Archive for the 'blockchain' Category

In this episode I have a wonderful conversation with Chris Skinner.

Chris and I recently got in touch at The banking scene 2019, fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”.

After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regulation and technology in finance.

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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!

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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 decentralized machine learnin

Ocean protocol decentralized data marketplace

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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.

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In the second part of this episode I am interviewing Johannes Castner from CollectiWise, a platform for collective intelligence.
I am moving the conversation towards the more practical aspects of the project, asking about the centralised AGI and blockchain components that are essential part of the platform.



    Thaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture".
    doi:10.2139/ssrn.1583509. SSRN 1583509
  2. Teschner, F., Rothschild, D. & Gimpel, H. Group Decis Negot (2017) 26: 953.
  3. Firas Khatib, Frank DiMaio, Foldit Contenders Group, Foldit Void Crushers Group, Seth Cooper, Maciej Kazmierczyk, Miroslaw Gilski, Szymon Krzywda, Helena Zabranska, Iva Pichova, James Thompson, Zoran Popović, Mariusz Jaskolski & David Baker, Crystal structure of a monomeric retroviral protease solved by protein folding game players, Nature Structural & Molecular Biology volume18, pages1175–1177 (2011)
  4. Rosenthal, Franz; Dawood, Nessim Yosef David (1969). The Muqaddimah : an introduction to history ; in three volumes. 1. Princeton University Press. ISBN 0-691-01754-9.
  5. Kevin J. Boudreau and Karim R. Lakhani, Using the Crowd as an Innovation Partner, April 2013.
  6. Sam Bowles, The Moral Economy: Why Good Incentives are No Substitute for Good Citizens.
    Amartya K. Sen, Rational Fools: A Critique of the Behavioral Foundations of Economic Theory, Philosophy & Public Affairs, Vol. 6, No. 4 (Summer, 1977), pp. 317-344, Published by: Wiley, Stable URL:

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Humans seem to have reached a cross-point, where they are asked to choose between functionality and privacy. But not both. Not both at all. No data, no service. That’s what companies building personal finance services say. The same applies to marketing companies, social media companies, search engine companies, and healthcare institutions.

In this episode I speak about the reasons to aggregate data for precision medicine, the consequences of such strategies and how can researchers and organizations provide services to individuals while respecting their privacy.


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Attacking deep learning models

Compromising AI for fun and profit


Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.

In this episode, we explain how machine learning models can be attacked and what we can do to protect intelligent systems from being  compromised.

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In the attempt of democratizing machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model that is privately trained should be verified and uniquely identified across its entire life cycle, from its random initialization to setting the optimal values of its parameters.
How does blockchain allow all this? Fitchain is the decentralized machine learning platform that provides models an identity and a certification of their training procedure, the proof-of-train

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I know, I have been away too long without publishing much in the last 3 months.
But, there's a reason for that. I have been building a platform that combines machine learning with blockchain technology.
Let me introduce you to fitchain and tell you more in this episode.

If you want to collaborate on the project or just think it's interesting, drop me a line on the contact page at

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Cross-posting from


Francesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions.Francesco Gadaleta is the founder of and senior advisor to Abe AI. Fitchain is a platform that officially started in October 2017, which allows data scientists to write machine learning models on data they cannot see and access due to restrictions imposed in healthcare or financial environments. In the Fitchain platform, there are two actors, the data owner and the data scientist. They both run the Fitchain POD, which orchestrates the relationship between these two sides. The idea behind Fitchain is summarized in the thesis “do not move the data, move the model – bring the model where the data is stored.”

The Fitchain team has also coined a new term called “proof of train” – a way to guarantee that the model is truly trained at the organization, and that it becomes traceable on the blockchain. To develop the complex technological aspects of the platform, Fitchain has partnered up with BigChainDB, the project we have recently featured on Crypto Radio.


Fitchain team is currently validating the assumptions and increasing the security of the platform. In the next few months, they will extend the portfolio of machine learning libraries and are planning to move from a B2B product towards a Fitchain for consumers.

By June 2018 they plan to start the Internet of PODs. They will also design the Fitchain token – FitCoin, which will be a utility token to enable operating on the Fitchain platform.


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