We propose Sketched Online Newton (SON), an online second order learning algorithm that enjoys substantially improved regret guarantees for ill-conditioned data. SON is an enhanced version of the Online Newton Step, which, via sketching techniques enjoys a running time linear in the dimension and sketch size. We further develop sparse forms of the sketching methods (such as Oja's rule), making the computation linear in the sparsity of features. Together, the algorithm eliminates all computational obstacles in previous second order online learning approaches. Papers published at the Neural Information Processing Systems Conference.

"There's this folklore mythology around if Amazon launches a business in a certain area, it means that all the other businesses in those areas are not going to be as successful," Jassy said at the Goldman Sachs Technology and Internet Conference in San Francisco yesterday. "I just haven't seen it." There are only two significant industries that Amazon has "disrupted," according to Jassy: retail with Amazon.com, and technology infrastructure with AWS. His remarks come as federal and state regulators are conducting antitrust probes to determine whether Amazon and other technology giants stifle competition and innovation. "In both cases, they were models that were pretty antiquated, and customers weren't so happy with those models, and somebody was going to end up reinventing them," Jassy said.

A clerk pick an item for a customer order at the Amazon Prime warehouse in New York. Amazon Empire director James Jacoby describes the pace of work within the company's warehouses as "incredibly grueling." A clerk pick an item for a customer order at the Amazon Prime warehouse in New York. Amazon Empire director James Jacoby describes the pace of work within the company's warehouses as "incredibly grueling." Amazon founder Jeff Bezos is now the richest man in the world, with an empire that stretches from Hollywood to Whole Foods -- and even into outer space.

Amazon Personalize is a machine learning service which enables you to personalize your website, app, ads, emails, and more, with custom machine learning models which can be created in Amazon Personalize, with no prior machine learning experience. AWS is pleased to announce that Amazon Personalize now supports ten times more item attributes for modeling in Personalize. Previously, you could use up to five item attributes while building an ML model in Amazon Personalize. This limit is now 50 attributes. You can now use more information about your items, for example, category, brand, price, duration, size, author, year of release etc., to increase the relevance of recommendations.

As the world grapples with artificial intelligence (AI) and debates its impact on security and privacy, many industries have already embraced this promising technology. The customer service space has become particularly intrigued by its capability, relying on AI to power text- and voice-based chatbots to field any number of questions or complaints. This has enabled the likes of Whole Foods, eBay and Burberry to field many phone and web inquiries before involving a human customer service agent. It's hard to imagine a world that would turn out differently, particularly as AI infiltrates a growing number of sectors. From airports to grocery stores, the spread of AI has only just begun.

In this paper, we study the assortment optimization problem faced by many online retailers such as Amazon. We develop a \emph{cascade multinomial logit model}, based on the classic multinomial logit model, to capture the consumers' purchasing behavior across multiple stages. Different from existing studies, our model allows for repeated exposures of a product, i.e., the same product can be displayed multiple times across different stages. In addition, each consumer has a \emph{patience budget} that is sampled from a known distribution and each product is associated with a \emph{patience cost}, which captures the cognitive efforts spent on browsing that product. Given an assortment of products, a consumer sequentially browses them stage by stage. After browsing all products in one stage, if the utility of a product exceeds the utility of the outside option, the consumer proceeds to purchase the product and leave the platform. Otherwise, if the patience cost of all products browsed up to that point is no larger than her patience budget, she continues to view the next stage. We propose an approximation solution to this problem.

When we got our beautiful puppy, Addy, I warned my boyfriend that golden retrievers shed--a lot. Despite my warnings, he didn't fully comprehend just how much hair one dog could produce. At first, he just kept saying it must be a seasonal shed, but after three months of daily Swiffering, he finally accepted that our apartment and clothing would simply be covered in dog hair forever. A few weeks later, it was Black Friday, and while I was browsing the daily deals on Amazon, I saw that the iRobot Roomba i7 was on sale for $700--that's $300 less than its list price. Even though I haven't been that impressed by robot vacuums in the past, I decided it would be a nice Christmas gift for our pet hair–infested household, and I made an impulse purchase. It's the end of January now, and both my boyfriend and I agree that Ruby, our darling Roomba, was an incredible purchase--seriously, the best $700 I've ever spent!

Artificial intelligence at its most basic level provides steroids for retail data. Suppose a regional fashion retailer with an eCommerce presence has 100,000 customers in its data base. And suppose those records are fairly basic: most recent transactions, average purchase per visit, demographic information and website traffic history. Now suppose those records are supplemented by a third-party anonymized database. AI will take that data and match it to an algorithm that then allows all the data to be supplemented by data within the category or for competitive purchases.

TL;DR: The Echo Dot (3rd Gen) smart speaker is on sale for £29.99 on Amazon, saving you 40% on list price. This isn't a typical time for deals on Amazon devices, but the online retail giant has thrown a curveball by putting its entire range on sale. This sort of thing usually only happens on Prime Day and Black Friday, but we've been treated to an early taste of what's to come with discounted e-readers, smart speakers, video doorbells, tablets, and more. One of the standout deals from this selection is the discounted Echo Dot. This popular smart speaker is down to just £29.99 on Amazon, saving you 40% on list price.

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.