Having read Roko's Basilisk, I was excited to read Roko's Labyrinth. The story is set sixteen years after the events of the first book, and follows a young computer programmer, Nick Rose, as he is tasked with breaking into an AI system to find the secret to saving humanity. I loved the advances in technology since the first book. Most notably, humans have now developed a way of splicing robotic cells into their bloodstream, which give them the ability to slow down the senses and give them more time to think. The AIs have also evolved -- Roko's Basilisk has multiplied into hundreds of individual AIs, all of them fighting against humanity and each other.

When Jeff Bezos first launched Amazon.com in 1994, he gave himself a 30% chance of success -- slightly better than the 1 in 10 odds for Internet start-ups. That's actually a very liberating expectation, expecting to fail," he said to Time magazine when it named him Person of the Year in 2000. By then, sales had ticked past $1 billion, but the company had yet to turn a profit. Some analysts remained skeptical that Bezos could deliver on his plan to sell everything and anything. But two decades after its launch, Amazon has conquered online retail, racking up $136 billion in sales in 2016.

The vast majority of software development books, whether it be for line-of-business app dev or game development, seem to have little to no information that can be found via a casual internet search. This book is one of the few exceptions. There is a refreshing breadth and depth of game AI knowledge in this book that has been of tremendous help. Unlike the common "Gems" series of books, this book contains enough information on nearly every topic for the reader to build a'ground up' implementation of their own. My only complaints are that the pseudocode seems to be overly simplified and not as easily converted to a concrete implementation as I'd like, and that even for a book on game-specific AI implementations, the authors seem to enjoy a bit more of an academic/idealized approach to the design.

In the age of Amazon domination, competing retail interests need to get smarter and do so quickly. Such was the message at a recent Cowen panel discussion that highlighted how artificial intelligence and its more studious cousin, artificial intelligence / deep learning / machine learning, operate to build and maintain a retail business. The discussion featured three advanced technology providers, each of whom operated at varying stages of the retail cycle. At the top of the list is Foursquare, a firm that is popularly considered for its social media platform that allows people to communicate their location to an audience waiting with bated breath. Foursquare is more than an application that breaks news when John Q. Rando in Peoria, Illinois checks in at Applebee's, there is a technology underneath the application that provides useful intelligence.

Alibaba is redefining China's online shopping experience by embedding artificial intelligence (AI) throughout its core e-commerce operations. Technology has made shopping easy for customers who leverage AI to search for products, said Jim Erickson and Susan Wang in an article on Alizila.com. Thanks to smart search engines, consumers are now introduced to several goods and services that they were not previously aware of. Alibaba has also introduced customer-service chatbots that can resolve consumer complaints without requiring human intervention. While previously orders were delivered in days, they now reach consumers in a matter of few hours.

Imagine this scene from the future: You walk into a store and are greeted by name, by a computer with facial recognition that directs you to the items you need. You peruse a small area -- no chance of getting lost or wasting time searching for things -- because the store stocks only sample items. You wave your phone in front of anything you want to buy, then walk out. In the back, robots retrieve your items from a warehouse and deliver them to your home via driverless car or drone. Amazon's $13.4 billion purchase of Whole Foods, announced Friday, could speed that vision along.

Men's dress clothing, mine included, can be a little boring. Like many male office workers, I lean toward clothes that are sharp but not at all showy. Nearly every weekday, I wear a dress shirt that is either light blue, white or has some subtle check pattern, usually paired with slacks and a blazer. The description alone could make a person doze. I used to buy my dress shirts from a Hong Kong tailor.

From a shopper's standpoint, building a grocery list via voice assistant is a lot more impactful than asking Alexa to buy, say, a pair of sneakers. Interacting with Alexa--be it through the Dash Wand, an Echo device, or Amazon app--could become as natural an experience as saying "Honey, we need eggs." The key difference: When you say that to Amazon, the eggs could just arrive. On the backend, Amazon has been developing an infrastructure to support such an enterprise for years. The Seattle company has deals with many delivery contractors, and also runs a network of independent "gig" couriers through a program called Amazon Flex.

Amazon didn't acquire an iconic grocery store brand just for the quinoa: Whole Foods operates hundreds of retail data mines, and Amazon just married a world-class artificial intelligence team with one of the best sources of in-store consumer shopping data in the U.S. There are lots of reasons, to be sure, why Amazon would want to spend $13.7 billion on Whole Foods. But the quintessential online retailer has been trying to establish a physical store presence for a few years now, and with one big check, it will now control more than 400 sources of prime data on consumer behavior. Big-box grocery stores are easy sources of data on human purchasing behavior. Any modern retail outlet monitors activity such as customer flow through the aisles, brand affinity, and, of course, the customer loyalty cards that do as good a job of profiling a person as anything.

This is a guest post from my colleagues Naveen Swamy and Joseph Spisak. Machine learning is a field of computer science that enables computers to learn without being explicitly programmed. It focuses on algorithms that can learn from and make predictions on data. Most recently, one branch of machine learning, called deep learning, has been deployed successfully in production with higher accuracy than traditional techniques, enabling capabilities such as speech recognition, image recognition, and video analytics. This higher accuracy comes, however, at the cost of significantly higher compute requirements for training these deep models.