As we end 2017, I'm tired of writing "lecturing" blogs about what organizations should be doing to master data monetization in order to power their business models and achieve digital transformation. While the objective of every organization should be to master big data and data science (artificial intelligence, machine learning, deep learning) to drive "data monetization," let's take a breath and have some fun. My recent ankle surgery afforded me the opportunity to binge watch "Game of Thrones." As I watched the impending battle between the White Walkers and humanity, I couldn't help but identify a number of lessons that we can learn from Jon Snow's battle with the leader of the White Walkers…and the power of Valyrian steel! Game of Thrones and data, not exactly two things you think are in harmony, but this is where I find myself.

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data.

Metacreation applies tools and techniques from artificial intelligence, artificial life, and machine learning, themselves often inspired by cognitive and natural science, for creative tasks. Musical Metacreation studies the design and use of these generative tools and theories for music making: discovery and exploration of novel musical styles and content, collaboration between human performers and creative software "partners", and design of systems in gaming and entertainment that dynamically generate or modify music. MUME intents to bring together artists, practitioners, and researchers interested in developing systems that autonomously (or interactively) recognize, learn, represent, compose, generate, complete, accompany, or interpret music. It is our pleasure to announce the double special issue on Musical Metacreation recently published the ACM journal Computer in Entertainment. The double issue begins with an introduction to Musical Metacreation that covers the basic for those who are not familiar with the use of generative systems to partially or completely automate musical tasks.

Over the past weeks I have started implementing basic machine learning algorithms in plain Python (Python 3.6). I created the repository to prepare for technical interviews and review my knowledge on algorithms such as k-means, k-nn, logistic regression, neural networks, etc. Also, I wanted to create a knowledge base of easy-to-understand implementations of these algorithms together with the most important theoretical explanations. Some of you might find these implementations helpful when preparing for interviews, starting to learn about machine learning or reviewing basic ML algorithms. I am still working on the repository, so more algorithms will follow over the next months.

The keynote for last years Nvidia GTC conference made headlines around the world for its video featuring of how omnipresent artificial intelligence might be in the future. Playing behind the video was a soundtrack also composed by an AI, AIVA, a small company from Luxembourg. The team behind AIVA are from a classical music background and trained it on music from composers such as Mozart, Beethoven, and Bach to give it an understanding of musical theory, and the patterns behind it. AIVA is purely a composer, providing the music to play, but not actively performing it. So far, this has meant that international orchestras have played its pieces as well as other computer software, such as music for a computer game.

I've been spending some time looking at the convergence behavior of different neural networks trained on MNIST data and cross-entropy loss. I started by training deeper and deeper networks using sigmoid type activations until the learning efficiency got too low before switching to ReLU activations. After switching to ReLU activations, my network converged without too many problems but I noticed that the learning rates exhibited an interesting pattern. In particular, it takes a complete epoch before the loss begins to fall. My weights and biases are initialized uniformly with weights initialized between -0.1 and 0.1.

The Austin-based company revealed research at SXSW that analyzed over 2,000 film scripts and 25,000 characters from between 1930 and 2018. Using machine learning, sentiment analysis and natural language processing, StoryFit's approach demonstrates new ways to break apart narratives and character personality. The company says they ask over 10,000 queries of every script, focusing on what characters say and how they say it. Analyzing the "Big 5" personality traits, they found that 80 percent of female characters measured scored over 85 percent on agreeableness, while only 27 percent of men scored that highly. Characters like Rey from Star Wars: The Force Awakens, who score low on agreeableness, are outliers among female characters.

CREATIVITY: IT'S AT THE HEART OF WHO WE HUMANS ARE… WE HUMANS ARE SPECIAL, RIGHT? Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece? OVER SOME 40,000 YEARS, HUMAN CREATIVITY HAS EXPLODED – FROM DRAWINGS ON CAVE WALLS THROUGH THE GREAT ART OF CENTURIES TO COME…. NOW, SCIENTISTS -- AND ARTISTS –ARE ASKING CAN A ROBOT TRULY IMAGINE AN ORIGINAL MASTERWORK? COMPUTATIONAL CREATIVITY IS LEADING US TO ASK NEW QUESTIONS ABOUT HUMAN CREATIVITY. IS THIS ESSENTIAL HUMAN TRAIT TRULY UNIQUE? WILL ARTIFICIAL INTELLIGENCE BE A COMPETITOR? OR CAN IT BE A COLLABORATOR, HELPING US TOWARD STILL UNIMAGINED CREATIONS? SCHAEFER: My first guest is a member of Google Brain's Magenta team. He is currently working on neural network models of sound and music and recently produced a synthesizer that designed its own sounds. SCHAEFER: Also with us, is an Assistant professor at the University of Illinois at Urbana Champaign in the Dept. of Electrical and Computer Engineering. He focuses on several surprising creative domains including the culinary arts and fashion and the theoretical foundations of creativity. SCHAEFER: Also with us is an Associate Professor of psychological and brain science at Dartmouth College. He's interested in the neural basis of imagination and in the evolution of human creativity. A former research fellow at MIT's Media lab and artist in residence at Google, please welcome Sougwen Chung.

I'm currently implementing an A3C agent in Tensorflow (Asynchronous Advantage Actor Critic) that plays doom (using vizdoom) and I was thinking about if there is a difference between using CNNs or Capsnets (Capsule Networks), Recently there was a big breakthrough in computer vision with these Capsnets. I know that Capsnets, instead of Convnets, handle the spatial relationship of the features and detecting rotated objects. As a consequence, I wondered if there is an advantage to use Capsnets in a Deep Reinforcement Learning agent?

Reuters is building a tool with artificial intelligence to help journalists in analyzing data, suggest story ideas, and even write more sentences. The tool aims not to replace reporters but instead augment them with a digital data scientist-cum-copywriting assistant. Called Lynx Insight, it has been used by many journalists and will now be rolled out across all the classrooms of Reuters. Reg Chua, executive editor of editorial operations, data and innovation at Reuters, says the aim is to divvy up editorial work into what machines do best and what editorial human staff excels at (such as asking questions, judging importance, understanding context and -- presumably -- drinking excessive amounts of coffee). That differs from previous editorial tech efforts that sought to train an AI to write entire stories in the form of snippets about local sports teams or earthquake warnings.