mpaicommunity

I think that the publication of the MPAI Metaverse Model (MPAI-MMM) standard last week is an important step in making the metaverse a viable proposition because it provides practical means for an implementer to develop a metaverse instance (M-Instance) that interoperates with another similarly developed M-Instance. This post has the moderate ambition of just describing the high-level content of the standard by looking at the Table of Contents. #MPAIStandards, #MMM_ARC, #MMM_TEC https://lnkd.in/dxwsT2MG

The 48th MPAI General Assembly (MPAI-48) has approved for publication of MPAI Metaverse Model (MPAO-MMM), the first publicly available specification designed for metaverse interoperability (https://bit.ly/3ZRkzV7).  Register at https://lnkd.in/dNV2aAte  to attend the online presentation on 18 October at 15 UTC. MPAI-48 has also approved 4 more standards: - Multimodal Conversation (MPAI-MMC) V2.2. Register at https://lnkd.in/d9bSyufU to attend online presentation on 15 October at 14 UTC. - Object and Scene Description (MPAI-OSD) V1.1. Register at https://lnkd.in/dBJBfBzM to attend online presentation on 16 October at 15  UTC. - Portable Avatar Format (MPAI-PAF) V1.2. Register at https://lnkd.in/dcxxjzrR to attend online presentation on 17 October at 14 UTC. MPAI-48 has also released the open-source implementation of the Television Media Analysis Use Case (OSD-TMA) that produces a description of the audio and visual objects, the IDs of speakers and faces with their space and time information, and the text of the speaker utterances of a TV program. Register at https://lnkd.in/dw9y7Dyi to attend online presentation on 14 October at 15 UTC. #MPAIStandards, #MPAI_MMM, #MPAI_MMC, #MPAI_OSD, #MPAI_PAF,

Don’t miss the opportunity to know more about Audio and Qualifiers in MPAI. Join the online presentations of MPAI-CAE V2.2 on 24 Sep at16 UTC (register @ https://lnkd.in/dHpYYgeC) and of MPAI-TFA V1.0 on 27 Sep at14 UTC (register @ https://lnkd.in/dsgD5EKx). New to Qualifiers? Read a brief account @ https://lnkd.in/dgFiiUNC

Collaborative Immersive Laboratory (XRV-CIL) is a project designed to enable researchers in network-connected physical venues equipped with devices that create an immersive virtual environment to manipulate and visualise laboratory data with other researchers located at different places and having a simultaneous immersive experience. One use case for CIL would be to work with medical data such as scans to discover patterns within cellular data to facilitate therapy identification. Figure 1 shows a CT or MRI dataset being normalised, analysed and the result rendered with e.g., a renderer that is common to the participating labs. Each lab may enter annotations to the dataset or apply rendering controls that enhance appropriate parts of the rendered dataset.  Read the document at https://lnkd.in/daySK_86 #MPAIStandards, #MPAI_XRV

MPAI is proud to announce that it has reached another important milestone with the publication of version 2.2 of Context-based Audio Enhancement (MPAI-CAE) and version 1.0 of Data Types, Formats, and Attributes (MPAI-TFA) with a request for Community Comments.   Technical Specification: Context-based Audio Enhancement (MPAI-CAE) V2-2 (https://lnkd.in/drSpGFEb) improves the user experience for different audio-related applications, such as entertainment, restoration, and communication in a variety contexts such as in the home, in the office, and in the studio. V2.2 extends the capabilities of several data formats used across MPAI standard, in particular Audio Object and Audio Scene Description.   Technical Specification: Data Types, Formats, and Attributes (MPAI-TFA) V1.0 (https://lnkd.in/dUyew4mn) specifies Qualifiers – a Data Type containing Sub-Types, Formats, and Attributes – associated to “media” Data Types – currently Text, Speech, Audio, and Visual – that facilitate or enable the operation of an AI Module receiving a Data Type instance.   The capabilities of the two standards will be presented online at two events: on August 24 at 16:00 UTC for MPAI-CAE V2.2 and on August 27 at T14:00 UTC for MPAI-TFA. To attend, please register at https://lnkd.in/dHpYYgeC for MPAI-CAE V2.2 and at https://lnkd.in/dsgD5EKx for MPAI-TFA V1.0.   #MPAIStandards, #MPAI_CAE, #MPAI_TFA

Register for the MPAI Human and Machine communication V1.1 online presentation https://bit.ly/4cU01is. Learn how a broad applicability standard has been built from 5 existing MPAI standards. Reusability of standard technologies is not just a slogan. In MPAI, it is our practice! #MPAIStandards, #MPAI_HMC

Human and Machine Communication (MPAI-HMC) enables an Entity – a human in an audio-visual scene of a real space or a Machine in an Audio-Visual Scene of a Virtual Space – to hold a multi-modal communication with another Entity possibly in a different Context, e.g., language and culture. Read an introduction at https://lnkd.in/dtFD6uSH #MPAIStandards, #MPAI_HMC

At its 46th General Assembly, MPAI approved the publication of Technical Specification: Human and Machine Communication (MPAI-HMC) V1.1 (https://lnkd.in/diZxCrhJ). The standard enables an Entity – a human in an audio-visual scene of a real space or a Machine in an Audio-Visual Scene of a Virtual Space – to hold a multi-modal communication with another Entity possibly in a different Context, e.g., language and culture. MPAI-HMC is the first standard that is fully agnostic of the human or artificial nature of the communication parties. #MPAIStandards, #MPAI_HMC

At its 45th General Assembly, MPAI approved the publication of Conformance Testing Specification: Multimodal Conversation (MPAI-MMC) V2.1 (https://lnkd.in/dj6nJphe). Using this specification, implementers can verify that an implementation of Conversation with Emotion, Multimodal Question Answering, and Unidirectional Speech Translation AI Workflows conforms with Technical Specification: Multimodal Conversation (MPAI-MMC) V2.1. By “implementation” MPAI means either the entire AI Workflow (AIW) or an AI Module (AIM) that is or may be used in an implementation. The Multimodal Conversation Development Committee (MMC-DC) had to produce the datasets to be used in Testing implementations of MPAI-MMC AIWs and AIMs for Conformance. The datasets are publicly available on the MPAI Git (https://lnkd.in/dyPfbXyA) upon registration. #MPAIStandards, #MPAI_MMC

At its 45th General Assembly, MPAI approved the publication of Reference Software Specification: Neural Network Watermarking (MPAI-NNW) V1.2 (https://lnkd.in/dzqP-b4z). The software attached to it enables the MPAI Community to start from an implementation of the MPAI-NNW standard. The software comes in two configurations. In the first one, users have an AI Framework-based implementation with the possibility to add and remove software components. In the second one, sophisticated Neural Network Watermarking technologies can be deployed on limited capability Microcontroller Units. #MPAIStandards, #MPAI_NNW