As Apple prepares to release the iPhone X on Friday, the wait time for those who pre-ordered the device last week is getting shorter as statuses of customers orders are updated to "shipped," according to MacRumors. Meanwhile, customers have started to line up outside Apple stores to get their hands on an iPhone X. Apple, carriers and retailers in 55 countries began taking pre-orders for the iPhone X last Friday. The device is set to be released in stores this Friday at 8 a.m. The 64GB iPhone X model is priced at $999, while the 256GB version is selling for $1,149, the most expensive iPhone yet. The smartphone includes the Face ID facial recognition technology, Animoji, a vertical dual-camera system and other features.

What you're seeing in this video from graphics processing firm Nvidia is the result of two algorithmic adversaries trying to one-up each other. Working from a photo database of 30,000 celebrity faces, the two algorithms learned about different details, like beards and jewelry, that make a face look real to the human eye, and then engaged in a rapid-fire back-and-forth process that produced amazingly realistic results. None of the good-looking folks you see are real, but you'd never know it.

This past summer proved to be the official tipping point for voice-first shopping for many consumers. With Amazon's Echo Dot ranking as the "best-selling product from any manufacturer in any category across Amazon globally" during Prime Day 2017 and Google Home pairing up with Wal-Mart and The Home Depot, the era of AI-assisted selling officially had its breakthrough during the first half of 2017. Additionally, according to a recent Gartner study, sales of voice-activated speakers with artificial intelligence capabilities will reach $3.52 billion by 2021, signaling that adoption of voice-enabled speakers will only continue over the next few years. Though e-commerce continues to gain ground on in-store purchasing, we are collectively a group of consumers who often use our voice throughout our purchasing journeys. Whether it is asking for a different size or color, checking if our product is in stock or simply expressing how we want to pay, we are used to these interactions.

Artificial intelligence and logistics is the perfect union for businesses seeking a competitive edge – and a necessary combination for companies hoping to compete in the future. Let's go inside the technology of AI and logistics. First, supply chain management, or SCM, is the process of overseeing the shipping process. Logistics is the portion of SCM concerned with the movement of goods. For many years, technical advances in logistics has lagged behind other industries.

After what seems like an eternity (for IKEA and HomeKit fans, at least), the Swedish furniture maker has added support for Apple's smart home platform. As 9to5Mac notes, so long as you have the company's Tradfri hub installed at your house, you'll be able to start controlling the lighting via your iOS device. It's worth noting that, this will only work with smart lightbulbs at this time; dimmer switches aren't online just yet. More than that, the hub integrates with Amazon's Alexa so if you aren't all-in on HomeKit, you can still use voice commands to turn your lights on and off. Google Assistant support isn't included in this update, however.

Artificial Intelligence (AI) could soon be deciding how much your weekly grocery shop costs, with dynamic pricing expected to become common place in the coming years. Dynamic pricing sees the costs of products change throughout the day according to factors such as demand and availability. You may have already witnessed similar pricing strategies with the likes of Expedia, who adapt prices based on your internet data and history, and Uber, who use "surge" prices when there is an increased demand, and cinemas are now even experimenting with such schemes. With the rise in popularity of internet shopping and tech giants like Amazon getting involved in the grocery sector, brick and mortar stores are becoming inventive to keep consumers shopping in-store. We are seeing consumers increasingly take a hybrid approach to their shopping, comparing prices online and buying in store. As a result of these changing shopping habits, supermarkets will increasingly turn to real-time data to react to market trends.

Romeo Kienzler takes the reader on a big and detailed tour through significant Spark topics and exercises, which occur in the practical usage of Spark in Big Data, Analytics, Data Science and Analytic Data Warehouse ("ADW") projects. In his book topics like the new Spark V2 Ecosystem, Machine Learning, Spark Streaming, Graph Processing, Cluster Design and Management (Yarn and Mesos), Cloud based deployments, Performance topics around HDFS, Date importing and handling, Spark Data Source API, Spark Dataframes and Datasets API, Code Generation for expression evaluation, Project Tungsten, Spark error handling and much more are covered. If you have taken one or more of the well done Spark courses from Databricks before, the topics might familiar but the book covers even some more enhanced topics as well it can be taken as a good comprehension or as in-depth notes. Additionally the book focus on very specific details and problems in parallel programming with Spark, derived from practical use cases.As well the book contains links and references on papers, literature and web forums. To summarize I would recommend this book as an excellent starting point and Spark reference guide.

We're all only about ten years away from sauntering into stores, grabbing whatever it is we want, then quick-stepping out like we stole it. It'll be possible because many shops will be ringed with machine vision-enabling cameras and sensors, that keep tabs on what you take while inside and then charge it to the corresponding app as you leave. Analysts say the big shift is being ushered in by retailers trying to stave off the online shopping explosion. People tend to cite crowds and lines as reasons they avoid stores, so the hope is that tech will be the savior of the remaining brick and mortar mainstays. But while that checkout change might thrill some customers, it'll also dramatically change employment for low-skilled retail jobs and comes with a host of privacy concerns.

It is, however, a thoughtful introduction to and overview of machine-learning methods, appropriately remembering about the context and life-cycle of an ML project, and keeping things hands-on with small Python examples, but managing not to fall into the catalogue mode. I have seen other books try this before. "Doing Data Science" by O'Neill and Schutt comes to mind first, long on enthusiasm but a little short on quality. Then there is Manning's own "Practical Data Science with R" by Zumel and Mount. Among the three, RWML looks like a clear winner. If I had to pick on something, I would register disappointment with the book's one extended exercise, based on the NYC taxi dataset.

As you plan your agenda, machine learning is undoubtedly a hot topic on your list. This year we have a lot of great technical content in the Machine Learning track, with over 50 breakout sessions, hands-on workshops, labs, and deep-dive chalk talks. You'll hear first-hand from customers and partners about their success with machine learning including Facebook, NVIDIA, TuSimple, Visteon, Matroid, Butterfleye, Open Infuence, Whooshkaa, Infor and Toyota Racing Development. This year we're hosting our inaugural Deep Learning Summit where thought leaders, researchers, and venture capitalists share their perspectives on the direction in which deep learning is headed. In addition you can take part in our deep-learning-powered Robocar Rally. Join the rally to get first-hand experience building your own autonomous vehicle and competing in an AI-powered race.