There are a few places where you can find Siri in macOS, and each feels intuitive. For starters, you'll find the familiar purple Siri shortcut in the Dock, right next to Finder. That lower-left corner is the same place we already expect to find Cortana on Windows 10. If you like, though, Siri also lives in the tray, in the upper-right area of the screen, right next to where the search bar already lives. Or -- and this is my personal favorite -- you can use the keyboard shortcut Fn-spacebar to bring it up without using your cursor.

Last week, at the annual news extravaganza/coding demo/Beats 1 jam sesh that is WWDC, Apple launched a new version of its desktop operating system. There was a funny slide about naming conventions, another California-centric name (Sierra) and a bunch of new features. And Craigy With The Good Hair announced a small but long overdue change: Mac OS X is now called macOS. One day after the event, not two minutes after booting up a MacBook Pro that Apple pre-loaded with a preview build of macOS Sierra, I asked Siri some existential questions. Then I asked which version of OS X she was running, expecting a gentle chiding about macOS and silly questions.

From Siri to photo memories, here are five new features of the MacOS Sierra, coming to the public as a beta in July. NEW YORK--Siri evaded my question for what took Apple's voice assistant so long to get onto the Mac. "Who, me?" responds the voice familiar to anyone who uses an iPhone. However tardy Siri's arrival, the debut of Apple's loquacious personal helper on the company's computers is arguably the most welcome new feature coming to Macs that will be running macOS Sierra. Sierra is the latest iteration of Apple's operating system for its PCs and is the first version in years without the OS X moniker.

For most, the term Artificial Intelligence evokes thoughts of futuristic technologies, of C-3PO and Asimov's "I,Robot." We think of walking, sentient robots that look like us – or at least we imagine they appear like us. What we don't generally think of is a glorified speakerbox, or a supercomputer tasked to create recipes. But that's where AI starts – with small steps and incremental advances in technology. These small steps gradually lead to giant leaps forward; think of a narrow canyon off in the distance: you're pretty sure you can jump the canyon, but first you need to get there.

Are artificial intelligence (AI) and predictive analytics just the latest trendy buzzwords, or is the technology about to transform the enterprise? We spoke to IBM, Microsoft, Insidesales.com, Google and SAP to find out. The world's biggest tech companies are investing heavily in artificial intelligence (AI): software that can learn to think and solve problems in a human-like way. According to Deloitte: "By end-2016 more than 80 of the world's 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products."

Both Apple and Amazon now allow third parties to build their own brand-specific voice technologies on their respective products, Siri and Echo. These are huge steps forward in artificial intelligence (AI) based conversational interfaces or'chatbots' that will make computing a more effortless and intuitive part of our world. Microsoft was also recently in the news, but not in the way they had hoped. Their release of a chatbot named Tay, an AI designed to become smarter as more users interacted with it, was hijacked to spout hate speech. Microsoft created Tay to learn how AI programs could interact with users in casual conversation and, over time, learn from that interaction--just like people do.

Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap between accuracy and transparency or explainability has generated an interest in automated explanation generation methods. Restricted Boltzmann Machines (RBM) are accurate models for CF that also lack interpretability. In this paper, we focus on RBM based collaborative filtering recommendations, and further assume the absence of any additional data source, such as item content or user attributes. We thus propose a new Explainable RBM technique that computes the top-n recommendation list from items that are explainable. Experimental results show that our method is effective in generating accurate and explainable recommendations.

Amazon's brightest star doesn't appear on any of its high-profile TV shows. Rather, it's Alexa, the voice-controlled "brain" at the center of the cylindrical Amazon Echo device that gets smarter the more often users engage with her. Alexa has moved the battle for the most helpful digital assistant from portable smartphones and watches to our homes, where consumers want the hands-off experience that decades of sci-fi movies have promised. Echo has sold an estimated 3 million units since it debuted 18 months ago. Later this year, Google will challenge Amazon with Google Home, an Echo-style device that will manage schedules, play music, and perform routine tasks like ordering an Uber.

It is no secret that artificial intelligence is one of the hottest tech trends of today, but there is a wide range of research fields that falls under the umbrella of AI. According to data from Venture Scanner, machine learning research is the clear leader in the industry by a wide margin, despite the fact that it is also one of the newest avenues of AI research. Machine learning is essentially the ability for a computer program to learn from new data and improve its functionality over time. For example, Google's AlphaGo AI learned how to play Go by evaluating millions of moves from real games, and as it continues to play more matches, it is constantly getting better and better. This allowed AlphaGo to defeat one of the top Go players in the world earlier this year, a landmark feat in the field of AI research.

Machine learning is at the top of the "hype curve" for emerging technologies and is one of the top 10 strategic technology trends for 2016 according to Gartner. A method of data analysis that automates analytical model building, machine learning can benefit almost any business that gathers data with the intention of acting upon it. It's safe to say that most of us are already interacting with machine learning applications on a daily basis, whether it's Apple's Siri, Facebook's face detection, Amazon and Netflix personalized recommendations, or iOS's autocorrect. What can we learn from this growing trend? What can we do with such a widely applicable technology and how can we maximize its potential?