Have you noticed that our rhetoric surrounding the epidemic is still concentrated on "going back" rather than "moving forward"? "During the pandemic, many people felt their lives had been thrown off course. So understandably, people desire to get back on track. However, much of the transformation during and after the pandemic has been positive. Might we think about it as "moving forward?" Author Heather McGowan's new book, The Empathy Advantage: Leading the Empowered Workforce, co-written with Chris Shipley, points out many of the ways we've changed for the good--moving forward--since the pandemic. But there is still a way to go. Once Heather pointed out the "going back" language during our interview, I couldn't help but notice that it is present in many conversations regarding the future of work. So many leaders are asking how to get things back to the way they once were rather than asking how to harness the change to achieve greater things. Insert almost any hot topic, be it generational differences in career priorities, gender norms, or attitudes toward how work fits into our lives. You'll see as many people pushing back on the "return" language as you do pushing for "moving forward" change. As Heather says, "You can't put the toothpaste back into the tube now." Gender norms are something applicable to all organizations when it comes to the future of work. When surveyed, Millennials and Gen Z say you shouldn't have fixed, exclusionary gender markers in your language, in your restrooms, in your customer offerings."

Research being carried out by a research team around Professor Ohbyung Kwon at Kyung Hee University and Dr Christine (Eunyoung) Sung at Jake Jabs College of Business and Entrepreneurship, Montana State University, involves examining consumers' evaluations of fashion products designed using generative adversarial networks (GANs), an Artificial Intelligence (AI) technology. They analyse consumers' buying behaviour and offer practical advice for businesses that are considering using GANs to develop products for the retail fashion market. Artificial Intelligence (AI) technology is changing the retail landscape. Generative AI is being used to produce creative outputs; tasks that have traditionally been considered exclusive to humans. In particular, generative adversarial networks (GANs), an Artificial Intelligence technology, powerful machine learning models that can generate realistic images, videos, and voice outputs, are successfully performing creative tasks previously considered unique to humans.

Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses.

As the festive season reaches full-swing, I want to reflect on AI's contributions to 2019. I'll do this by rounding up and linking to the most practical advice that can be applied to businesses, organizations, and governments across the world in the New Year. Recent research from Microsoft has revealed a direct correlation between the use of AI technologies and an organization's ability to enhance and retain its competitive edge. So, without further ado, here's a round-up of seven of the best pieces of journalism that focused on AI's problems, advantages, and opportunities in 2019. Do you want to adopt AI for your business? Have you started preparing, but have no idea how to progress?

It's time to Get Good with Tyler Blevins, a.k.a. That's the title of his new book -- subtitled My Ultimate Guide to Gaming -- which promises to divulge Ninja's "secrets to become unstoppable." Unlike typical celebrity hardbacks, Blevins' book is light on drama and full of practical advice. Ninja walks readers through the ins and outs of building a video game streaming career (starting with buying the right equipment) and ending with how to manage the stress that comes with having millions of fans. Not everyone can be Ninja.

Sigo publicando Machine Learning Yearning: AI is the new electricity Machine Learning Yearning This week's chapters teach you how to read learning curves. I also give practical advice on how to plot learning curves when your dataset is very small or very large. Read Chapters 31-32 Pun of the Week If you have a great AI pun, tweet it to me @AndrewYNg using #AIpun. You are receiving this because you opted in to receiving emails about Andrew's upcoming book. This week's chapters teach you how to read learning curves.

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

Sprinkled throughout Andrew Ng's machine learning class is a lot of practical advice for applying machine learning. That's what I'm trying to compile and summarize here. The key is dividing data into training, cross-validation and test sets. The test set is used only to evaluate performance, not to train parameters or select a model representation. The rationale for this is that training set error is not a good predictor of how well your hypothesis will generalize to new examples.

MLconf in Seattle is a week away and we are getting a glimpse. Ethics in machine learning is the hottest conversation right now. Hear how a quantum molecular dynamic model made Uber service more reliable, get practical advice on next revolution in text search, and learn about multi-classification evaluation and ensemble learning. Franziska Bell, Data Science Manager at Uber, will talk about how a quantum molecular dynamic model for enzymes made Uber more reliable as a service. And of course, you don't need to be a quantum mechanics to be a data scientist.