Multi-objective optimization (MOO) is a field of mathematics dedicated to finding solutions to optimization problems where we have two or more objectives to optimize at the same time, that are usually in concurrence. It can be applied to problems in many domains, be it finance, biology, logistics…Read More »A peek at multi-objective optimization
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.
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…
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?
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 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.
Last time, I talked about cellular automata, and more precisely Conway’s Game Of Life. To continue on this topic, I searched for small applications we can derive from the concept of cellular automaton and I eventually settled for one: a RPG-like map generation algorithm.Read More »A peek at cellular automata (2)
To follow up on evolutive models, I wanted to talk about cellular automata. Broadly speaking, a cellular automaton is a discrete model that is defined by a grid of cells – each in a given state – that changes generation after generation according to one or more evolution rules.