Build a device that works for disorganized people. Most consumer-oriented tech expects us to engage in an organized way -- and become even more organized. Alexa is built for a much more haphazard set of use cases, in which voice helps us get our messy lives back on track, quickly and effortlessly. Get the machine's AI-generated voice just right. Listen closely to Alexa's cadence, and you won't hear the know-it-all bombast of earlier competitors.

Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user privacy, because users need to share their preferences with others in order to be grouped with like-minded people and receive accurate recommendations. Prior related work have proposed to preserve user privacy in a CF framework through different means like (i) random data obfuscation using differential privacy techniques, (ii) relying on decentralized trusted peer networks, or (iii) by adopting secured cryptographic strategies. While these approaches have been successful inasmuch as they concealed user preference information to some extent from a centralized recommender system, they have also, nevertheless, incurred significant tradeoffs in terms of privacy, scalability, and accuracy. They are also vulnerable to privacy breaches by malicious actors. In light of these observations, we propose a novel selective privacy preserving (SP2) paradigm that allows users to custom define the scope and extent of their individual privacies, by marking their personal ratings as either public (which can be shared) or private (which are never shared and stored only on the user device). Our SP2 framework works in two steps: (i) First, it builds an initial recommendation model based on the sum of all public ratings that have been shared by users and (ii) then, this public model is fine-tuned on each user's device based on the user private ratings, thus eventually learning a more accurate model. Furthermore, in this work, we introduce three different algorithms for implementing an end-to-end SP2 framework that can scale effectively from thousands to hundreds of millions of items.

In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default production action. In general, this is impossible because the agent has to explore unknown actions, some of which can be bad, to learn better actions. However, when the actions are combinatorial, this may be possible if the unknown action can be evaluated by interleaving it with the production action. We formalize this concept as learning in stochastic combinatorial semi-bandits with exchangeable actions. We design efficient learning algorithms for this problem, bound their n-step regret, and evaluate them on both synthetic and real-world problems. Our real-world experiments show that our algorithms can learn to recommend K most attractive movies without ever violating a strict production constraint, both overall and subject to a diversity constraint.

Artificial Intelligence is no longer just contained in science fiction films. It is a part of our everyday lives and in our classrooms. As we use tools like Siri and Amazon's Alexa, we are just beginning to see the possibilities of AI in education. And, we should expect to see more. The Artificial Intelligence Market in the US Education Sector 2017-2021 report suggests that experts expect AI in education to grow by "47.50% during the period 2017-2021."

Apple's annual Worldwide Developers Conference is always full of surprises. USA TODAY's Jefferson Graham previews what we can expect in Talking Tech. For many Apple fans, the annual June Worldwide Developers Conference is a holiday fest of many, many new software updates that bring new features to their beloved iPhones and iPads. To others, it's an, "Oh no, Apple's going to make me download this update and it's going to destroy my phone." Just ask all the folks who saw their iPhones intentionally slow down, on purpose, with iOS 10's update that tried to compensate for aging batteries.

Most laptops come with their own virtual assistants. Amazon's Alexa has been the odd assistant out. Without a desktop OS of its own, it's been absent from this arena even as it's emerged as the most helpful virtual assistant and the Amazon Echo line dominated smart-speaker sales. But in a curious, although not entirely unexpected move, Alexa is now available on some Windows 10 PCs. Acer's new Spin 3 and Spin 5 lines both come with Alexa pre-installed, while its gaming laptop and all-in-one desktop setups will arrive with Alexa integration in the coming weeks.

Artificial Intelligence has become commonplace to the point where many don't even realize they use Artificial Intelligence on a daily basis. Last year, 84% of people used AI to do things that make life easier. Filtering out junk email, asking Siri where the nearest burrito place is, and those Facebook ads that seem to know us better than we know ourselves―these are just a few of the things AI does for us. Despite the great majority of people using the technology, only half of them even realize they are talking to a machine. The innovations in AI are astounding, and what machines can do is growing more and more similar to human behavior all the time.

Apple introduces the biggest new features of its mobile and desktop operating systems each year at its annual developer conference, which kicks off on Monday. WWDC, the Worldwide Developer Conference, offers a sneak peek at the new capabilities app developers will be able to work into their apps--and iOS and Mac users will get to use--during its opening keynote. After that, developers get deeper dives, demos, and how-to's during the conference's breakout sessions and panels. Sometimes Apple uses the event as a platform to launch new hardware products as well. Last year, the big news included the HomePod's debut, a new 10.5-inch iPad Pro, redesigns to its App Store and Music app, and ARKit, the developer framework that lets developers more easily build augmented reality apps for iOS.

From being a fancy, futuristic technology to seamlessly assimilating in our daily lives, Artificial Intelligence (AI) has well and truly arrived. Voice-powered personal assistants like Siri and Alexa to advanced Chatbots, Robo-advisors and Self-Driving vehicles, all are different applications of Artificial Intelligence that are rapidly changing the way we work, live and indulge. While Artificial Intelligence is often misunderstood for any machine learning algorithm, the true essence of it is when machines and systems can analyse user behaviour, make connections and comprehend rich meanings without relying on pre-defined behavioural algorithms and improve on past iterations. Artificial Intelligence, which is an umbrella that covers the more specific technologies of Deep Learning (DL) and Machine Learning (ML), is rapidly becoming a mainstay as organizations aim to leverage greater competitiveness, business efficiencies and enter new markets. This is proven by a Forrester Research which indicates that by the end of 2017 business investment in Artificial Intelligence will grow by 300%.

The 2018 developers conference season is coming to a close. We've heard from Microsoft and Google already, Monday, June 4th is Apple's turn to trot out its plans for the next year or so. Apple typically saves its premiere announcements like new iPhones for later in the year, using its World Wide Developers Conference to offer a look at what's coming to the devices many people already own. Usually that's in the form of MacOS and iOS updates, though last year we also met Apple's HomePod smart speaker and a 10.5-inch iPad Pro. In advance of the 2018 conference, here's a look back at what we saw last year and what we expect for Monday's events.