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      "body": "https://www.youtube.com/watch?v=4PfkduraAfc\n\n#####Video Introduttivo del Protocollo DML#####\n\nIl Decentralized Machine Learning (Apprendimento automatico decentralizzato) consente di liberare la potenzialità di dati privati non sfruttati, potenza di elaborazione inattiva e sviluppo di algoritmi crowdsourced attraverso il machine learning on-device, su dispositivo, ed attraverso la blockchain e le tecnologie di federated learning.\n\nDa AlphaGo, possiamo già vedere quanto potente e ampio possa essere il potenziale del machine learning. E se ci fosse un modo per coinvolgere potenzialmente miliardi di dispositivi e decine di migliaia di sviluppatori per rendere il futuro apprendimento automatico ancora più potente, ma decentralizzato? Il Protocollo DML è la chiave per questa rivoluzione.\n\n#Il mercato del Machine learning è enorme, ma incontra tre grandi limiti#\n\nL’International Data Corporation prevede che le entrate del settore raggiungeranno oltre 210 miliardi di dollari nel 2020. I giganti della tecnologia hanno investito molto e ottenuto notevoli risultati nell’apprendimento automatico. Tuttavia, lo sviluppo attuale dell’apprendimento automatico è ostacolato da:\n\n##Inaccessibilità dei Dati Privati##\n\nIl tradizionale machine learning richiede che i dataset vengano caricati su un server dedicato. A causa di problemi di privacy, l’enorme quantità di dati privati archiviati nei singoli dispositivi non è sfruttata.\n\n##Centralizzazione della Potenza di Elaborazione##\n\nOggigiorno, l’apprendimento automatico viene condotto principalmente attraverso un computer centralizzato, la cui potenza di elaborazione è solitamente limitata o confinata ai processori di una singola macchina.\n\n##Limitazione dello Sviluppo di Modelli e Algoritmi##\n\nSolo le grandi aziende possono permettersi di investire enormi capitali e risorse iniziali per costruire modelli e algoritmi in-house di apprendimento automatico, o di acquisire strumenti personalizzati dalle società di consulenza per applicare il machine learning nella propria attività.\n\n##DML Migliora lo Sviluppo del Machine Learning Restituendo Energia a Tutti i Contributori dell’Ecosistema##\n\nAbbiamo creato un’infrastruttura open source e una rete in cui i dati, potenza di elaborazione e sviluppo di modelli / algoritmi saranno tutti decentralizzati.\n\n##Data##\n\nDistribuendo algoritmi crittografati in tutti i singoli dispositivi e conducendo il machine learning a livello locale, non è necessario estrarre o caricare dati privati su un server di terze parti.\n\nTutti i dati privati verranno conservati all’interno dei dispositivi e verranno trasferiti solo i risultati di local prediction. L’utilizzo di dati privati non sfruttati viene liberato di conseguenza.\n\n##Potenza di calcolo##\n\nDML Procotol utilizzerà la potenza di elaborazione inattiva dei dispositivi per eseguire il machine learning on-device, sul dispositivo.\n\n##Algoritmi##\n\nL’offerta di algoritmi sarà affidata al crowdsourcing nella nostra comunità di sviluppatori. Clienti come aziende, istituti di ricerca, governi o ONG possono semplicemente cercare o richiedere algoritmi di machine learning adatti a loro nel marketplace DML Algorithms.\n\nAl fine di promuovere lo sviluppo dell’apprendimento automatico, terremo anche diversi concorsi/ blockathons per attrarre e premiare i talenti della nostra comunità.\n\n##Cosa Abbiamo in Programma?##\n\nNei prossimi articoli, esploreremo ulteriormente le diverse componenti del protocollo Decentralized Machine Learning, inclusi i partecipanti, la funzione dei nodi decentralizzati e gli smart contract della blockchain. Incontrerete poi anche il nostro team e capirete perché stiamo costruendo il protocollo DML.\n\nNel frattempo, Nel frattempo, vi invitiamo ad approfondire visitando il [nostro sito web] (https://decentralizedml.com), leggendo il nostro [whitepaper] (https://decentralizedml.com/DML_whitepaper_31Dec_17.pdf) e guardando il [nostro video introduttivo] (https://www.youtube.com/watch?v=4PfkduraAfc).\n\n##Unisciti alla Community##\n\nAnnunceremo tutte le novità più recenti ed esclusive nella nostra community. Chatta con il team e con la nostra crescente comunità unendoti al [nostro canale Telegram] (https://t.me/decentralizedML).",
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