podcast

Today’s episode  will be about deep learning and compression of data, and in particular compressing images. We all know how important compressing data is, reducing the size of digital objects without affecting the quality. As a very general rule, the mor...

In this episode I explain the differences between L1 and L2 regularization that you can find in function minimization in basically any machine learning model.  

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 blockcha...

This is the first part of the amazing episode with Johannes Castner, CEO and founder of CollectiWise. Johannes is finishing his PhD in Sustainable Development from Columbia University in New York City, and he is building a platform for collective intelli...

Predicting the weather is one of the most challenging tasks in machine learning due to the fact that physical phenomena are dynamic and riche of events. Moreover, most of traditional approaches to climate forecast are computationally prohibitive. It seem...

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 c...

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 w...

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 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 enti...