At AWS re:Invent 2017 we announced the AWS DeepLens Challenge in conjunction with Intel. The AWS DeepLens Challenge gave attendees of the re:Invent DeepLens workshops an opportunity to put their skills to the test by building a machine learning (ML) project using their AWS DeepLens. The mission was to get creative with computer vision and deep learning, and learn in the process! All of the contestants put forward amazing entries. We were blown away by the incredible uses they found for AWS DeepLens.

These are all related, yet distinct, concepts that have been used for a very long time to describe an aspect of data mining that many would argue is the very essence of the term data mining: taking a set of data and applying statistical methods to find interesting and previously-unknown patterns within said set of data. We aren't looking to classify instances or perform instance clustering; we simply want to learn patterns of subsets which emerge within a dataset and across instances, which ones emerge frequently, which items are associated, and which items correlate with others. It's easy to see why the above terms become conflated. So, let's have a look at this essential aspect of data mining. Foregoing the Apriori algorithm for now, I will simply use the term frequent pattern mining to refer to the big tent of concepts outlined above, even if somewhat flawed (and even if I personally prefer the less often used term association mining).

"Enlightenment Now: The Case for Reason, Science, Humanism, and Progress" Is the world really falling apart? Is the ideal of progress obsolete? Cognitive scientist and public intellectual Steven Pinker urges us to step back from the gory headlines and prophecies of doom, and instead, follow the data: In seventy-five jaw-dropping graphs, Pinker shows that life, health, prosperity, safety, peace, knowledge, and happiness are on the rise.

AI has been identified as a threat to the warehouse and logistics industry, but what about retail workers? A new report from Blue Yonder looks at how AI could support rather than replace human retail workers. A report from the Martin School at the University of Oxford and Citi estimated that, while perhaps unsurprisingly 80 per cent of retail transportation, warehousing and logistics jobs are at risk due to automation and artificial intelligence, 63 per cent of sales positions are also under threat. Uwe Weiss, CEO at Blue Yonder, argues that removing sales staff from the shop floor to be replaced by AI would be the wrong approach for retailers, as they should be enabling their employees to do what they do best, providing friendly, responsive and bespoke customer service, and letting the machines take care of the manual time-consuming processes, such as replenishment, that require analysis of vast quantities of data and keep staff away from delivering good customer service. For decades, large-scale retail companies have used manual processes to anticipate consumer demand, and stock replenishment has often been based on gut feeling, assumptions, existing agreements with wholesalers and expectations that are hard to measure.

As guests mingled among the appetizers and food at Accenture's Technology Vision event last month in San Francisco, California, jazz music played in the background. The band improvised a number of classic standards, which was noteworthy because one of the players was Shimon, a robot, and it was playing a mean marimba. The presence of a jazz-playing robot was fitting because the theme of the evening was the intersection of human and machine, highlighting the release of Accenture PLC's "Technology Vision 2018" report. It documented the need for enterprises to fully understand emerging technologies, such as artificial intelligence, virtual reality, augmented reality and cloud computing. The robot's musical accompaniment offered yet another example of civilization's inexorable march to a world where machines are part of daily life, doing just about everything humans can do in real time.

Kumar Venkateswar, Product Manager on the AWS ML Platforms Team, shares details on the announcement of Auto Scaling with Amazon SageMaker. With Amazon SageMaker, thousands of customers have been able to easily build, train and deploy their machine learning (ML) models. Today, we're making it even easier to manage production ML models, with Auto Scaling for Amazon SageMaker. Instead of having to manually manage the number of instances to match the scale that you need for your inferences, you can now have SageMaker automatically scale the number of instances based on an AWS Auto Scaling Policy. SageMaker has made managing the ML process easier for many customers.

Voice-based commerce is shaping up to be the next major disruptive force in retail -- and Amazon is poised to grab most of this wallet share. Voice shopping is expected to jump to $40 billion in 2022, up from $2 billion today, according to data from OC&C Strategy Consultants. The voice segment will be driven by a surge in the number of homes using smart speakers, which is on pace to hit 55% in 2020, from 13% today. And Amazon is positioned to dominate the new channel with the largest market share -- currently more than twice that of its nearest competitor. For example, Amazon's Echo device has 10% penetration of homes in the United States, followed by Google's Home (4%), and Microsoft's Cortana (2%).

A recent survey of more than 300 marketers by Resulticks found that almost half thought artificial intelligence was an overhyped industry buzzword and 40% felt skeptical when they saw or heard the term. The survey also found that 47% of marketers believed AI was more fantasy than reality. Artificial intelligence has certainly become the buzzword of 2018 as brands and retailers increasingly explore how it will impact their business this year and beyond. Speaking at an event recently hosted by StoryStream on the hype behind AI in marketing, it became apparent that whilst AI is in its infancy today, many brands and retailers are already actively using AI for the likes of content creation, content management, customer insights, personalization and customer acquisition. Brands including Aston Martin, Shiseido and Mars are already actively embracing AI experimentation in marketing and some have been at it for some time.

TensorFlow enables developers to quickly and easily get started with deep learning in the cloud. The framework has broad support in the industry and has become a popular choice for deep learning research and application development, particularly in areas such as computer vision, natural language understanding and speech translation. You can get started using TensorFlow on AWS by launching the AWS Deep Learning AMI which comes bundled with TensorFlow, as well as other popular deep learning frameworks such as Apache MXNet and Gluon, Caffe, Caffe2, Theano, Torch, Keras, and the Microsoft Cognitive Toolkit.

One-third of its global online sales are now picked up in the store, the company says, but that has created long lines in some cities and waits for attendants to retrieve packages, customers say. To speed up the process, Zara said earlier this year it would roll out a robot-run version of click and collect, automating the service. The collection points in brick-and-mortar stores will allow shoppers who have ordered items online to scan or enter a code, triggering a behind-the-scenes robot to search for the customer's package in a small warehouse, and then deliver it quickly to a drop box. The move comes as Zara faces heightened challenges to maintain its momentum and compete with online-only apparel retailers, such as Zalando and ASOS, that sell a variety of brands. Annual sales at both have grown more than 20% in the past couple of years compared with low double-digit percentage growth at Zara's Spanish parent company Inditex SA ITX 0.46% .