Google Assistant turns three this year. Whether you love it or not, it's the AI for everyone. Google's intelligent little helper has been powering almost every other smartphone for the past few years, getting better and smarter each day. Despite its dominance on mobile devices, Google shows no signs of slowing down. Aggressive marketing, widespread device integration and new innovations show where Google is placing its eggs.

Artificial Intelligence (AI) is the new electricity of our times. That's what Chris Duffey, creative technologist says about this incredible technology revolutionizing industries the world over. His new book Superhuman Innovation showcases how AI will supercharge the workforce, the world of work, and can be harnessed to deliver powerful change. It is a practical guide to how AI and Machine Learning are impacting not only how businesses, brands, and agencies innovate, but also what they innovate: products, services and content. In this world of product and pricing parity, the delivery of superior service experience has become the new marketing, and the new real competitive edge. Superhuman Innovation discusses how AI will serve the superstar innovators of tomorrow by enabling them to see deeper insights and set sail for higher goals.

Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery. Machine learning methods utilized in these applications would be better served with specialized embedding techniques. We propose two embeddings for bipartite graphs that decompose edges into sets of indirect relationships between node neighborhoods. When sampling higher-order relationships, we reinforce similarities through algebraic distance on graphs. We also introduce ensemble embeddings to combine both into a "best of both worlds" embedding. The proposed methods are evaluated on link prediction and recommendation tasks and compared with other state-of-the-art embeddings. Our embeddings are found to perform better on recommendation tasks and equally competitive in link prediction. While being all highly beneficial in applications, we demonstrate that none of the existing state-of-the-art or our embeddings is clearly superior (in contrast to what is claimed in many papers), and discuss the trade offs present among them.

Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive matrix completion (IMC), was further proposed. In real applications, the factorized version of IMC is more favored due to its efficiency of optimization and implementation. Regarding the factorized version, traditional IMC method can be interpreted as learning an individual representation for each feature, which is independent from each other. Moreover, representations for the same features are shared across all users/items. However, the independent characteristic for features and shared characteristic for the same features across all users/items may limit the expressiveness of the model. The limitation also exists in variants of IMC, such as deep learning based IMC models. To break the limitation, we generalize recent advances of self-attention mechanism to IMC and propose a context-aware model called collaborative self-attention (CSA), which can jointly learn context-aware representations for features and perform inductive matrix completion process. Extensive experiments on three large-scale datasets from real RS applications demonstrate effectiveness of CSA.

Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student's ability and the relative difficulty of learning materials. This makes it hard to recommend appropriate content that matches the student's prior knowledge. In this paper, we propose a refined hierarchical knowledge structure to model vocabulary knowledge, which enables us to automatically organize the authentic and up-to-date learning materials collected from the internet. Based on this knowledge structure, we then introduce a hybrid approach to recommend learning materials that adapts to a student's language level. We evaluate our work with an online Japanese learning tool and the results suggest adding adaptivity into material recommendation significantly increases student engagement.

Cross-domain recommendation has long been one of the major topics in recommender systems. Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxilliary contents, e.g., images and review texts, and the patterns in the rating matrix itself is rarely touched. In this work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model (DARec) that is capable of extracting and transferring patterns from rating matrices {\em only} without relying on any auxillary information. We empirically demonstrate on public datasets that our method achieves the best performance among several state-of-the-art alternative cross-domain recommendation models.

Some people use the terms "artificial intelligence" and "machine learning" interchangeably, but they're not the same thing. To put it simply, artificial intelligence is a broad computer science concept that encompasses the idea of machines displaying cognitive abilities. These abilities range from visual perception and speech recognition to decision-making. Think anything from Amazon's Alexa to Hanson's Robotics' Sophia. Machine learning, on the other hand, is only one of the applications (or subfields) of AI.

I'm the Azure content lead at Cloud Academy and I have over 10 years of experience with cloud technologies. If you have any questions, feel free to connect with me on LinkedIn and send me a message or send an email to support@cloudacademy.com. This course is intended for people who are interested in artificial intelligence services on Azure especially recommendation engines. To get the most from this course, it would be helpful to have some experience using Azure. Ideally, you should also have some experience using APIs, although that's not strictly necessary.

In this video, you'll learn about Microsoft's Product Recommendation Solution. Watch the full course https://cloudacademy.com/course/build... to learn how to use artificial intelligence to add product recommendations to your website using Azure resources. You'll learn the essentials of building, deploying and testing a recommendation engine on Microsoft Azure. You will also build skills to fine-tune a recommendation model and evaluate its effectiveness. Some Azure and API experience is recommended.

In this episode, we talk to Nick Saretzky, Senior Director of Project Management at Tinder, about how dating apps started out with data, most recently with Tinder data. We discuss the benefits of driving change through data insights, and what user data Tinder has at its disposal. We also talk about the impact of dating apps on how people interact, and on the changing approach to modern relationships. Listen to this episode on Spotify, iTunes, and Stitcher. You can also catch up on the previous episode of the Hub & Spoken podcast, in which Jason spoke to Kerry Dawes, Director of Digital Customer Experience at The Rank Group, on the impact of data on the digital customer experience in gambling.