A one-year-old boy has been injured after he was run over by a 300lb mall security robot. Toddler Harwin Cheng was'crying like crazy' after the 5ft-tall robot plowed into him at Stanford Shopping Center in Palo Alto, California, on Thursday. Harwin was left with a sore head and swollen right foot, as well as bruises on his right leg. Toddler Harwin Cheng (right) was'crying like crazy' after the 5ft-tall robot (left) plowed into him at Stanford Shopping Center in Palo Alto, California, on Thursday The Knightscope K5 robot is used to recognize known shoplifters and also reports unusual noises to the mall's security team. It is meant to use sensors to negotiate its way around its patrol path, but these apparently malfunctioned when it knocked down and ran over the 16-month-old boy.

Today is Amazon's second annual Prime Day, the made-up shopping holiday where Amazon makes hundreds of thousands of discounted deals available to Prime members. And chances are it will go even better for Amazon this year than it did the last. Amazon launched Prime Day a year ago to commemorate its 20th anniversary, and in spite of what many subscribers viewed as a rather lame product lineup, the company said it sold more on Prime Day than on Black Friday in 2014--34.4 million items sold, or 398 per second. Beyond the sheer number of deals, Prime Day reveals much about Amazon's deeper strategy for selling more. Amazon keeps oiling its logistics machine to make delivery faster: this year customers in more than 25 US metro areas will be able to get their Prime Day hauls in an hour or less via Prime Now, Amazon's superfast same-day delivery service.

DESCRIPTION Do you want to be part of a startup team at Amazon that is going to make history? Are you interested in building engaging community experiences on Amazon.com and on mobile devices? Do you want to build things that millions of people will use? We are looking for a great Scientist to lead an effort out of Berlin (while partnering with a US team) centered around customer interactions. The opportunity is big - millions of Amazon customers contribute their opinions, answer questions, and share their experiences.

Amazon.com has unveiled another new feature for its second iteration of Prime Day, and customer voices will be heard. The e-tail giant is offering exclusive Prime Day deals for Amazon Prime members who use its Alexa voice-activated artificial intelligence platform through July 12. These offers will be good for eligible shoppers who order products using their voice on Amazon Echo, Echo Dot, or Amazon Tap devices. Also through July 12, Prime members will receive 10 off their first purchase on any eligible order of more than 20 when they shop using Alexa. To find more exclusive deals on the actual date of Prime Day, July 12, Prime members can ask, "Alexa, what are your Prime Day deals?" or visit the special Alexa Deals page.

The'machine learning' involved in General Electric's (GE) Predix platform is a game-changer and Microsoft (MSFT) is coming along for the ride, said TheStreet's Jim Cramer. Shares of General Electric are up almost on percent on Monday morning after the company said its Predix industrial operating system will be available on Microsoft's Azure cloud. Microsoft hopes that GE's Predix will convince its loyal industrial customers to adopt Azure. Microsoft said it will will integrate Predix with its business applications including Office 365, Dynamics 365 and Power BI.

The event many online shoppers have waited for arrives Tuesday. The second iteration of Amazon Prime will offer plenty of deals for customers to grab. The second Prime Day comes after the inaugural event last year that marked the 20-year anniversary of the company. It was the biggest international sales day for Amazon, but many customers were left frustrated as the top products quickly went out of stock. The company is likely hoping for a smoother 2016, which "will feature more than 100,000 deals worldwide exclusively for Prime members, making it the biggest Amazon event ever," it said in press release.

DESCRIPTION Amazon.com is ranked as one of the most admired companies in the US, #1 most innovative, and # 1 in Customer Service. Amazons technology business has a history and tradition of leading the world in Web-related technologies and services. Now, with Amazon Web Services (AWS) you have the chance to help individuals and businesses take their computing infrastructures and applications into the Cloud. Amazon leaders have said publicly that AWS can become as big as our retail business and includes a fast paced release cycle that saw 400 features launched in 2014. The AWS Support team is both a self-standing P&L and a critical operational function with the ability to drive Free Cash Flow and a world-class customer experience.

Cluster-wise linear regression (CLR), a clustering problem intertwined with regression, is to find clusters of entities such that the overall sum of squared errors from regressions performed over these clusters is minimized, where each cluster may have different variances. We generalize the CLR problem by allowing each entity to have more than one observation, and refer to it as generalized CLR. We propose an exact mathematical programming based approach relying on column generation, a column generation based heuristic algorithm that clusters predefined groups of entities, a metaheuristic genetic algorithm with adapted Lloyd's algorithm for K-means clustering, a two-stage approach, and a modified algorithm of Sp{\"a}th \cite{Spath1979} for solving generalized CLR. We examine the performance of our algorithms on a stock keeping unit (SKU) clustering problem employed in forecasting halo and cannibalization effects in promotions using real-world retail data from a large supermarket chain. In the SKU clustering problem, the retailer needs to cluster SKUs based on their seasonal effects in response to promotions. The seasonal effects are the results of regressions with predictors being promotion mechanisms and seasonal dummies performed over clusters generated. We compare the performance of all proposed algorithms for the SKU problem with real-world and synthetic data.

I have read the first 6 chapters of the book. I was expecting to learn machine learning in Java. But I gave up in chapter 6. The text does not provide full examples. Code snippets are provided in the book.

Within the next five years, scalable artificial intelligence in the cloud – Brand AI – could potentially transform how retailers use personalisation to make every store visit a memorable, exclusive customer experience distinct from anything a competing digital disruptor could offer. Arguably the success of this engagement approach is contingent upon a retailer's ability to combine a range of data sources (such as social media behaviour, loyalty card history, product feedback) with its analytics capabilities to create personalised moments of delight in-store dynamically for an individual customer that drives their decision to purchase. But could the truly disruptive approach be one where a customer is continually teaching the Brand AI directly about their wants or needs as part of their long-term personal relationship with a retailer? Could this deliver new forms of customer intimacy online competitors can't imitate? This ongoing relationship can be changed any time by the customer and be pro-active or reactive – the customer may set the preference that the Brand AI only engages them when they are located within a mile of a retailer's store or one week before a family member's birthday, for example.