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.

As Silicon Valley investors and tech giants continue to pour cash into burgeoning artificial intelligence technologies such as machine learning and chatbots, the relatively nascent A.I. industry is emerging as the latest mega-hot new ticket in town--the heir to online delivery apps, anything-hailing services, and virtual reality start-ups. But much like another buzz-worthy predecessor, Big Data, many A.I. cheerleaders and investment check signatories probably don't quite understand it. But in Silicon Valley, when has that ever stopped anyone? The obsession in the Valley with artificial intelligence is almost palpable. Just last week, Twitter announced its acquisition of London-based machine learning start-up Magic Pony Technology.

Microsoft has a birthday present for Windows 10 users: more capabilities for its Cortana digital assistant and new ways to ditch passwords. The company is also changing the notices it sends to users of previous versions, following complaints that it was too aggressive in pushing them to get the free Windows 10 upgrade. Microsoft's "Anniversary Update," scheduled for release Aug. 2, will let users activate Cortana with a spoken command ("Hey Cortana") even while their screen has gone into sleep mode. Cortana will be able to recall more types of information, such as frequent flier numbers or parking locations. Users can also ask Cortana to remember specific photos, such as a wine bottle to buy again later.

Last month Salesforce released details on some recent acquisitions in machine learning, reporting spending of almost 33 million on the AI startup MetaMind, and an additional 41.6 million for two other companies, including the intelligence systems startup PredictionIO. This all furthers the Salesforce commitment to bolster their AI capabilities. Current predictions suggest that the global AI market in general will grow to over 5 billion by 2020, driven in part by the rising adoption of predictive marketing intelligence and natural language processing technologies across all business sectors. SugarCRM recently announced that they are developing a new intelligence service with a Siri-like agent called Candace. According to their literature, Candace will be able to listen in on business meetings and use natural language processing to analyze the conversations.