So many streaming services and so little time to find the one that suits you best. What's a music lover to do? Talking Tech has you covered. Which of the monthly streaming music services makes the best recommendations, is easiest to use and has the best prices? After the newest kid on the block, YouTube Music Premium, debuted in a soft launch this week, we set to find out, comparing YouTube to the Big 3: No. 1 Spotify (75 million subscribers,) Apple Music (50 million) and Amazon, which won't be more specific other than to say it has "tens of millions" of users. For several days, we have been searching for our favorites, looking for clues to discover stuff we didn't know about, creating playlists, looking for song recommendations and playing the music on the computer, phone and through Echo, Google Home, Apple HomePod and Sonos speakers.

Independent Women's Forum's Nan Hayworth and Democratic strategist Wendy Osefo discuss the report that researchers discovered a way to hack Amazon's Alexa.

For a company as big and sprawling as Google, an annual developer conference can feel overwhelming. Google offers a dizzying number of services and dabbles in almost every consumer tech industry under the sun, including seven core products with more than a billion users each. But the company has a measured tempo when it comes to Google I/O, and we've come to understand how it prioritizes certain products over others come May every year. At this year's I/O, which will be held again at the Shoreline Amphitheater in Mountain View, California starting Tuesday, May 8th, we know we'll be hearing about the future of Android and Google's artificial intelligence efforts. But there will also be news on everything from its new wearable platform, Wear OS, and Google Assistant to Android TV, Google Home, Google Play, and Search. This is the time of the year when Google pulls out all the stops to showcase how its software is smarter and more forward-looking than the products from its rivals Amazon, Apple, Facebook, and Microsoft.

Microsoft is acquiring conversational AI startup Semantic Machines in an effort to make bots and intelligent assistants like Cortana sound and respond more like humans. Founded in 2014, Semantic Machines uses machine learning to make bots respond in a more natural way to queries. Semantic Machines is led by UC Berkeley professor Dan Klein and former Apple chief speech scientist Larry Gillick. Both are considered pioneers in conversational AI. Microsoft's acquisition will boost the company's Cortana digital assistant, as well as the company's Azure Bot Service that's used by 300,000 developers.

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.