Lately, there is a lot of discussion happening on Artificial Intelligence (AI) and its capabilities in web designing. Believe it or not, AI can do anything. Of course, we can see that many companies are experimenting creative things related to Artificial Intelligence in all sorts of online forums and articles. Nowadays, customers are expecting personalization at every level. They are looking for something that is easy to follow.

Unless you've been living under a rock, you've probably run across the Vocabulary.com Perhaps you even weighed in, offering your two cents on the elocution of the opera singer (a member of the original Broadway cast of Cats, as it turns out) in the recording. But you probably didn't consult artificial intelligence for a second opinion. Well, not to worry: Nuance and Voxbone have saved you the trouble. Nuance Communications, a company that specializes in natural language processing, fed its Dragon speech platform the "Laurel" or "Yanny" audio clip to put an end to the debate once and for all.

The world is teeming with intelligence, from little wormy grubs in the garden to physicists poring over equations in university offices. In the past few years we've also come to view our virtual assistants as possessing some kind of intelligence--imperfect and sometimes downright creepy, but intelligence nonetheless. A.I. has come a long way since Microsoft's Clippy. Whether we're talking to Siri like a friend or asking our dogs for advice, humans love to imagine other animals' intelligence. As we enter into the infancy of A.I., it's fun to speculate how some existing lifeforms stack up to our best A.I so far.

In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either 1) incorporate content information into collaborative filtering to perform hybrid recommendation, or 2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leverage both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.

Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms (e.g. Taobao). Generally speaking, two essential modules named "Click-Through Rate Prediction" (CTR) and "Conversion Rate Prediction" (CVR) are included, where CVR module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multi-Level Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (GBDT) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding GBDT as the input feature representation for next level GBDT, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level GBDT corresponds to the combination of certain traversals in the preceding GBDT. The deep cascade structure and the combination rule enable the proposed ldcTree to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble ldcTree (E-ldcTree) to encourage the model's diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on EldcTree (F-EldcTree) for taking adequate use of weak and strong correlation features identified by pre-trained GBDT models. Experimental results on off-line dataset and online deployment demonstrate the effectiveness of the proposed methods.

Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' preferences are dynamic. This inevitably costs a recommender system consistent suboptimal performance. In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants. In accordance, we propose a contextual bandit algorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in such a non-trivial environment. Extensive empirical evaluations on both synthetic and real-world datasets for recommendation confirm its practical utility in a changing environment.

Though not one to give much away, Apple's voice assistant Siri has hinted she may be undergoing a major upgrade ahead of the technology company's annual developer conference. Australian site the Apple Post noted that if a user asks Siri about WWDC, she won't provide any useful information but will instead drop cryptic hints.

As digital assistants improve, we're learning new things to expect from them, but the tasks that a real-life assistant may have handled before can still be a bit of a challenge to home assistants. Amazon's Alexa voice assistant is gaining functionality to help it get smarter about working with your calendar. The new abilities will let users move appointments around and schedule meetings based on other people's availability. If you've been shared on someone's calendar availability, Alexa will be able to suggest times that work for both of you. Just say, "Alexa schedule a meeting with [name]" and Amazon's assistant will search through your schedule for a good time, suggesting up to two time slots that could work.

We're already used to virtual assistance like Siri and Alexa, and with more sophisticated forms of artificial intelligence these virtual assistants will be …

By Will Yang Technology in today's day and age has enabled a human to do things and accomplish far more than one could think of a few years back. Thanks to rapidly evolving and innovative technologies, personal lives have become more enriched. Meaningful collaborations between a human and machine/technology has in many ways provided a wealth of opportunities to us making our lives comfortable. One such technology buzzword in the industry today is Artificial Intelligence. Once a topic for science fiction, Artificial Intelligence technology is now being used by brands across industries and categories.