In the last article, we focused on VAEs and GANs for image generation. This time, we’ll talk about analyzing images and trying to identify classes. We will also take this opportunity to talk about the usual traps and limits of AI classification.
To start off with this series of articles on the AI & Art project I did in collaboration with Cali Rezo, we’ll discuss some common generative models and how we applied them to her artwork to create new images in a “Cali-like” style.
Nowadays, machine learning (ML) is a red-hot topic which is discussed everywhere in various ways. More and more companies are relying on AI as part of their production process, be it in the domain of finance, medicine, management, art… This last application of ML algorithms, in particular, is really interesting to me. And, since the great abstract painter Cali Rezo shares this interest, we decided we would collaborate on a project to study how to apply AI to art.
Last summer, I did a 2-month internship for the French startup HERETIC which has been fighting scams on the Internet for several years now. They offered me an opportunity to test my AI-engineer skills on a practical problem: how can we use machine learning to detect how fraudulent an email or a website address looks?
Last September, I had the chance to participate in an interview for the “Guide de l’Ingénieur” (i.e.: the Engineer’s guide), a special edition of the French magazine L’Usine Nouvelle that focuses on science and technology. Thanks to the journalist Christophe Bys, 3 other engineering students and I were offered the opportunity to meet with the leader of BCG Gamma, Sylvain Duranton, and ask him a few questions.
A few months ago, I collaborated with the abstract painter Cali Rezo on a 1′ video that showed her Live Painting session at the Paul Stewart Gallery. It was a very interesting project that taught me a lot about video editing and I wanted to keep on learning about this media.