podcast

In this episode I speak about how important reproducible machine learning pipelines are. When you are collaborating with diverse teams, several tasks will be distributed among different individuals. Everyone will have good reasons to change parts of your...

Have you ever wanted to get an estimate of the uncertainty of your neural network? Clearly Bayesian modelling provides a solid framework to estimate uncertainty by design. However, there are many realistic cases in which Bayesian sampling is not really a...

The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific conditions a well trained machine learning model can gene...

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 th...

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...

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 achiev...

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 m...

Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer scienc...

In this episode I continue the conversation from the previous one, about failing machine learning models. When data scientists have access to the distributions of training and testing datasets it becomes relatively easy to assess if a model will perform ...