Neural Synesthesia: GAN AI dreaming of music

Xander Steenbrugge shared his latest work on LinkedIn yesterday, and I was completely stunned! Xander had been working on, what he called, a “fun side-project”, but which was in my eyes, absolutely awesome. He had used two generative adversarial networks (GANs) to teach one another how to respond visually to changing audio cues. This resulted…

ArchiGAN: Designing buildings with reinforcement learning

I’ve seen some uses of reinforcement learning and generative algorithms for architectural purposes already, like these evolving blueprints for school floorplans. However, this new application called ArchiGAN blew me away! ArchiGAN (try here) was made by Stanislas Chaillou as a Harvard master’s thesis project. The program functions in three steps: building footprint massing program repartition…

Beating Battleships with Algorithms and AI

Past days, I discovered this series of blogs on how to win the classic game of Battleships (gameplay explanation) using different algorithmic approaches. I thought they might amuse you as well : ) The story starts with this 2012 Datagenetics blog where Nick Berry constrasts four algorithms’ performance in the game of Battleships. The resulting levels…

Open Source Visual Inspector for Neuroevolution (VINE)

In optimizing their transportation services, Uber uses evolutionary strategies and genetic algorithms to train deep neural networks through reinforcement learning. A lot of difficult words in one sentence; you can imagine the complexity of the process. Because it is particularly difficult to observe the underlying dynamics of this learning process in neural network optimization, Uber…

Libratus: A Texas Hold-Em Poker AI

Four of the best professional poker players in the world – Dong Kim, Jason Les, Jimmy Chou, and Daniel McAulay – recently got beat by Libratus, a poker-playing AI developed at the Pittsburgh Supercomputing Center. During a period of 20 days of continuous play (10h/day), each of these four professionals lost to Libratus heads-up in…

Video: Human-Computer Interactions in Reinforcement Learning

Reinforcement learning is an area of machine learning inspired by behavioral psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward (Wikipedia, 2017). Normally, reinforcement learning occurs autonomously. Here, algorithms will seek to minimize/maximize a score that is estimated via predefined constraints. As such, algorithms can thus learn to perform the most effective actions (those that…