When Apple launched the HomePod in February of this year, early users squawked in equal measures about its unbelievable audio capabilities and the fact that it was a WiFi-connected smart speaker that didn't "talk" to other speakers. Its limits, not just around stereo pairing and multi-room audio but also around some basic voice commands, established it early on as a $349 product that only true Apple fans or audiophiles should consider. But Apple promised then that key updates were in the works for HomePod, that super impressive sound machine built into a body like a soft roll of toilet paper. Now, a week ahead of its giant annual software conference, those key updates have come. HomePod is still one of the more expensive smart speakers in its class, and Apple doesn't plan to offer any kind of bundle deal on the hardware.

For years, "smart" TVs have largely been defined by internet connectivity and a wide array of apps. LG, with its webOS platform, has been at the forefront of that push. This year, the company has focused more on making its TVs more intelligent thanks to its'ThinQ' initiative, first integrating Google Assistant and now rolling out support for Amazon's Alexa. All of LG's 2018 OLED TVs and Super UHD TVs with ThinQ AI are now compatible with Alexa-enabled devices -- including Amazon Echo, Echo Show, Echo Plus. Users have the power to switch channels, toggle TV volume, browse available content and even initiate private YouTube streams in another room, all through the power of their voice.

FRANKFURT (Reuters) - Moscow-based Internet firm Yandex launched a $160 smart speaker on Tuesday to work with its digital assistant'Alice', becoming the latest challenger to take on the leading voice-activated home helpers from Silicon Valley.

Artificial Intelligence is profoundly changing the way we consider technology. It is advancing quickly, with key headways running from virtual assistants, (for example, Apple's Siri and Google's google assistant) to fraud recognition. This developing tech now has an impact in regular day to day existence. A research performed by Forrester Research anticipated an expansion of 300% in investment in Artificial Intelligence 2017, contrasted with 2016. Tools and innovations assume a critical part in development of any technology.

Where do I focus my marketing spend next year? The diversity of digital marketing trends is huge: content marketing, social media marketing, marketing automation and artificial intelligence – just to name a few. Check out this list of the top digital marketing trends for 2018 to decide what to incorporate into your marketing strategy next year. In the end, you will find an infographic summarizing all digital marketing trends. The top content marketing trend for 2018 is delivering highly personalized messages. Remember that personalized content is not limited to "Hello $First Name$" messages.

Voice control gadgets – such as Amazon's Alexa, Google's Home or Apple's Homepod – are becoming increasingly popular, but people should pause for thought about advances in machine learning that could lead to applications understanding different emotions in speech. The CEO of Google, Sundar Pichai, recently said that 20% of the company's searches are initiated by voice via mobile phones. And, at the end of 2017, analysis of the US market suggested that a total of 44m Amazon Alexa and Google Home devices had been sold. The technology has increasingly impressive abilities to recognize words, but – as an expert on acoustics – it is clear to me that verbal communication is far more complex. How things are said can be just as important as the words themselves.

Speaker: Daniel Pyrathon Recommender systems have become increasingly popular in recent years, and are used by some of the largest websites in the world to predict the likelihood of a user taking an action on an item. In the world of Netflix, this means recommending similar movies to the ones you have seen. In the world of dating, this means suggesting matches similar to people you already showed interest in! My path to recommenders has been an unusual one: from a Software Engineer to working on matching algorithms at a dating company, with a little background on machine learning. With my knowledge of Python and the use of basic SVD (Singular Value Decomposition) frameworks, I was able to understand SVDs from a practical standpoint of what you can do with them, instead of focusing on the science.

The Amazon Echo with Alexa is great, but it can act a little funny sometimes. These are Ranker's best stories involving Alexa. If you're worried about what exactly Amazon's Echo-connected speaker has been recording in your home, there's an easy way to find out. Amazon makes all recent recordings available for listening in the companion Alexa app for iOS and Android. Amazon has said consistently that the Echo speakers only listen in and record after you use the wake word, usually "Alexa," to make your request.

On your screens, in your pockets and one day may even be walking to a home near you. The headlines tend to group together this vast and diverse field into one subject. Robots emerging from the labs, algorithms playing ancient games and winning, AI and its promises are becoming a part of our everyday lives. While all of these instances have some relationship to AI, this is not a monolithic field, but one that has many separate and distinct disciplines. A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything.

A framework is introduced for actively and adaptively solving a sequence of machine learning problems, which are changing in bounded manner from one time step to the next. An algorithm is developed that actively queries the labels of the most informative samples from an unlabeled data pool, and that adapts to the change by utilizing the information acquired in the previous steps. Our analysis shows that the proposed active learning algorithm based on stochastic gradient descent achieves a near-optimal excess risk performance for maximum likelihood estimation. Furthermore, an estimator of the change in the learning problems using the active learning samples is constructed, which provides an adaptive sample size selection rule that guarantees the excess risk is bounded for sufficiently large number of time steps. Experiments with synthetic and real data are presented to validate our algorithm and theoretical results.