The iPhone 8 could include face recognition and a "wraparound" screen design, analyst Timothy Arcuri of Cowen and Company said, according to Business Insider. Arcuri predicts three iPhone models later this year, according to a research note circulated to Cowen and Company clients. The next iPhone 8 is referred to in the note as the "iPhone X." The note says one of the models, the iPhone X, will be a 5.8 inch OLED iPhone 8 with a "wraparound" "fixed flex" screen design with embedded sensors, according to Apple Insider, who also obtained the note. The model is rumored to come with features such as, face recognition.

Vizio's living room devices already play nicely with Google services, but wouldn't it be nice if you could use Google voice control as well? All of Vizio's SmartCast-equipped devices, including recent TVs, soundbars and speakers, just got support for Google Home. If you want to watch a movie on Netflix, you don't even have to wake your TV -- say the right words to Google's smart speaker and it'll start playing on your set. You can also pause, skip songs and tweak the volume without touching a button. If you're willing to buy Google Home, you're not dependent on busting out a phone or tablet to get the most out of Vizio's ecosystem.

Internet of @ThingsExpo, taking place June 6-8, 2017 at Javits Center, New York City, is co-located with 20th International @CloudExpo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo 2017 in New York and Silicon Valley.

One of the key themes in Numbersense is the relationship between models and data. Think of data as inputs to models which generate outputs (predictions, etc.). A lot of the dialog in the data science community revolves around models, or algorithms that implement underlying models (random forests, deep learning, etc.). But there are countless examples of applying good models to bad data, resulting in bad outputs. I just finished teaching a class about Analytical Models at Columbia.

Access from devices to cloud AI APIs is the game changer providing highly powerful and scalable real-time capabilities for processing data. The concept of'machines thinking like humans', has moved beyond theory thanks to the increase in – and increase in access to – massive amounts of unstructured multimedia combined with the low cost of high-power, specifically cloud-based, computing. Essentially, if it weren't for big data there would be no artificial intelligence. "There's a been a huge influx of data with everyone feeding sound, text and imagery over social media which has accelerated our ability to try to find ways to process and understand it," says Ian Hughes, analyst at 451 Research. "Traditional processing and analytics are too slow since it doesn't scale, so research has been pushed into AI as a way of dealing with data."

In an ordinary conference room in this city of start-ups, a group of engineers sat down to order pizza in an entirely new way. "Get me a pizza from Pizz'a Chicago near my office," one of the engineers said into his smartphone. It was their first real test of Viv, the artificial-intelligence technology that the team had been quietly building for more than a year. Everyone was a little nervous. Then, a text from Viv piped up: "Would you like toppings with that?"

John McCarthy, the late computer scientist who first coined the term "artificial intelligence," famously said: "As soon as it works, no one calls it AI any more." What was once cutting-edge AI is now considered standard behavior for computers. As I write this, my computer is continuously performing millions of tasks, caching files, managing RAM and balancing CPU loads. The algorithms behind many of these operations would have been considered AI years ago. Last year, I looked into how well neural networks -- programs that behave like a scaled-down version of your brain's neurons -- are able to write.

Nearly half a century has passed between the release of the films 2001: A Space Odyssey (1968) and Transcendence (2014), in which a quirky scientist's consciousness is uploaded into a computer. Despite being 50 years apart, their plots, however, are broadly similar. Science fiction stories continue to imagine the arrival of human-like machines that rebel against their creators and gain the upper hand in battle. In the field of artificial intelligence (AI) research, over the last 30 years, progress has been similarly slower than expected. While AI is increasingly part of our everyday lives - in our phones or cars - and computers process large amounts of data, they still lack human-level capacity to make deductions from the information they're given.

A renewed international effort is gearing up to design computers and software that smash language barriers and create a borderless global marketplace. A woman sits at a desk in Manhattan, talking to herself in French. The phrases she balances on each breath are musical to American ears. She has postcards of Montreal tacked up on the walls of her cubicle – pastel-painted houses in the snow – so as she sculpts the contours of each syllable, she can remind herself of the place where the sounds she's making are heard every day in the street. Her name is Guylaine Laperrière, and she came to New York City more than a decade ago to study musical theater. One day, a friend asked her if she wanted to make a little cash dubbing a French voice-over for a promotional short about insurance. She took the job, and was surprised how much she enjoyed bringing ideas from one language home into another. This article has been reproduced in a new format and may be missing content or contain faulty links.

With a souped-up reproducing piano and some ingenious learning machines, AI maestro Gerhard Widmer is discovering how performers unlock the art in Mozart. A gray-blue dusk is settling over the Gothic cathedrals, palatial opera houses, and labyrinthine streets of Vienna's First District. Here in the Austrian capital, music is an almost elemental force. It's a place where very old and very new musical traditions collide and intermingle – the perfect setting for a computer scientist obsessed with examining the blips and fault lines, deviations and inventions, that transform music into something more than code and just slightly less than magic. This article has been reproduced in a new format and may be missing content or contain faulty links. Contact wiredlabs@wired.com to report an issue. It's Friday evening, and most of his fellow teachers at the University of Vienna have already gone home, but associate professor Gerhard Widmer is bounding up the stairs of a peach-colored Baroque building and into the offices of the Austrian Research Institute for Artificial Intelligence. He waves hello to his team scientists – Simon Dixon, Emilios Cambouropoulos, and Werner Goebl – and makes for a computer monitor marked EUROPA, which is jacked into an electric piano. Widmer eagerly begins to trigger several audio files.