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 Fitchain.io 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.
RoadmapFitchain 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.
Episode 28: Towards Artificial General Intelligence: preliminary talk
Episode 27: Techstars accelerator and the culture of fireflies
Episode 26: Deep Learning and Alzheimer
Episode 25: How to become data scientist [RB]
Episode 24: How to handle imbalanced datasets
Episode 23: Why do ensemble methods work?
Episode 22: Parallelising and distributing Deep Learning
Episode 21: Additional optimisation strategies for deep learning
Episode 20: How to master optimisation in deep learning
Episode 19: How to completely change your data analytics strategy with deep learning
Episode 18: Machines that learn like humans
Episode 17: Protecting privacy and confidentiality in data and communications
Episode 16: 2017 Predictions in Data Science
Episode 15: Statistical analysis of phenomena that smell like chaos
Episode 14: The minimum required by a data scientist
Episode 13: Data Science and Fraud Detection at iZettle
Episode 12: EU Regulations and the rise of Data Hijackers
Episode 11: Representative Subsets For Big Data Learning
Episode 10: History and applications of Deep Learning
Episode 9: Markov Chain Montecarlo with full conditionals
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