This is a guest blog post by Andrea Tabacchi, the Solution Architects team lead at Memsource. Memsource is always looking out for exciting new integrations that enhance its cutting-edge translation solutions. With machine translation (MT) continuing to be a hot topic in the localization industry, Memsource is focusing on integrating with innovative MT engines that meet customers' growing MT needs. In particular, Memsource strives to offer neural machine translation (NMT) engines, such as Amazon Translate. NMT is proving to be a highly influential technology.
Amazon's got a great deal for smart home users today, but you can only get it if you're part of a special club. Anyone who has an Echo device or a third-party Alexa-enabled device can get a TP-Link Kasa Smart Wi-Fi Plug Mini for $10 with coupon code SMART10. The bad news is Amazon's sale is limited to one device per customer. TP-Link's Kasa devices are already great options for smart home rookies since they don't require a hub, instead connecting via the Kasa smartphone app and Wi-Fi. The Kasa Smart Wi-Fi Plug Mini HS105 works with Alexa devices (natch) as well as Google Home, though only the Alexa-enabled get today's discount.
Not only does Amazon SageMaker provide easy scalability and distribution to train and host ML models, it is modularized so that the process of training a model is decoupled from deploying the model. This means that models that are trained outside of Amazon SageMaker can be brought into SageMaker only to be deployed. This is very useful if you have models that are already trained, and you want to use only the hosting part of SageMaker instead of the entire pipeline. This is also useful if you don't train your own models, but you buy pre-trained models. This blog post explains how to deploy your own models on Amazon SageMaker that have been trained on TensorFlow or MXNet.
Want to know what Amazon.com Inc. will be doing in physical retail tomorrow? Look at what is happening in China today. If you'd taken this advice, you wouldn't have been surprised when the behemoth spent $13.7 billion last year buying Whole Foods. Eighteen months earlier Alibaba Group Holding Ltd. had launched Hema, a technologically advanced blend of online grocery shopping, dining and bricks and mortar.
A robot will soon be able to handle your groceries for you. Walmart announced Friday that it will soon incorporate automated robotic carts, called Alphabots, in one of its superstores in Salem, New Hampshire. Alphabots can pick and pack shoppers' online orders and complete otherwise mundane tasks in the hopes of streamlining Walmart's online grocery service. 'Alphabot will work behind the scenes to make the process even easier by automatically bringing items from storage to associates who will consolidate the items in the order,' Mark Ibbotson, Walmart's executive vice president of central operations, said in a statement. 'For our pickup associates, that means less time walking the store aisles in search of products and more time ensuring customers are getting the absolute best in fresh produce, meats, etc.' The retail giant installed a 20,000-square-foot extension connected to the store that will house Alphabot.
Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it's much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training. TensorFlow provides an example of using an Estimator to classify irises using a neural network classifier.
Dozens of members of Congress joined forces to request Amazon CEO Jeff Bezos explain the recent "Rekognition" facial recognition flap that misidentified 28 members of Congress as suspected criminals. Bezos does have some questions to answer. It's the least he can do after his artificial intelligence software mistook the faces of more than two dozen lawmakers for mugshots in police files. The American Civil Liberties Union first found the discrepancy; Reps. John Lewis and Tom Garrett, contacted by The Washington Times for response, put out a bipartisan statement, along with Rep. Jimmy Gomez, criticizing the technology.
The pressure to deliver a positive customer experience has never been greater. Real-time access to information and the ease of comparison shopping have elevated expectations and radically changed the buyer's journey. How customers are treated has a profound effect on future transactions: Those whose problems are resolved with minimal effort are far more likely to repurchase goods or services and increase how much they spend. Do a great job, and they will reward you with more business. According to the Consumer Expectations in 2018 report from Avionos, 17 percent of consumers are willing to share even more personal information during the buying experience if it means Amazon can better anticipate their needs.
Yesterday, the ACLU published a report showing that Amazon's Rekognition facial mapping software could have some serious problems with accuracy. A test scanned every current member of the House and Senate and compared them to a database of 25,000 mug shots -- and matched 28 of those lawmakers with various mugshots. It hasn't taken long for some politicians to craft a response. Senator Edward Markey (D-MA) and Representatives Luis Gutiérrez (D-IL 4th district) and Mark DeSaulnier (D-CA 11th district) sent a letter to Amazon and Jeff Bezos asking for more information on Rekogntion, specifically concerning its sale to law enforcement agencies. Today's letter from Markey, Gutiérrez and DeSaulnier said that "the efficacy and impact of [facial recognition] technology are not yet fully understood," going on to note that "serious concerns have been raised about the dangers facial recognition can pose to privacy and civil rights, especially when it is used as a tool of government surveillance."