After playing through some Billie Eilish tracks in Beat Saber, soon you'll also be able to kick back and listen to a limited-edition Echo Studio sporting the cover of her latest album, "Happier Than Ever." Beyond the beige fabric and Eilish's visage, the $230 speaker is no different than the standard $200 Echo Studio. That's a shame if you were hoping for some sort of upgrade, but if it's any consolation, we adored the Echo Studio's beefy hardware when it launched two years ago. It's one of the few smart speakers built for 3D Audio, and it has more than enough power to blast all of your favorite tunes. The Billie Eilish Limited-Edition Echo Studio also comes with a six-month subscription to Amazon Music, typically a $48 value.

Amazon Advertising helps companies build their brand and connect with shoppers, through ads shown both within and beyond Amazon's store, including websites, apps, and streaming TV content in more than 15 countries. Businesses or brands of all sizes including registered sellers, vendors, book vendors, Kindle Direct Publishing (KDP) authors, app developers, and agencies on Amazon marketplaces can upload their own ad creatives, which can include images, video, audio, and of course products sold on Amazon. To promote an accurate, safe, and pleasant shopping experience, these ads must comply with content guidelines. Can you figure out why two of the following ads would not be compliant? It also shows the same product multiple times.

GM will soon allow OnStar subscribers to contact emergency services through Alexa smart speakers. The company is bringing the OnStar Guardian Alexa skill to Amazon devices in the coming months. If you need emergency assistance, you're an OnStar member and the skill is active, you can say "Alexa, call for help." OnStar emergency-certified advisors can then call police or EMTs for you. Alexa devices don't support 911 calls otherwise, but you can set up an emergency contact.

If you are a student or a professional looking for various open-source Natural Language Processing (NLP) projects, then, this article is made to help you. The NLP projects listed below are categorized in an experience-wise manner. All of these projects can be implemented using Python. Text Summarizer is a project that can summarize long paragraphs of text into a single line summary. It can turn an article into a summary using Python and Keras library.

How to predict precise user preference and how to make efficient retrieval from a big corpus are two major challenges of large-scale industrial recommender systems. In tree-based methods, a tree structure T is adopted as index and each item in corpus is attached to a leaf node on T . Then the recommendation problem is converted into a hierarchical retrieval problem solved by a beam search process efficiently. In this paper, we argue that the tree index used to support efficient retrieval in tree-based methods also has rich hierarchical information about the corpus. Furthermore, we propose a novel context-aware tree-based deep model (ConTDM) for recommender systems. In ConTDM, a context-aware user preference prediction model M is designed to utilize both horizontal and vertical contexts on T . Horizontally, a graph convolutional layer is used to enrich the representation of both users and nodes on T with their neighbors. Vertically, a parent fusion layer is designed in M to transmit the user preference representation in higher levels of T to the current level, grasping the essence that tree-based methods are generating the candidate set from coarse to detail during the beam search retrieval. Besides, we argue that the proposed user preference model in ConTDM can be conveniently extended to other tree-based methods for recommender systems. Both experiments on large scale real-world datasets and online A/B test in large scale industrial applications show the significant improvements brought by ConTDM.

Low-rank matrix approximation is one of the central concepts in machine learning, with applications in dimension reduction, de-noising, multivariate statistical methodology, and many more. A recent extension to LRMA is called low-rank matrix completion (LRMC). It solves the LRMA problem when some observations are missing and is especially useful for recommender systems. In this paper, we consider an element-wise weighted generalization of LRMA. The resulting weighted low-rank matrix approximation technique therefore covers LRMC as a special case with binary weights. WLRMA has many applications. For example, it is an essential component of GLM optimization algorithms, where an exponential family is used to model the entries of a matrix, and the matrix of natural parameters admits a low-rank structure. We propose an algorithm for solving the weighted problem, as well as two acceleration techniques. Further, we develop a non-SVD modification of the proposed algorithm that is able to handle extremely high-dimensional data. We compare the performance of all the methods on a small simulation example as well as a real-data application.

How many times has this happened that you would've had a conversation about this particular thing and the next thing you know is all your social media apps and web browsers are displaying advertisements related to it. It almost feels like a nexus. It's not even an exaggeration when I say sometimes, I am shown ads of things I've just thought about in my head. The main question that pops up is whether our so very smartphones are listening to us? The vulnerability begins with voice assistants always having their ear at us with the "always-on" feature to pick up words like "Hello Siri" or "Alexa" or "Ok Google".

Apple has introduced new sharing and personalized recommendation features for Podcasts on iOS 15, all meant to help you discover new shows to listen to. Starting today, you'll find personalized recommendation sections in the Listen Now page that show you podcasts similar to the ones you enjoy. They'll be entitled "If You Like '[Show Name]'..." and then list titles in the same category or with the same theme or same format. They could also list shows from the same studio or titles other users listening to that particular podcast are also following. You'll find new sections with recommendations based the topics you usually enjoy, as well.

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.

Nate and his teams specialize in custom software development, web design and digital marketing. Artificial intelligence (AI) conjures a lot of unique imagery depending on your background and how much exposure to the topic you've had. For some people, AI looks and feels like something out of The Terminator or 2001: A Space Odyssey -- an oppressive, human-like intelligence that poses an existential threat. To some, AI is simply a fancy name for digital assistants like Siri or Cortana. Of course, if you have a lot of development experience, you know the line between AI and any other kind of computer programming is blurry and that in some ways, a basic pocket calculator could be considered AI.