With the printing industry sparing no efforts publishing books on Artificial Intelligence (AI), why should there be another that, in its title and subtitle, combines the overused words AI and trustworthy, with the alien words standards and pervasive? The answer is that the book describes a solution that covers all the elements of the title: to effectively combine the AI and trustworthy words, but also to make AI pervasive. How? By developing standards for AI-based data coding. Many industries needed standards to run their business and used to have high respect for them. The MP3 standard put users in control of the content they wanted to enjoy, and the television – and now the video – experiences have little to do with how users approached audio-visual content some 30 years ago. At that time, the media industry was loath to invest in open standards. The successful MPEG standards development model, however, changed its attitude. Similarly, the AI industry has been slow in developing AI-based data coding standards making proprietary solutions their preferred route. This book provides the full description of mission, achievements and plans of the Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) standards developing organisation. It describes how MPAI develops standards that can also be used, how standards can make AI pervasive and promote innovation, how MPAI gives users the means to make informed decisions about how to choose a standard implementation having the required level of trustworthiness. MPAI is a unique adventure open to those who want to make the MPAI vision real.
Towards Pervasive and Trustworthy Artificial Intelligence: How standards can put a great technology at the service of humankind / Artusi, Alessandro; Basso, Andrea; Bosi, Marina; Canazza, Sergio; Chiariglione, Leonardo; Choi, Miran; Columbano, Fabiano; Burkay Çöteli, Mert; Dalla Pozza, Nadir; Dini, Roberto; Guarise, Michelangelo; Hacıhabiboğlu, Hüseyin; Iacoviello, Roberto; Jia, Chuanmin; Kang, Jisu; Kudumakis, Panos; Lazzaroli, Valeria; Mazzaglia, Marco; Perboli, Guido; Pretto, Niccolò; Ribeca, Paolo; Rosano, Mariangela; Seligman, Mark. - STAMPA. - (2021), pp. 1-110.
Towards Pervasive and Trustworthy Artificial Intelligence: How standards can put a great technology at the service of humankind
Guido Perboli;Mariangela Rosano;
2021
Abstract
With the printing industry sparing no efforts publishing books on Artificial Intelligence (AI), why should there be another that, in its title and subtitle, combines the overused words AI and trustworthy, with the alien words standards and pervasive? The answer is that the book describes a solution that covers all the elements of the title: to effectively combine the AI and trustworthy words, but also to make AI pervasive. How? By developing standards for AI-based data coding. Many industries needed standards to run their business and used to have high respect for them. The MP3 standard put users in control of the content they wanted to enjoy, and the television – and now the video – experiences have little to do with how users approached audio-visual content some 30 years ago. At that time, the media industry was loath to invest in open standards. The successful MPEG standards development model, however, changed its attitude. Similarly, the AI industry has been slow in developing AI-based data coding standards making proprietary solutions their preferred route. This book provides the full description of mission, achievements and plans of the Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) standards developing organisation. It describes how MPAI develops standards that can also be used, how standards can make AI pervasive and promote innovation, how MPAI gives users the means to make informed decisions about how to choose a standard implementation having the required level of trustworthiness. MPAI is a unique adventure open to those who want to make the MPAI vision real.File | Dimensione | Formato | |
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2021-MPAI-Towards Pervasive and Trustworthy Artificial Intelligence.pdf
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https://hdl.handle.net/11583/2947052