Joseph Sirosh currently leads Microsoft's AI platform strategy and products such as Azure Machine Learning, Azure Cognitive Services, Azure Search and Bot Framework. Prior to this role, he was the CVP for Microsoft's Data Platform. Joseph joined Microsoft in the fall of 2013 from Amazon.com, Inc. Before joining Amazon, he worked for Fair Isaac Corp. as vice president of research and development, where he led R&D projects for DARPA, homeland security and several government organizations. He is passionate about machine learning and its applications and has been active in the field since 1990.

Cyber retail giant Amazon is planning to add a checking-account-like product to their platform. Practically all of its profits come from its cloud computing division, Amazon Web Services (AWS). Beyond that, Amazon has a growing advertising business, offers a variety of services to third-party merchants, and runs one of the biggest streaming video services in the world. Amazon is still concentrated on various retail efforts as well. It recently opened its Amazon Go store to the public in Seattle, and it reportedly plans to open up to six more across the country this year.

What a confused hairball of a book. Yet somehow, out of the terrible writing, forests of typo's, vast swaths of formatting errors, splatter-shot jumps of topic (from the minutiae of one company's website to settlement patterns of ancient Madagascar), SOMEHOW out of all the NOISE and IRRELEVANT and DISTRACTING signals, the errors, the garden paths, the red herrings,the level jumps, and general mayhem in this horrific mishmash of a "book", some kind of ... INSIGHT... may begin to organically ... EMERGE... and may contribute to a careful and well-seat-belted reader's useful and possibly even ultimately coherent .... INTUITION ... about Deep Learning.

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%).