Often referred to as the Amazon of China, JD.com started in 1998 as a brick-and-mortar store in Beijing, but it has aspirations to be the world's leading e-commerce retailer. Based on its tremendous growth, it might not take long for the company to get there. Richard Liu, the company's founder, CEO, and chairman, has even gone so far to predict his company won't need humans and said, "I hope my company would be 100% automation someday…no human beings anymore, 100% operated by AI and robots." JD.com and its competitors such as Amazon, Alphabet, Tencent, Alibaba and more are not only racing to be the world's largest e-commerce business but to create the operating system for retail in the future. JD.com is driving business with artificial intelligence, big data, and robotics while building the retail infrastructure for the 4th industrial revolution.

Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it's much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training. TensorFlow provides an example of using an Estimator to classify irises using a neural network classifier.

We're in the age of the customer, and that means providing a seamless experience across channels, delighting customers with same-day deliveries, and providing unparalleled customer service and personalized recommendations that garner evangelism and brand loyalty. But personalized commerce isn't just about big data--it's about the innovative ways marketers use that data to deliver the personalized, contextually relevant experience customers have come to expect. Retailers who are proactively working to give their customers the best possible experience are approaching marketing in an entirely different, data-driven way. Think you have to be Amazon, Nike, or Google to turn actions into insights? Artificial intelligence is having a transformative impact on the technology landscape and is already showing promise for retail.

The deal is with Paltac Corporation, the biggest supplier to drugstore-style shops in Japan. It begins modestly, with a single pilot store in the city of Sendai, about four hours north of Tokyo, set to open in early 2019. Then it ramps up fast: The plan is to outfit over 3,000 stores in time for the Tokyo Olympics in July 2020. "The government is pushing its stores and its companies to put their best digital foot forward for the Olympics," says Michael Suswal, Standard Cognition's COO and one of the Bay Area startup's seven cofounders. Partnering with Paltac, which supplies most of Japan's small retail industry, allows Standard Cognition to reach a diverse market.

Not a day goes by that I don't find myself reading about grocery stores rolling out new technologies to engage shoppers. If you happen to Google "technology in grocery stores," I guarantee you'll find at least five pages of relevant, interesting articles. I often wonder if we're closer to living like The Jetsons than ever before. The transforming grocery landscape, led by unprecedented advancements in technology, has left food retailers of all shapes and sizes addressing new challenges. Earlier this year, FMI identified five Emerging Issues that have the greatest potential to affect the food industry in the next three to five years.

With wit, vision, and scholarship, Domingos decribes how these scientists are creating programs that allow a computer to teach itself.

I still remember very clearly back in 2012, when I walked inside Eddie Bauer, nervously dropping off my resume with the assistant manager, expressing my interest, and finally getting an in-person interview for a retail associate role. I remember getting the job and thinking to myself if I would be a good retail associate. What came out of that experience was this: I was able to connect with the people walking in, in ways I didn't think was possible. I was able to help them during times when shopping simply felt daunting and burdensome. Taking this experience and my passion for the retail world, I wanted to take a dive into what it may mean to be a retail associate of the future.

I've been to dozens of retail-related conferences in the past year, most of which were attended by retailers, brands and early-growth companies seeking to gain traction in the retail ecosystem. Over the year, I've seen a continuous increase in the number of AI-driven technologies and companies showcasing solutions for retailers. I've also observed growing interest in these innovations among retailers. Because as multichannel retailers continue to fight age-old problems such as inaccurate inventory, stockouts and overstocks, overstretched and undertrained store associates, and suboptimal pricing, advances in AI targeting these problems are proving their ROI. AI, or machine intelligence, is human-like or intelligent behavior exhibited by computers and machines that are "trained" by data to make autonomous decisions.

H2O is an open source data machine learning platform that provides a flexible, user-friendly tool to help data scientists and machine learning practitioners. It was created by H2O.ai, an AWS Partner Network (APN) Advanced Partner with the AWS Machine Learning Competency. Moreover, H2O boasts a significant number of users in the data science community. Based on website analytics embedded in the product, H2O is being used by more than 160 of the Fortune 500 companies, including eight of the top 10 banks and seven of the top 10 insurance companies. In this post, I will show you how to use H2O on Amazon Web Services (AWS) and share best practices for using H2O in the cloud.

Predictive analytics attempts to predict a future outcome based on historical data, and the most common method is referred to as Supervised Learning.