The greatest buzzwords to come from 2018 were blockchain and artificial intelligence (AI). Blockchain has had it's highs and lows, however AI is trending much more positively. Artificial Intelligence isn't brand new, we've seen the likes of Alexa, Siri, Cortana, and Google not only in our hands but now welcomed into our homes. All of these have gained huge momentum at a consistent pace for the last few years. Other well-known established tech companies, governments, and cutting-edge startups are all looking for a share of the expanding market, and Healthcare companies are right there with them.

Described as "the world's most powerful end-to-end encrypted communication device," Solaborate's HELLO 2 platform transforms any television set into a powerful communications device that responds intelligently to voice and touch. The device, which includes a 4K camera with night vision, wireless connectivity, sophisticated privacy controls, can turn a touch TV into a whiteboard and video conferencing platform. It can also integrate with Amazon Alexa and Google Home. And Solaborate is opening the device's platform to developers so that they can create extensions that will expand the device's functionalities. It is an AI-based device that translates a user's speech into another language and then back into the original. Powered by a quad-core processer, the Travis Touch works via 4G, Wi-Fi and Bluetooth, and can also function off-line.

A link has been posted to your Facebook feed. Can you fix voice recognition in new cars? After years of designing their own often-faulty voice recognition systems, auto companies are handing the reins over to tech giants that have already developed the technology for their devices. The trend is on full display at the 2019 Detroit auto show, where automakers are showcasing new vehicles with increasingly common systems that allow drivers to plug in their phones and bypass built-in infotainment systems. Using spoken commands to tune the radio, make a call or get directions has required patience, awkward pronunciation and frequent do-overs ever since it became possible in some vehicles earlier this century.

The new second generation Echo Show is bigger with a better display, but is size enough to keep Amazon ahead of stiff competition from Google? Since the original Echo Show launched last year the software has been refined, but the experience is broadly the same. The Show is a voice-first Alexa speaker, with touch interactivity as an additional input rather than the core experience. If you never wanted to touch the screen beyond the initial set-up,you wouldn't have to. When you do go to touch it, swipes and taps work as you might expect from a modern smartphone. Swipe down from the top to reveal a quick settings shade, including a smart home control panel, a place to trigger Alexa routines and one-tap access to alarms.

When you're lost, Siri can be your best friend. But if she can't retrieve the right address from your contacts, she can drive you crazy. And so it is with the legion of virtual personal assistants that are entering our lives. From Amazon's Alexa to Google's Home, people are busy talking to intelligent machines as never before. It's estimated that more than 60% of internet traffic is now generated by machine-to-machine, and person-to-machine, communication.

In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework.

ESPN is making some welcome (and arguably overdue) improvements to its ESPN service that could change how and where you watch. Its updated app now includes personalized recommendations for ESPN, starting with on-demand videos. You'll probably see more highlight clips from the latest NHL matches. Recommendations will "soon" spread to live and future events, so you might spot big matches you would otherwise miss. The company is also borrowing a page from Netflix and other services by introducing offline viewing.

From business innovations and media headlines to TV and movies, it seems that artificial intelligence (AI) is virtually everywhere. While still in its early stages across the financial services industry, AI adoption is expected to accelerate over the next few years. And it's expected to save companies big bucks. According to a recent study by Accenture, 77% of banks plan to use AI to automate tasks to a large extent in the next 3 three years. In addition, a recent study by Autonomous Next, indicates the potential cost savings of using AI could total $450 billion across the banking industry by 2030.

Google this week debuted a slew of new capabilities for its artificial intelligence software, Google Assistant, at CES in Las Vegas. One of the headliners was a preview of Google Assistant Connect. The new platform lets device manufacturers incorporate Google Assistant into their products easily and cost-effectively. Connect uses Google's existing smart home platform to expand to new device types, while making device setup and discovery easy for consumers. A manufacturer could create a continuous e-ink display projecting weather or calendar information, for example, while using Connect to drive content from a linked smart speaker.

Artificial Intelligence (AI) can and will underpin many of the upcoming changes affecting organizations as a whole, but especially IT departments. AI is being hailed as the cornerstone of the Fourth Industrial Revolution, with the potential to completely overhaul the way we live and work. Much of the focus around AI has been on the threat to jobs and displacing the need for humans; savvy organisations already realise that the real risk around this emerging technology is failure to embrace the productivity benefits on offer. IT departments not already involved in AI projects should move quickly to understand how the technology can improve their performance and that of the wider organization, as it will be down to them to embed AI across business applications and IT platforms. Thanks to recent advancements in machine learning, and the convergence of cloud compute power and big data, AI is finally making its way into the mainstream, with user-friendly systems available for organizations of all shapes and sizes.