Voice technology is changing how we surf the net, buy products and interact with our devices. Already, we can search for coffee shops while we're driving and order pizza from our home automation devices. In the coming years, we'll be able to talk to our refrigerators, ask our fitness trackers questions, have conversations with our TVs and so much more. As people ditch their touchpads and keyboards, businesses will have to adapt. Smart leaders and entrepreneurs have already started using voice technology to their advantage.

If there's one common theme at this year's CES 2019 – the Las Vegas tech expo we've been covering all week – it's talking. Not just chatting on the show floor (though there's been plenty of that too). By talking we mean the sheer avalanche of devices arriving with support for Amazon Alexa, Google Assistant and so many other competing voice assistants. In fact, everything from motorcycle helmets, water bottles and phone chargers to doorbells, pianos and toilets at least now has the option of being controlled by voice. "Getting rid of the remote control is one of the strongest use cases for voice technologies," says Mark Lippett, CEO at XMOS, whose farfield voice tech is used in soundbars, Freebox and Skyworth TVs to allow them connect to Alexa.

But: "Vulnerabilities in voice assistants like Microsoft's Cortana and Amazon's Alexa are perfect examples of how the rush to simplify can cause complicated problems down the road".

We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task. The two tasks are associated by cross&compress units, which automatically share latent features and learn high-order interactions between items in recommender systems and entities in the knowledge graph. We prove that cross&compress units have sufficient capability of polynomial approximation, and show that MKR is a generalized framework over several representative methods of recommender systems and multi-task learning. Through extensive experiments on real-world datasets, we demonstrate that MKR achieves substantial gains in movie, book, music, and news recommendation, over state-of-the-art baselines. MKR is also shown to be able to maintain a decent performance even if user-item interactions are sparse.

In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period. The users respond by selecting one of the items recommended or abandon the platform due to fatigue from seeing less useful items. Assuming a parametric stochastic model of user behavior, which captures positional effects of these items as well as the abandoning behavior of users, the platform's goal is to recommend sequences of items that are competitive to the single best sequence of items in hindsight, without knowing the true user model a priori. Naively applying a stochastic bandit algorithm in this setting leads to an exponential dependence on the number of items. We propose a new Thompson sampling based algorithm with expected regret that is polynomial in the number of items in this combinatorial setting, and performs extremely well in practice. We also show a contextual version of our solution.

For example, Netflix engages its audience by making customized content recommendations to each user. The company reported in 2012 that three-quarters of viewings originated from such suggestions. To make personalized recommendations at such scale, the company leverages an autonomous, integrated learning system that receives feedback (in the form of user ratings or viewer behavior) and updates suggestions accordingly. And it deliberately introduces variation into its recommendations, allowing new behaviors to emerge and letting the data speak for itself. For example, Amazon's dozens of data science systems are closely integrated, which allows decision engines to react to new data immediately and consistently.

Artificial intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually intensive tasks. CRM and Configure, Price & Quote (CPQ) providers continue to develop and fine-tune their digital assistants, which are specifically designed to help the sales team get the most value from AI and machine learning. Salesforces' Einstein supports voice-activation commands from Amazon Alexa, Apple Siri, and Google. Salesforce and other enterprise software companies continue to aggressively invest in research & development (R&D).

Beyond the lip service, I feel like we are in a trough of disillusionment on so many pivotal technologies. In particular, there are five emerging fields which have garnered tremendous excitement over the past couple of years, and yet this excitement is likely to die down in the next year as the reality sets in that the real impact of these technologies is still a good few years away. It is useful to understand these fading trends because while rising trends can be obvious, to take advantage of many of them in 2019, you needed to have acted a while ago. However, it is not too late to avoid wasting time and resources on these non-trends, at least for 2019, unless you are making significant long-term bets in which case, load up. It's useful to understand why we have hype and then disappointment before the real impact kicks in.

Since the first website launched in 1991, web development has been an ever-evolving trade. Although it's come a long way in 27 years, it's likely we haven't seen anything yet. If innovations like artificial intelligence and variable fonts gain more widespread use, the future of web design is going to be a wild ride. Whether you know it or not, you probably interact with artificial intelligence every day. Google's algorithm uses AI to provide you with relevant search results.