Programming

AI & Art with Cali Rezo (6): Conclusion & Final Notes

To end this series of articles on the AI & Art project I realized in collaboration with Cali Rezo, today I would like to give you some of my thoughts on this project and on AI in general, plus some info on the tools we used and on Cali’s upcoming events.

AI & Art with Cali Rezo (5): Cali’s Interview

Over the past few weeks, we explored several questions related to AI and its application to art generation or analysis. Before finishing the series, let’s look at things from Cali’s point of view and talk a bit about artists and technology…

AI & Art with Cali Rezo (4): Explainability and uncertainty of AI models

In the third article of the series, we discussed how to apply AI to art analysis. Even if our results were not as conclusive as we’d hoped, they still raised a few questions that we will tackle today: what is really happening in these black box models that are neural networks? And to which extent can we assess how certain a model is of its predictions?

AI & Art with Cali Rezo (3): Analytical models

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.

AI & Art with Cali Rezo (2): Generative models

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.

AI & Art with Cali Rezo (1): Project & Goals

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.

Solving PDEs numerically: the finite element method (3)

In this last article from the FEM series, I present some additional concepts related to the finite element method and further developments. In particular, I focus on finite element analysis, some methods for solving time-dependent problems and optimization ideas.

Solving PDEs numerically: the finite element method (2)

Today, let’s continue with the finite element method and focus on how to implement it on a computer. How do we discretize our domain? How do we actually compute the stuff that’s in our variational formulation – efficiently, that is? How do we store and visualize our solution?

Solving PDEs numerically: the finite element method (1)

The study of partial differential equations is a fascinating field of mathematics with many concrete applications, be it in physics, mechanics, meteorology, medicine, urbanism… Mathematicians model the world as equations to better understand it and, if possible, compute a theoretical solution to a physical problem. However, we can’t always get an analytical solution – for example if the geometry is too complex – and sometimes must rely on numerical methods to get an approximation.

ScamDoc: a free tool to check for scams online

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?

A peek at L-systems

Today, let’s continue with procedural generation. So far, we have seen various tools to generate specific structures, so let’s take a look at another type of generator: L-systems.

A peek at Markov chains

Last time, we introduced the basic concepts of our name generator and we saw how webcrawlers can help us gather information easily and efficiently. Now, it’s time to use this information to actually produce some words! To do so, we will rely on statistics and Markov chains.