NEWS ANALYSIS: Storage and data protection have come from far behind the other key components of IT--computing and networking--to catch up in terms of innovation. Chris Preimesberger is Editor of Features and Analysis at eWEEK. Join us for our next #eWEEKchat on March 8: "App Economy or API Economy: Which Is It Going to Be?"

From artificial intelligence to virtual reality, emerging technologies are rewriting the retail playbook at a rapid pace, suggests J. Walter Thompson Intelligence in a new report called Frontier(less) Retail. Launched in collaboration with WWD, the report explores the idea that brands and retailers are increasingly putting innovation at the core of their strategies. This relates to everything from digital integration through to the more future-looking technologies helping to shift their businesses forward. Rebecca Minkoff has boosted sales with smart mirrors in dressing rooms, it notes, while Kate Spade has had a hit with Everpurse, a smartphone-charging handbag. It also attributes the success of Under Armour in part to its positioning as a tech-forward brand, and references Topshop's new incubator program, Top Pitch, as a clever bid to achieve the same at a time when its young consumer base is more likely to spend on smartphones than splurge on streetwear. Within all this, however, it is keeping abreast of change that is proving one of the industry's biggest challenges.

The Luddites feared machinery taking workers' jobs. Machines replacing jobs is only a negative prospect if there's no contingency plan. Otherwise, it should be pursued with all available powers. Have you heard the Word of Vyrd? John Henry Vyrd told us that our machines are awakening, and that the greatest wealth mankind has ever know will soon be afforded to us if only we were to take the next step.

Data Science in Python: Pandas Cheat Sheet -- This cheat sheet, along with explanations, was first published on DataCamp. Click on the picture to zoom in. To view other cheat sheets (Python, R, Machine Learning, Probability, Visualizations, Deel Learning, Data Science, and so on) click here. To read the article, click here. Will Trump Kill Statistician's Jobs? -- Today Trump met with leaders of pharmaceutical companies, to discuss "astronomical" drug prices and reduce regulations, so that drug companies can still make hefty profits while charging less for drugs.

Summary: In our recent article on "5 Types of Recommenders" we failed to mention Indicator-Based Recommenders. These have some unique features and ease of implementation that may be important in your selection of a recommender strategy. A few weeks ago in the midst of our series on recommenders we published an article "5 Types of Recommenders" in which we offered our view on the primary types of recommendation engines. We got a very nice comment from Ted Dunning suggesting that we'd missed an important one, Indicator-Based Recommenders. If you ever get a note from Ted Dunning you should pay attention.

This blog post will demonstrate how deep reinforcement learning (deep q learning) can be implemented and applied to play a CartPole game using Keras and Gym, in only 78 lines of code! I'll explain everything without requiring any prerequisite knowledge about reinforcement learning.

Imagine reliably asking Amazon Alexa, Amazon Echo Dot, Google Home, or a chatbot to run analytics queries against a big data platform. For example, "What were the top three revenue generating products last week?" Big Data at the tip of your tongue -- pun intended. The concept of conversing with a computer is very interesting and has been around for a while -- think Star Trek's "LCARS" and Hal from "A Space Odyssey". While we might be a long way off from those realities, recent advancements from Amazon, Google, Microsoft, IBM and other natural language and AI technologies have brought us closer.

For years, marketing was considered more art than science. But more recently, as marketing automation software has proliferated, marketers have had to blend the art of storytelling with the science of data. Then along comes artificial intelligence (AI) and machine learning, which promise to help marketers make sense of all that data. Some experts believe AI's impact on marketing will be hugely significant, that it could even change the nature of marketing entirely -- enabling brands to break through the noise and deliver a more personalized experience to customers. Not surprisingly, though, there are challenges ahead for organizations seeking to add AI to their marketing technology stack.

This is exactly what Qzone, one of China's largest online social network platforms owned by Tencent, YouTu Lab, an AI research lab under Tencent focusing on machine learning, and Vipshop Holdings Limited, China's leading online discount retailer for brands, have joined hands to accomplish. The three platforms have produced a new AI powered report that reveals the fashion preferences of China's "post-95" generation in terms of most popular colors, fabrics and patterns and inspired a new collection by famous Chinese designer Chi Zhang, named Designer of the year by Esquire China, to be launched at New York Fashion Week 2017. This is the first time that AI technology has been leveraged to identify fashion trends to guide the design of a major new collection for presentation at New York Fashion Week. By applying facial recognition technology to big data aggregated on Tencent's Qzone platform, YouTu Lab's algorithm identified the ages of "post-95" users accurately within three years. In order to analyze fashion preferences, the AI-powered technology was able to distinguish users' clothing from a multitude of varied backgrounds with 95% accuracy.

Man-versus-machine is a false dichotomy, pitting one against the other as two discrete, antagonistic entities. Machines will free up our time so we have fewer basic tasks to complete. We have already begun to see the start of this, with the creation of digital assistants like Siri and Alexa. Furthermore, machines will enable us to reimagine what our careers and working lives look like. It s worth remembering that the 40-hour workweek is a construct with a short history it was only in 1940 that it became codified through an amendment to the Fair Labor Standards Act.