This is what Aïcha – a fictional Master's student in AI – and her friend Félix discover in Utop'IA an educational (French language) graphic novel developed in association with author and illustrator Herji as part of a project initiated by LEARN. "Exploring AI through an environmental lens brings its physical, tangible side into sharp focus," says Sonia Agrebi, an expert in digital sociology and a LEARN projects manager. "Utop'IA examines how AI can make both a positive and negative impact on the environment. As a society, we use AI without realizing the repercussions. Our aim isn't to moralize or point the finger of blame, but rather to challenge perceptions and explain concepts to raise awareness of the issues surrounding AI." Utop'IA is backed by solid scientific reasoning and evidence, since every detail was reviewed by a committee of EPFL experts in AI, sustainability and learning science. "AI is playing an increasingly important role in our everyday lives, but I find it alarming that so little is said about its environmental impact. Utop'IA offers digestible insights into this complex subject."

People have been asking me what is the current development state of Owl (a numerical library in OCaml). Well, I think it is about time to show what Owl can actually do at the moment with its newly added AD (Algorithmic Differentiation) module. I will demonstrate how to build a small two-layer neural network from scratch in order to learn the hand-written digits in MNIST dataset. The following code snippet defines a simple two-layer neural network with tanh and softmax as the activation function for the first and second layer respectively. Remember to open Owl and Algodiff.AD modules. Defining a network seems trivial, but how about the core component in all neural networks: back propagation?