It is the day after Google's big hardware event in San Francisco, when the company formally unveiled a new phone (a jab to the iPhone) and a voice-activated speaker (a gut punch to Amazon's Echo). Word of mouth is already tracking positive; a countdown to ecstasy, in the form of upcoming rhapsodic reviews of the Pixel phone, has already begin. But in a conference room on the company's sprawling Mountain View campus, Fernando Pereira, who leads Google's projects in natural language understanding, is less excited about his company's shiny new devices than he is about what will happen when people use them. "Let me tell you a little bit about The Transition," he says. Pereira holds the title of distinguished scientist at Google.

The technology landscape changed immensely in 2016–especially among the players that influence the digital marketing and advertising environments. Five key technologies played a hand in this year's advancements, but the speed of progress for each varied, and not all lived up to expectations. With the familiar traffic light as our analogy, let's take a deeper look and also consider what 2017 has in store for all of them. I've given each area an objective (but unscientific) status value based on my own observations through the year, defined as follows: From Advertising To Experience Current landscape: Research suggests that U.S. consumers are exposed to between 3,000 and 5,000 commercial messages daily. Little wonder that marketers, who are investing a lot of money and effort in marketing campaign development, still struggle to break through the noise.

Make no mistake: We're still probably at least a decade away from any kind of meaningful shift away from the smartphone. Assuming we're still eating at all, I guess.) Yet, piece by piece, the groundwork for the eventual demise of the smartphone is being laid by Elon Musk, Microsoft, Facebook, Amazon, and a countless number of startups that still have a part to play. And, let me tell you: If and when the smartphone does die, that's when things are going to get really weird for everybody. Not just in terms of individual products but in terms of how we actually live our everyday lives and maybe our humanity itself. Here's a brief look at the slow, ceaseless march toward the death of the smartphone -- and what the post-smartphone world is shaping up to look like.

Apple is rumored to be making a smart speaker on the lines of the Google Home and Amazon Echo. Phone leaker Sonny Dickson tweeted Thursday that the device, which would run using the company's voice assistant Siri and its AirPlay functionality, would use the Siri voice assistant and AirPlay functionality. He further said that the device would use the company's Beats Audio technology and will run on iOS. While Apple is still mum on the device's existence, let alone its features, there are chances that the company could launch the device at the World Wide Developers Conference in June in San Francisco. Facial recognition tech: According to CNET, the device might have a built-in camera like the Amazon Echo Look to detect multiple users.

WhatsApp now lets Siri read your messages aloud and even reply to them without you having to press a button. All users have to do it say'Hey Siri, read my last WhatsApp message' and it will dictate your outstanding notifications, the content of the message with information about who sent them. This means you can access your contacts remotely - although not everyone might like the idea of their messages being read out in public. In the latest update to the chat app users running Apple's iOS 10.3 software will be able to access your messages hands-free by getting Siri to read them out (stock image) The latest update works for users running on iOS 10.3. Users can use voice dictation to compose and receive outstanding notifications.

There's just one male northern white rhino left in the world, and he's now on the Tinder dating app as wildlife experts try to keep his species alive. The rhino, named Sudan, will appear as an ad on Tinder as you're swiping through potential suitors – and despite the burden of having to save his species from extinction, he has'no problems performing under pressure.' It's hoped that the'Most Eligible Bachelor in the World' campaign launched by Ol Pejeta Conservancy and Tinder will help raise $9 million towards breeding methods before it's too late. The rhino, named Sudan, will appear as an ad on Tinder as you're swiping through potential suitors – and despite the burden of having to save his species from extinction, he has'no problems performing under pressure' While swiping through Tinder, Sudan will eventually appear. If you swipe right on his profile, it will be a'match.'

Amazon's Alexa has learned how to be a personal stylists. The e-commerce giant has added the AI to the Echo Look, which takes full-length photos and short videos of your outfit from every angle, helps you create a'personal lookbook' and designs shareable photos. There is also a service, called Style Check, that combines machine learning algorithms with advice from fashion specialists to give you a second opinion on your clothing choices. However, Twitter users are weary about the new device, as many have stated that the device will not just help you be a trendsetter, but will be used to gather personal data while it'watches you'. Amazon has released the Echo Look, which is an Alexa-powered camera that takes full-length photos and short videos of your outfit from every angle, helps you create a'personal lookbook' and creates shareable photos Amazon's Echo Look smart camera is priced at $200.

Not too long ago humanity left behind its skeuomorphic interfaces. Skeuomorphism meant using references to real world surface textures on visual interfaces to enhance their comprehensibility. We stripped our visual interfaces off their ornamentations to allow a more authentic approach to visual aesthetics. Killing skeuomorphism made us feel very smart about ourselves. We finally don't need glossy buttons to understand something is tappable!

Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these methods depends on the number of observed entries and the rank of the matrix; the larger the rank, the more entries need to be observed in order to accurately complete the matrix. In this paper, we deal with matrices that are not necessarily low rank themselves, but rather they contain low-rank submatrices. We propose Targeted, which is a general framework for completing such matrices. In this framework, we first extract the low-rank submatrices and then apply a matrix-completion algorithm to these low-rank submatrices as well as the remainder matrix separately. Although for the completion itself we use state-of-the-art completion methods, our results demonstrate that Targeted achieves significantly smaller reconstruction errors than other classical matrix-completion methods. One of the key technical contributions of the paper lies in the identification of the low-rank submatrices from the input partially-observed matrices.

Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface, and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user. We present the design and implementation of Yum-me, and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from item-wise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.