The smart speaker is a tech innovation that's caught fire with consumers, according to the "Smart Speaker Consumer Adoption Report 2018" released by Voicebot.ai in collaboration with PullString and the RAIN Agency. The findings are based on an in-depth national survey of 1,057 U.S. adults, who were queried on ownership, product preference and use cases. About 19.7 percent of U.S. adults are said to have access to a smart speaker, which would mean an absolute number of 47.3 million out of the total U.S. adult population of 252 million, according to Voicebot.ai. "Access" includes adults who have a device in their homes but are not the primary user. A separate survey by NPR and Edison Research completed in November found 16 percent of adult Americans, or about 39 million, own a smart speaker.

A version of this article appeared in the Spring 2018 issue of strategy business. If you haven't noticed, a high-stakes global game of digital disruption is currently under way. It is fueled by the latest wave of technology: advances in artificial intelligence, data analytics, robotics, the Internet of Things, and new software-enabled industrial platforms that incorporate all these technologies and more. Every enterprise leader recognizes that, as a result, the prevailing business models in his or her industry could drastically and fundamentally change. A wide range of industries, such as entertainment and media, military contracting, and grocery retail have already been profoundly affected. No enterprise, including yours, can afford to ignore the threat. Yet most companies are still not moving fast enough to meet this change. Some leaders are still in denial about it, some are reluctant to upend the status quo in their companies, and some are unaware of the necessary steps to take. But these excuses are not good enough. If your company is aleady struggling, then digital disruption will accentuate your problems. You may not have needed a plan for the new digital age yet, if only because it didn't seem relevant to your industry. But you will need it now.

Widescale technology adoption is causing disruption in many industries, and for retail it means an increase in what consumers expect from their shopping experience. Download this e-guide to discover the results of our 2018 UK IT Priorities survey, where IT leaders shared with us what they are going to be investing in over the coming 12 months. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Introduced at re:Invent 2017, Amazon SageMaker provides a serverless data science environment to build, train, and deploy machine learning models at scale. Customers also have the ability to work with frameworks they find most familiar, such as Scikit learn. In this blog post, we'll accomplish two goals: First, we'll give you a high-level overview of how Amazon SageMaker uses containers for training and hosting models. Second, we'll guide you through how to build a Docker container for training and hosting Scikit models in Amazon SageMaker. In the overview, we'll discuss how Amazon SageMaker runs Docker images that have been loaded from Amazon Elastic Container Service (ECS) for training and hosting models. We will also discuss the anatomy of a SageMaker Docker image, including the training code and inference code. If you are only interested in building, training, and deploying Scikit models in Amazon SageMaker, you can skip the overview. Instead, go right to the hands-on demonstration of how to containerize Scikit models in SageMaker with the minimal amount of effort. SageMaker makes extensive use of Docker containers to allow users to train and deploy algorithms. Containers allow developers and data scientists to package software into standardized units that run consistently on any platform that supports Docker. Containerization packages code, runtime, system tools, system libraries and settings all in the same place, isolating it from its surroundings, and insuring a consistent runtime regardless of where it is being run. When you develop a model in Amazon SageMaker, you can provide separate Docker images for the training code and the inference code, or you can combine them into a single Docker image.

The in-store experience has never been more valuable than it is for luxury brands today. In an industry where marketing is heavily focused on the product experience, it can be hard to imagine an alternate reality where that level of live, in-person engagement can be replicated digitally. However, brands are now confronted with the challenge of maintaining relevance among loyal followers while also attracting a new generation of consumers. The double-digit growth of 2010-13 has dwindled, and e-commerce is starting to represent more and more of the industry's total sales. Amazon and Walmart have charted formidable digital territory, with the former raking in 44% of all U.S. e-commerce sales, and Walmart aiming to grow online sales as much as 40% by 2019.

Since the introduction of self-serve checkouts in Australian supermarkets nearly ten years ago, customers have been engaging in the simplest of hacks to outsmart the supermarket technology. Not so much– mostly it is just by putting through more expensive items as much cheaper ones (think a kilo of lemons as a kilo of potatoes). But thanks to an Aussie start-up, new AI technology will put an end to customer's criminal careers. Tiliter Technology has developed an automated product recognition system which automatically identifies products, removes the needs for barcodes and the entering of additional information at the checkout, so that customers can't cheat the system. Chris Sampson, the co-founder, told News Corp that the "smart checkouts" use a camera to identify the product, and is "based on machine learning and artificial intelligence which has been taught to recognise different types of fruit and other products."

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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.