entertainment and telecommunication products and content variously converge by incorporating increasingly sophisticated technologies and the need for timely available standards is as strong as ever, MPEG used to provide a proven mechanism to bring research results into standards that promote innovation for the benefit of all. In its 30 years of activity MPEG had developed an impressive portfolio of standards and technologies that have created an industry worth several hundreds billion USD. This page is kept as historical reference to MPEG achievements. The initial significant step is to enable the understanding of the inner working of complex AI systems. MPAI pledges to address ethical questions raised by its technical work with the involvement of high-profile external thinkers. 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 where IPR holders set out in advance IPR guidelines.įinally, although it is a technical body, MPAI is aware of the revolutionary impact AI will have on the future of human society. MPAI is mindful of IPR-related problems which have accompanied high-tech standardisation. MPAI’s AI framework enabling creation, execution, composition and update of AIM-based workflows (MPAI-AIF) is the cornerstone of MPAI standardisation because it enables building high-complexity AI solutions composed of interconnected multi-vendor AIMs trained to specific tasks, operating in the standard AI framework and exchanging data in standard formats.įocusing on AI-based data coding will also allow MPAI to take advantage of the results of emerging and future research in representation learning, transfer learning, edge AI, and reproducibility of performance. AIMs can be implemented in hardware or software, with AI or Machine Learning or legacy Data Processing. An AIM is defined by its function, and by the syntax and semantics of its interfaces, not by how the function is performed. MPAI considers AI module (AIM) and its interfaces as the AI building block. Examples are compression and semantics extraction. MPAI defines data coding as the transformation of data from a given representation to an equivalent one more suited to a specific application. This is the list of standards adopted and published so far.Ĭompression and Understanding of Industrial Data MPAI – Moving Picture, Audio and Data Coding by Artificial Intelligence – believes that universally accessible standards can have the same positive effects on AI that standards wrought to digital media and has identified data coding as the area where standards can foster development of AI technologies, promote use of AI applications and contribute to the solution of existing problems. MPAI approves a new Technical Specification and a Technical Report MPAI publishes Version 2 Audio Enhancement for Community Comments and the Neural Network Watermarking Reference Software Online presentation: MPAI’s AI-based End-to-End video codec has better compression than traditional codecs. Online presentation: 30 months after foundation MPAI presents its activities and results. This page is now used to communicate news about MPAI, the group the continues the MPEG spirit with the mission to develop standards for data coding by artificial intelligence with clear licencing frameworks. Splinters using the word MPEG still exists. This used to be the home page of MPEG, the group who developed an impressive portfolio of standards and technologies that have created an industry worth several hundreds billion USD. This page is kept as historical reference to MPEG achievements because the group I conceived in the summer of 1987, implemented in 1988, and guided for 32 years was closed on 2.
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