You don't hear many people bellyaching about the servant problem these days. In 1904, when Saki wrote, "The cook was a good cook, as cooks go, and as cooks go, she went," this lapidary witticism would have fallen on kind ears. The bourgeoisie of that era talked about the unreliability of hired help with the same willed petulance that we reserve today for conversations about how it takes three remotes to turn on our TV. Indeed, in today's world, it seems as if you'd be less likely to hear about a domestic walking out than you would someone falling in love with his virtual assistant. I recently used a virtual assistant named Amy for 10 days. We did not fall in love, but I should point out that Amy is underage because she is still in beta.

Ask Apple's Siri digital assistant if she's evil, and she'll respond curtly, "Not really." Repeat a famous line from the movie "2001: A Space Odyssey," in which a computer on a spaceship kills nearly all the human crew, and Siri groans. And who can blame her? We humans have a morbid fascination with machines rising up to wipe us out or to enslave us as cocooned, flesh-and-blood battery packs. You can see that vision of the future streaming over Netflix whenever you want.

The line between fiction and reality is blurring. Some years ago, driverless cars and drones delivering packages at our doorsteps would have seemed like science fiction. However, these and other new technological advances continue to astound us and make our lives easier. For instance, Dag Kittlaus, who created the virtual assistant Siri, recently showcased another artificial intelligence (AI) platform, Viv, at TechCrunch Disrupt, New York. Based on the demo shown, Viv seems to be a more sophisticated and powerful a virtual assistant.

Whether exchanging dialogue with our smartphones or scribbling characters on touchscreens, the Human-Machine Interfaces (HMI) we interact with today are intuitive and foster'easy to use' input methods. Driven by speech, handwriting and touch, our technologies are continually progressing towards intuitive communication between humans and machines, and we are continuing to march forward. However, several advancements in artificial intelligence technology, such as machine and deep learning capabilities, have paved the way for the humanistion of our machines and devices. And there's one particular development in the AI space which has pioneered the ability for seamless human-to-machine interaction - cognitive ergonomics. Through cognitive ergonomics, system designs that allows machines to adapt and operate considering mental workloads and other factors, we are able to communicate with our devices as easy as writing a note on paper.

Ask Apple's Siri digital assistant if she's evil, and she'll respond curtly, "Not really." Repeat a famous line from the movie "2001: A Space Odyssey," in which a computer on a spaceship kills nearly all the human crew, and Siri groans. And who can blame her? We humans have a morbid fascination with machines rising up to wipe us out or to enslave us as cocooned, flesh-and-blood battery packs. You can see that vision of the future streaming over Netflix whenever you want.

There's been a lot of press recently about Artificial Intelligence, or AI. Put simply, AI can be defined as the "capability of a machine to imitate intelligent human behavior" – in this case, the fairly unique human trait of intelligence. But intelligence can mean a lot of things – anything from finding the shortest distance from A to B to detecting a cancerous cell in a collection of thousands of slide photos. To understand why AI is such a popular news topic these days, we first have to understand what it is. The first thing to understand about AI is there are two distinct types: weak AI, which we can think of as specific intelligence, and strong AI, which we can think of as general intelligence.

Scala is a programming language that makes it possible to write terse but efficient code. In today's fast paced world, learning languages like Scala pay great dividends when solving complex problems like building recommendation engines to optimize the customer experience. The big players like Google, Amazon, Linkedin and Facebook all employ recommendation engines to keep the customer coming back for more. The author of this book assumes no prior experience with Scala and starts from the beginning, explaining how to install Scala, the Scala Build Tool and Apache Spark. The author combines these with Apache Kafka and MongoDB to build a data processing pipeline able to glean upto the minute insights into customer data.

THE ORIGINAL MACHINERY question, which had seemed so vital and urgent, eventually resolved itself. Despite the fears expressed by David Ricardo, among others, that "substitution of machinery for human labour…may render the population redundant", the overall effect of mechanisation turned out to be job creation on an unprecedented scale. Machines allowed individual workers to produce more, reducing the price of many goods, increasing demand and generating a need for more workers. Entirely new jobs were created to oversee the machines. As companies got bigger, they required managers, accountants and other support staff.

Artificial intelligence is creeping into everyday life in the form of intelligent personal assistants (IPA). Question-answering applications aim for human-like responses, but it's tough to live up to that standard.

Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend $K$ most attractive items from a large set of $L$ candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items $L$. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.