(Informative)
A key element of the MPAI approach to for AI-based Data Coding standards is Technical Specification: AI Framework (MPAI-AIF) V2.0 based on the notion of a framework – called the AI Framework (AIF) – implementing systems – called AI Workflows (AIW) – where interconnected components – called AI Modules (AIM) – communicate Data obtained by applying specific functions to other AIMs in the AIW. AIMs can improve the effectiveness of the functions they perform if they know more about the capabilities of the AIMs they are connected to and the data they receive.
The Natural Language Processing example highlights some issues related to AIM capability. An instance of the MPAI Natural Language Processing (MMC-NLU) AIM should produce refined text and Meaning from the text possibly received from an Automatic Speech Recognition (MMC-ASR) AIM with at least three levels of sophistication: 1) it may just be able to process the recognised text and Meaning using that text; or 2) it can process the information identifying an object referenced by the text being analysed; or 3) it can also utilise information about the position of the object in the relevant space. The accuracy of the produced refined text and Meaning is expected to improve when moving from the first to the third case.
Technical Specification: AI Module Profiles (MPAI-PRF) enables an AIM instance to signal its Attributes – such as input data, output data, and functionality – and Sub-Attributes – such as languages supported by a Text and Speech Translation AIM – that uniquely characterise the AIM. Currently, MPAI-PRF defines the Attributes of eight AIMs but Profiles for more AIMs are likely to be defined in the future.
The effectiveness of the functions performed by an AIM can also be enabled or enhanced if the AIM knows more about the characteristics of the Data received. Examples of characteristics can include the CIE 1931 colour space of an instance of the Visual Data Type; the MP3 digital representation of an instance of a Speech Data Type; the WAV file format of an instance of an Audio Data Type; the gamma correction applied to the device that produced an instance of a Visual Data Type; the Instance ID of an object in an instance of Audio Data Type; or the Text conveyed by an instance of a Speech Data Type.
Technical Specification: Data Types, Formats, and Attributes (MPAI-TFA) V1.0 specifies a new Data Type called Qualifier, a container that can be used to represent, for instance, that a Visual Data Type instance uses a given colour space (Sub-Type), is digitally represented as an AVC video (Format), and is described by Dublin Core Metadata (Attribute). The current versions of MPAI Technical Specifications generally assume that most of the Media Objects exchanged by AIMs are composed of “Content” and “Qualifiers”.
Therefore, Qualifiers are a specialised type of metadata intended to support the operation of AIMs receiving data from other AIMs and conveying information on Sub-Types, Formats, and Attributes related to the Content. Qualifiers are intended to convey information for use by an AIM. They are human-readable but intended only to be used by AIMs.
Future versions of MPAI-TFA will likely be published because of the large variety of application needs that will require the specification of Qualifiers for additional Data Types. MPAI-TFA users are invited to communicate their need for extension of existing and specification of additional Data Types in MPAI-TFA to the MPAI Secretariat. Therefore, versioning of Qualifiers is a critical component of MPAI-TFA.
The Chapters, Sections, and Annexes of this Technical Specification are Normative unless they are explicitly labelled as Informative. In all Chapters and Sections, Terms beginning with a capital letter are defined in Table 1 if they are specific to this Technical Specification and in Table 2 if they are common to all MPAI Technical Specifications. All Chapters and Annexes are Normative unless they are labelled as Informative.
