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An introduction to the Neural Network Watermarking Call for Technologies

Introduction During the last decade, Neural Networks have been deployed in an increasing variety of domains and the production of Neural Networks became costly, in terms of both resources (GPUs, CPUs, memory) and time. Moreover, users of Neural Network based services more and more express their needs for a certified service quality. NN Traceability offers solutions to satisfy both needs, ensuring that a deployed Neural Network is traceable and any tampering detected. Inherited from the multimedia realm, watermarking assembles a…

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The birth of Audio in MPEG

Thirty-six years ago today marked the birth of the MPEG-Audio group. The initial two characters of MPEG - M (oving) and the P (icture) - leave no doubt about the original public intentions in setting up the MPEG group which had met for the first time in Ottawa, ON on 10-12 May. My real intentions, however, were very clear in my mind since the very beginning (actually, they dated back several years before): an audio-visual  system had to be specified…

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Introduction to MPAI’s Human and Machine Communication (MPAI-HMC) V1.1

One of the new Technical Specifications published by MPAI is Version 1.1 of Human and Machine Communication (MPAI-HMC). The title is definitely not reductive – the scope is not intended to be narrow. Indeed, Human and Machine Communication is a vast area where new technologies are constantly introduced – more so today than before, thanks to the rapid progress of Artificial Intelligence. It is also a vast business area where new products, services, and applications are launched by the day,…

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A new brick for the MPAI architecture

At its 43rd General Assembly of 2024 April17, MPAI approved the publication of the draft AI Module Profiles (MPAI-PRF) standard with a request for Community Comments. The scope of MPAI-PRF is to provide a means to identify AI Modules with the same functionality but with different features. First two words about AIMs. MPAI develops application-oriented standards for applications that MPAI calls AI Workflows (AIW) that can be broken down into components called AI Modules. AIWs are specified by what they…

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An overview of AI Framework (MPAI-AIF)

From its early days, MPAI realised that AI-based data coding standards could facilitate AI explainability if monolithic AI applications could be broken down to individual components with identified functionality processing and producing data with semantics known as far as possible. An important side effect of this approach was identified in the possibility for developers to provide components with standard interfaces and potentially better performance than that provided by other developers. Version 1 of AI Framework (MPAI-AIF) published in September 2021…

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What is the AI for Health Call for Technologies about?

AI for Health (MPAI-AIH) is a project addressing interfaces and data types involved in an AIH Platform where End Users acquire and process health data on their handsets equipped with an AI Framework executing AI Workflows enabled by models distributed by the AIH Back end and installed in their handsets (AIH Front ends). Figure 1 depicts the AIH Front end. Figure 1 – The AIH Front-End End Users upload their processed health data with associated Smart Contracts granting the AIH…

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