Data Science at Home

Founder Interview – Francesco Gadaleta of Fitchain

Cross-posting from Cryptoradio.io
Overview
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.
Roadmap
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.
 

Episode 31: The End of Privacy

Data is a complex topic, not only related to machine learning algorithms, but also and especially to privacy and security of individuals, the same individuals who create such data just by using the many mobile apps and services that characterize their digital life.
In this episode I am together with B.J.n Mendelson, author of “Social Media is Bullshit” from St. Martin’s Press and world-renowned speaker on issues involving the myths and realities involving today’s Internet platforms.  B.J. has a new a book about privacy and sent me a free copy of "Privacy, and how to get it back" that I read in just one day. That was enough to realise how much we have in common when it comes to data and data collection.
 

Episode 30: Neural networks and genetic evolution: an unfeasible approach

Despite what researchers claim about genetic evolution, in this episode we give a realistic view of the field.

Episode 29: Fail your AI company in 9 steps

In order to succeed with artificial intelligence, it is better to know how to fail first. It is easier than you think.Here are 9 easy steps to fail your AI startup.

Episode 28: Towards Artificial General Intelligence: preliminary talk

The enthusiasm for artificial intelligence is raising some concerns especially with respect to some ventured conclusions about what AI can really do and what its direct descendent, artificial general intelligence would be capable of doing in the immediate future. From stealing jobs, to exterminating the entire human race, the creativity (of some) seems to have no limits. In this episode I make sure that everyone comes back to reality - which might sound less exciting than Hollywood but definitely more... real. 

Episode 27: Techstars accelerator and the culture of fireflies

In the aftermath of the Barclays Accelerator, powered by Techstars experience, one of the most innovative and influential startup accelerators in the world, I’d like to give back to the community lessons learned, including the need for confidence, soft-skills, and efficiency, to be applied to startups that deal with artificial intelligence and data science.In this episode I also share some thoughts about the culture of fireflies in modern and dynamic organisations.

Episode 26: Deep Learning and Alzheimer

In this episode I speak about Deep Learning technology applied to Alzheimer disorder prediction. I had a great chat with Saman Sarraf, machine learning engineer at Konica Minolta, former lab manager at the Rotman Research Institute at Baycrest, University of Toronto and author of DeepAD: Alzheimer′ s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI.
I hope you enjoy the show.

Episode 25: How to become data scientist [RB]

In this episode, I speak about the requirements and the skills to become data scientist and join an amazing community that is changing the world with data analyticsa

Episode 24: How to handle imbalanced datasets

In machine learning and data science in general it is very common to deal at some point with imbalanced datasets and class distributions. This is the typical case where the number of observations that belong to one class is significantly lower than those belonging to the other classes.  Actually this happens all the time, in several domains, from finance, to healthcare to social media, just to name a few I have personally worked with. Think about a bank detecting fraudulent transactions among millions or billions of daily operations, or equivalently in healthcare for the identification of rare disorders. In genetics but also with clinical lab tests this is a normal scenario, in which, fortunately there are very few patients affected by a disorder and therefore very few cases wrt the large pool of healthy patients (or not affected). There is no algorithm that can take into account the class distribution or the amount of observations in each class, if it is not explicitly designed to handle such situations. In this episode I speak about some effective techniques to handle imbalanced datasets, advising the right method, or the most appropriate one to the right dataset or problem.
In this episode I explain how to deal with such common and challenging scenarios.

Episode 23: Why do ensemble methods work?

Ensemble methods have been designed to improve the performance of the single model, when the single model is not very accurate. According to the general definition of ensembling, it consists in building a number of single classifiers and then combining or aggregating their predictions into one classifier that is usually stronger than the single one.
The key idea behind ensembling is that some models will do well when they model certain aspects of the data while others will do well in modelling other aspects. In this episode I show with a numeric example why and when ensemble methods work.