Deck building is a crucial component in playing Collectible Card Games (CCGs). The goal of deck building is to choose a fixed-sized subset of cards from a large card pool, so that they work well together in-game against specific opponents. Existing methods either lack flexibility to adapt to different opponents or require large computational resources, still making them unsuitable for any real-time or large-scale application. We propose a new deck recommendation system, named Q-DeckRec, which learns a deck search policy during a training phase and uses it to solve deck building problem instances. Our experimental results demonstrate Q-DeckRec requires less computational resources to build winning-effective decks after a training phase compared to several baseline methods.

Should children be polite to virtual assistants? And for most parents and child development experts, the answer is simple, too: Yes, of course they should. Nobody wants to hear children rudely barking orders at, or verbally abusing, an adult voice. But teaching kids to say "please" and "thank you" to Alexa and Google Assistant may have unintended consequences and raise other questions that aren't so simple. Millions of parents have suddenly been forced to grapple with this new parental conundrum.

Google is probably one of the most active tech giants when it comes to Artificial Intelligence. In their most recent I/O, they probably demonstrated how to nail down a turing test. For those who missed it, here's a sneak peek: If such disruptive and moot evolution in Artificial Intelligence are not an effort towards enhancing mobile experiences then I don't know what is. With the change in consumer lifestyles and an escalating preference towards mobile devices, virtually all web applications are following the mobile first approach. Consumer behavior has evolved to an extent that more time is spent on mobile phones as compared to televisions or desktops.

Machine Learning (ML), along with the Internet of Things (IoT) seems to be the next big revolution in science and technology. AI experts are debating why machine learning is the most wondrous thing, today. They are trying to predict the way ML can affect the future and its evolution. The ability to feed the machine with big amounts of data, so that the machine can learn concepts and rules to focus on specific categories of problems and solutions, is a critical part of AI development. In 1959, the term'machine learning' was coined by Arthur Samuel, an AI professional.

I think most folks are best off with an Amazon Echo Dot or a Google Home Mini to start. Pick the Google product if you're on Android and are already used to the Google Assistant since it'll feel familiar. Both products, however, offer affordable ways to see what the whole "smart home assistant" experience is like. The Echo Dot and Home Mini offer almost all of the same functions as more expensive Echo and Google Homes (the exception is Amazon Echo products with touchscreens, which have more visual data). If you're invested in the Apple ecosystem, like Siri, and put a high value on audio quality, then the HomePod is a good choice.

SAN FRANCISCO -- California technology analyst Brian Solis was having a conversation with a friend while the two were driving through Texas. His friend was buying a ranch in Texas but was having trouble with the financing because it was considered a "barndominium." Solis had never heard the term before nor had he ever researched it online. But as soon as he hopped out of his friend's car and checked Facebook, up popped an ad for barndominiums in Texas. "How is that possible?" he wrote on Facebook.

This post originally appeared on the PullString blog. Unless you've been living under a rock for the last twelve months, you've heard about voice assistants. They go by the name of Alexa, Google Assistant or Siri and can give you information about your commute or the weather, turn lights on and off, book flights, and order groceries. A line of questioning we hear a lot from CMOs in North America and Europe is: should they even care about this latest technology trend, does voice deserve its own strategy, does it warrant attention at their level of the organization? We believe there are four strategic reasons why every CMO should start thinking about what voice assistant customer engagement means for their digital transformation strategy.

Alexa is coming to your hotel room. Amazon this morning is announcing a new program called Alexa for Hospitality, designed to bring its voice assistant technology to everything from chain hotels to vacation rentals. The system can be customized to include key guest information, like checkout time or pool hours; allows guests to request services like housekeeping or room service; and can be configured to control "smart" hotel room functions, like adjusting the thermostat or raising the blinds. Marriott is Amazon's launch partner on the new platform, which is notable not only for the potential scale of this rollout, but also because the hotelier had been testing both Siri and Alexa devices ahead of today's news. According to Amazon, Marriott International will introduce the new Alexa experience at select properties in Marriott Hotels, Westin Hotels & Resorts, St. Regis Hotels & Resorts, Aloft Hotels, and Autograph Collection Hotels starting this summer.

In today's world, every customer is faced with multiple choices. For example, If I'm looking for a book to read without any specific idea of what I want, there's a wide range of possibilities how my search might pan out. I might waste a lot of time browsing around on the internet and trawling through various sites hoping to strike gold. I might look for recommendations from other people. But if there was a site or app which could recommend me books based on what I have read previously, that would be a massive help. Instead of wasting time on various sites, I could just log in and voila! 10 recommended books tailored to my taste. This is what recommendation engines do and their power is being harnessed by most businesses these days. From Amazon to Netflix, Google to Goodreads, recommendation engines are one of the most widely used applications of machine learning techniques. In this article, we will cover various types of recommendation engine algorithms and fundamentals of creating them in Python. We will also see the mathematics behind the workings of these algorithms. Finally, we will create our own recommendation engine using matrix factorization.

As is so often the case in our modern buzzword culture, the phrase artificial intelligence has grown well beyond the confines of what it is, and is instead often thought of in terms of what it could be. There's nothing wrong with maintaining a healthy sense of a technology's potentially dark extremes, but many experts contend that the gloomy predictions of a Terminator-like apocalypse levied by people like Elon Musk are not only a few decades premature, but ultimately make too many assumptions about the ways AI could be employed -- or, for that matter, is already being employed. Whether you realize it or not, artificial intelligence likely already plays an active role in your day to day life. Dating apps like Bumble use it to ban images of firearms and shirtless guys on their platforms -- they certainly don't employ people to enforce their prohibition on guns (of the steel or the flesh variety), they rely on machine learning to identify and flag images containing banned material. This same method, utilized by Yelp to differentiate between pictures of hot dogs and corn muffins, is at the center of the controversial defense contract Google has been under fire for taking on.