This has further foisted pressure on the traditional retailers to reimagine the strategies for creating and capturing value in order to explore the optimal usage of their assets. Public policy liberalization is also one of the key factors supporting the flow of knowledge, information and resources, further generating pressure on the brick n' mortar retailers to tackle with the lowered entry barriers to the online retailers in the market. Key trend which will predominantly effect the market in coming year is rising adoption of multi-channel or omni channel retailing. In forthcoming years, the retail industry is anticipated to witness higher growth in the trend of Omni channel retailing. Artificial intelligence will be having a key role as this technology would be bridging the gap between online and offline retailing in coming future.

You made it to right here, Saturday morning. This week, Nintendo faced a patent infringement investigation over its Switch, Sprint and T-Mobile announce they're together, and the notch rears its ugly head again. All it takes to get your stolen pooch back is an email to a CEO. Apparently, all you need to do to get a hold of Jeff Bezos though is have your puppy stolen by a delivery driver and guess the CEO's email address, according to CNBC. After UK resident Richard Guttfield's black miniature schnauzer was nicked following a dog food delivery, Amazon tracked the driver, an independent contractor, and found the dog at the thief's home.

For a computer science professor, Greg Mori doesn't spend nearly as much time staring at a computer as one might think. "A lot of what I do is talk to people," said the director of Simon Fraser University's School of Computing Science. It's fitting, then, that he's using both his human and his computer skills for his next venture: making computers see and perceive the world as humans do. Mori is leading the latest artificial intelligence lab within a network of cross-Canada AI labs that the Royal Bank of Canada (RBC) research institute Borealis AI is operating. The lab, announced by the big bank late last month, will be housed in a downtown Vancouver facility where researchers will be tackling fairness, privacy and bias in AI in addition to its mandate for computer vision. Mori said this holistic approach of Borealis AI helped entice him to join.

A few days after watching a small army of Sawyer robots drop fake apples into plastic bowls, we're back at UC Berkeley's Sutardja Dai Hall to witness another approach to robotic learning. The UCB research team has deemed the robot BRETT -- that's the Berkeley Robot for Elimination of Tedious Tasks. Like the Sawyer robots, BRETT is here to learn -- and hopefully offer researchers some valuable insight into how we can teach robots how to perform dull and repetitive tasks without a lot of programming. In this case, the job is picking and placing -- a decidedly tedious warehouse task that has become extremely demanding as online retailers like Amazon have put the crunch on logistics companies. In some scenarios, robots are taught to execute the process through human demonstrations.

A few days after watching a small army of Sawyer robots drop fake apples into plastic bowls, we're back at UC Berkeley's Sutardja Dai Hall to witness another approach to robotic learning. The UCB research team has deemed the robot BRETT -- that's the Berkeley Robot for Elimination of Tedious Tasks. Like the Sawyer robots, BRETT is here to learn -- and hopefully offer researchers some valuable insight into how we can teach robots how to perform dull and repetitive tasks without a lot of programming. In this case, the job is picking and placing -- a decidedly tedious warehouse task that has become extremely demanding as online retailers like Amazon have put the crunch on logistics companies. In some scenarios, robots are taught to execute the process through human demonstrations.

"We are interested in understanding how bodies change shape over time," according to the survey. The invite comes from Amazon's new 3-D body scanning unit, an outgrowth of its acquisition last year of computer vision startup Body Labs. Accurately predicting how a pair of jeans or a suit will fit is a Holy Grail for retail. Technology to model a human body and how clothing will look on it has a wide range of applications, from being able to prevent returns of ill-fitting garments to on-demand printing and production. Startups and research teams have sprung up around the world to tackle the problem.

The retail industry has benefited greatly from the advancements of machine learning (ML), which have the ability to improve a company's bottom line. The technology can do so by improving the retail experience for consumers with a better user interface, a personalized recommendation engine, the optimization of stocking and inventory and to more accurately price an object. Many companies have already shifted towards a more digitized platform in order to have a better understanding of when to push products more aggressively and when to use more tact with customers. There's no telling how far ML will go in revolutionizing the retail world, but a recent study by McKinsey suggests that U.S. retailers that have adopted data and analytics into their supply chain have experienced up to a 10% increase in operating margin over the last five year. Attaining data and developing the right smart solutions platform with predictive capabilities have been key to boosting businesses' ROIs.

Today, we are launching support for Random Cut Forest (RCF) as the latest built-in algorithm for Amazon SageMaker. RCF is an unsupervised learning algorithm for detecting anomalous data points or outliers within a dataset. This blog post introduces the anomaly detection problem, describes the Amazon SageMaker RCF algorithm, and demonstrates the use of the Amazon SageMaker RCF on an example real-world dataset. Suppose you have collected data on traffic volume over a period of time across multiple city blocks. Can you predict if a spike in traffic volume represents a collision or just the usual rush hour?

Most people are familiar with shopping at Amazon. As with most online retailers, you visit its website, shop for items, place them in your cart, pay for them – and then Amazon ships them to you. Right now, Amazon's business model is "shopping then shipping." During the shopping process, Amazon's artificial intelligence offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job.

In this blog post we'll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. These conversations are a low-cost way to acquire leads, improve website traffic, develop customer relationships, and improve customer service. In this blog post, we'll build a serverless data processing and machine learning (ML) pipeline that provides a multi-lingual social media dashboard of tweets within Amazon QuickSight. We'll leverage API-driven ML services that allow developers to easily add intelligence to any application, such as computer vision, speech, language analysis, and chatbot functionality simply by calling a highly available, scalable, and secure endpoint. These building blocks will be put together with very little code, by leveraging serverless offerings within AWS.