Bridging the gap between data science and data engineering: metrics (Ep. 95)
Data science and data engineering are usually two different departments in organisations. Bridging the gap between the two is essential to success. Many times the brilliant applications created by data scientists don't find a match in production, just be...
A big welcome to Pryml: faster machine learning applications to production
Why so much silence? Building a company! That's why :) I am building pryml, a platform that allows data scientists build their applications on data they cannot get access to. This is the first of a series of episodes in which I will speak about the techn...
It’s cold outside. Let’s speak about AI winter (Ep. 93)
In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot o...
The dark side of AI: bias in the machine (Ep. 92)
This is the fourth and last episode of mini series "The dark side of AI". I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the machine C: Francesco, today we are starting with an infuriating ...
The dark side of AI: metadata and the death of privacy (Ep. 91)
Get in touch with us Join the discussion about data science, machine learning and artificial intelligence on our Discord server Episode transcript We always hear the word “metadata”, usually in a sentence that goes like this Your Honor, ...
The dark side of AI: recommend and manipulate (Ep. 90)
In 2017 a research group at the University of Washington did a study on the Black Lives Matter movement on Twitter. They constructed what they call a “shared audience graph” to analyse the different groups of audiences participating in the debate, and fo...
The dark side of AI: social media and the optimization of addiction
Chamath Palihapitiya, former Vice President of User Growth at Facebook, was giving a talk at Stanford University, when he said this: “I feel tremendous guilt. The short-term, dopamine-driven feedback loops that we have created are destroying how society ...
3 best solutions to improve training stability of GANs (Ep. 88)
Generative Adversarial Networks or GANs are very powerful tools to generate data. However, training a GAN is not easy. More specifically, GANs suffer of three major issues such as instability of the training procedure, mode collapse and vanishing gradien...
