"I think, initially, Ubisoft approached me because of my electronic music background – my live career, my albums, my touring. But I didn't know if I was the right person for the job, you know?" Composer Brendan Angelides has never worked in video game music before. You might know him better as Eskmo or Welder, or perhaps as the mind behind the music of TV shows 13 Reasons Why or Billions. When Ubisoft approached him to be the composer for its sort-of reboot of the Assassin's Creed franchise, Mirage, he had doubts. The game is set at the height of the Islamic golden age, and centres around Baghdad: a hub flowing with the lifeblood of a changing world, a cultural centre of art and science, old and new.

Without the ability to estimate and benchmark AI capability advancements, organizations are left to respond to each change reactively, impeding their ability to build viable mid and long-term strategies. This paper explores the recent growth of forecasting, a political science tool that uses explicit assumptions and quantitative estimation that leads to improved prediction accuracy. Done at the collective level, forecasting can identify and verify talent, enable leaders to build better models of AI advancements and improve inputs into design policy. Successful approaches to forecasting and case studies are examined, revealing a subclass of "superforecasters" who outperform 98% of the population and whose insights will be most reliable. Finally, techniques behind successful forecasting are outlined, including Phillip Tetlock's "Ten Commandments." To adapt to a quickly changing technology landscape, designers and policymakers should consider forecasting as a first line of defense.

Would Machine Learning be Baroque because it takes a different perspective with a technological medium? Would Machine Learning be more than just algorithms and lines of code, but also drama and emotion, as was characteristic in artists during the Baroque period? But most importantly, how do we define what is valuable about art and beauty in this 21st century where AI can create things like never before?

As AI begins to play a much larger role in our daily lives, informing healthcare decisions, making recommendations, helping us resolve customer service issues, talking with us as companion bots, making financial decisions, driving autonomous cars, and helping employees make more informed, faster decisions, it becomes more important that ethics and morality are built into AI applications. AI applications are making decisions that affect people's privacy, health, finances, jobs, criminal justice, safety, and overall happiness. Ethical AI is no longer an afterthought -- it must be built into the fabric of AI from this point forward. This article will look at the ways that ethics and diversity are being built into AI and the importance of doing so. To ensure that AI is ethical, it must be transparent and explainable.

For chief data officers and data scientists, the business case for DataOps can be obvious. DataOps, correctly done, can streamline data workflows, reduce errors, and offers transparency to the entire data operations. It improves efficiency, increases data trust, and gives more time to do analysis. For business executives, such benefits are not immediately apparent. So, getting the budget to build your DataOps can run into snags -- right up until a business problem challenges your company's core value proposition. That's what happened for Screenrights.

In an era of constant change, companies' data and analytics capabilities must rapidly adapt to ensure that the business survives, never mind competes. Organizations seek insights from their data to inform strategic priorities in real time, yet much of the historical data and modeling formerly applied to predict future behavior and guide actions are proving to be far less predictive, or even irrelevant, in our current normal with COVID-19. In order to survive through crises, proactively detect trends, and respond to new challenges, companies need to develop greater analytical agility. This agility comes from three areas: improving the quality and connections of the data itself, augmenting analytical "horsepower" at the organization level, and leveraging talent that is capable of bridging business needs with analytics to find opportunity in the data. Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations. The quest for better data is not new, but the cost of not having it is easier to substantiate and understand in a time of crisis.

Welcome to the art and science of machine learning. In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. Once familiar with the knobs and levers, otherwise known as hyperparameters, you will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.

You want to learn Machine Learning but have no idea how? Well, before you embark on your epic journey into machine learning, there are some important theoretical and statistical principles you should know first. And that's where this book comes in! It is a practical and high-level introduction to Machine Learning for absolute beginners. Machine Learning For Absolute Beginners teaches you everything basic from learning how to download free datasets to the tools and machine learning libraries you will need. Topics like Data scrubbing techniques, Regression analysis, Clustering, Basics of Neural Networks, Bias/Variance, Decision Trees, etc. are also covered. So, if you haven't had that Lion King moment yet, where you proudly gaze on the expanse of ML-like Simba looks over the Pride Lands of Africa, then this is the best book to gently hoist you up and offer you a clear lay of the land.

Sougwen Chung is an internationally renowned multi-disciplinary artist and researcher, whose work explores the dynamics of humans and systems. Chung is a former research fellow at MIT's Media Lab and a pioneer in the field of human-machine collaboration. In 2019, she was selected as the Woman of the Year in Monaco for achievement in the Arts & Sciences. For the uninitiated, what is human-machine collaboration? Sougwen Chung: Human-machine collaboration is a perspective of technology not as a tool, but as a collaborator.

Sougwen Chung is an internationally renowned multi-disciplinary artist and researcher, who uses hand-drawn and technologically-reproduced marks to address the closeness between person-to-person and person-to-machine communication. She is a former research fellow at the M.I.T. Media Lab and current Artist in Residence at Bell Labs and New Museum of Contemporary Art in New York. For the uninitiated, what is human-machine collaboration? Sougwen Chung: Human-machine collaboration is a perspective of technology not as a tool, but as a collaborator. It stems from an understanding that the relationship between humans and their tools have changed.