Gabriele Randelli
HPE
Gabriele Randelli è Senior Machine Learning Solution Architect presso Hewlett Packard Enterprise, dove progetta soluzioni intelligenti basati su algoritmi di machine learning per il mercato delle telecomunicazioni, ed in particolar modo allinterno dei settori BSS e Actionable Customer Intelligence.
Oltre ad un passato imprenditoriale e come Chief Technology Officer, avendo fondato la start-up Smart-I S.r.l., allinterno del suo percorso professionale ha prevalentemente svolto il ruolo di R&D Engineer, nel contesto dellintelligenza artificiale e della robotica applicate ai settori difesa, ricerca ed applicazioni spaziali.
Ha conseguito un Post-Doc ed un PhD in robotica ed intelligenza artificiale presso Sapienza Università di Roma. I suoi principali interessi includono: human-robot interaction technologies, sistemi cognitivi robotici e machine learning. E autore di oltre 20 articoli pubblicati su riviste, conferenze e libri di rilievo internazionale.
Speeches di Gabriele Randelli
AI in Telecom: how artificial intelligence is reshaping the vision of telco industry
In the past decades telecom operator have mainly focused their business models in enabling fast telecommunications (e.g. fiber) and enriching their subscriber base, without any tangible interest into the content of the transferred data, nor into their customers usage of such services.
On the converse, so-called over-the-top (OTT) providers, Netflix, Skype, Google, have significantly increased their revenues introducing innovative customer-based services deployed over the network infrastructure (e.g. movie streaming), while leaving the operators with all the costs involved in maintaining their networks.
In order to reduce the huge gap between revenues and costs, telecom operator must approach their clients with the same customer-centric mind-set. Data flowing through their networks must be analysed in real-time to extract complex behaviour and usage pattern, and this requires a novel set of artificial intelligence techniques that can leverage on this huge amount of information and scale horizontally across network computation elements, such as deep learning.
This talk presents how telecom operators are shifting towards a subscriber-centric network paradigm, by leveraging on artificial intelligence algorithms to extract meaningful customer insights in real time from the huge amount of data collected. Such a knowledge in turn enables smarter services, such as: proactive customer support, customized marketing campaigns, self-healing networks, automated network optimization.
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