Data Science at Home
Episodes

Tuesday Jan 08, 2019
Episode 51: Decentralized machine learning in the data marketplace (part 2)
Tuesday Jan 08, 2019
Tuesday Jan 08, 2019
In this episode I am completing the explanation about the integration fitchain-oceanprotocol that allows secure on-premise compute to operate in the decentralized data marketplace designed by Ocean Protocol.
As mentioned in the show, this is a picture that provides a 10000-feet view of the integration.
I hope you enjoy the show!

Wednesday Dec 26, 2018
Episode 50: Decentralized machine learning in the data marketplace
Wednesday Dec 26, 2018
Wednesday Dec 26, 2018
In this episode I briefly explain how two massive technologies have been merged in 2018 (work in progress :) - one providing secure machine learning on isolated data, the other implementing a decentralized data marketplace.
In this episode I explain:
How do we make machine learning decentralized and secure?
How can data owners keep their data private?
How can we benefit from blockchain technology for AI and machine learning?
I hope you enjoy the show!
References
fitchain.io decentralized machine learnin
Ocean protocol decentralized data marketplace

Wednesday Dec 19, 2018
Episode 49: The promises of Artificial Intelligence
Wednesday Dec 19, 2018
Wednesday Dec 19, 2018
It's always good to put in perspective all the findings in AI, in order to clear some of the most common misunderstandings and promises. In this episode I make a list of some of the most misleading statements about what artificial intelligence can achieve in the near future.

Sunday Oct 21, 2018
Episode 48: Coffee, Machine Learning and Blockchain
Sunday Oct 21, 2018
Sunday Oct 21, 2018
In this episode - which I advise to consume at night, in a quite place - I speak about private machine learning and blockchain, while I sip a cup of coffee in my home office.There are several reasons why I believe we should start thinking about private machine learning...It doesn't really matter what approach becomes successful and gets adopted, as long as it makes private machine learning possible. If people own their data, they should also own the by-product of such data.
Decentralized machine learning makes this scenario possible.