You can now use Google Assistant voice controls to navigate Disney content on smart displays like Nest Hub and Nest Hub Max. To use the feature, you'll have to link your Disney subscription to your Google Home or Assistant app. Then, just say something like "Hey Google, play The Mandalorian," to stream content. From the start, Disney has been available on Google Assistant smart displays like Nest Hub. You can already use Assistant to play Netflix, Hulu, CBS All Access and HBO content, so it only makes sense that the same feature would be available for Disney .

Two intriguing functions in Microsoft Outlook, Play My Emails and the Daily Briefing, are scheduled to receive updates that add new Cortana-powered AI to functions that go far beyond just email. Microsoft's AI assistant, Cortana, has moved from being an integral part of Windows to more of an assistive technology. Outlook has been the main beneficiary, however, new Cortana-powered intelligence is coming to Teams, and to the Cortana app within Windows 10 as well. One of the problems with Microsoft's parade of new features is simply keeping track of the timeline. The Daily Briefing functionality debuted in preview mode a year ago, but at the Microsoft Ignite conference it's now becoming officially available for Microsoft 365 users with Exchange Online mailboxes.

Please don't complain to me about literally anything if you've touched human flesh since March. Being very single, I have not, and my Grubhub guy doesn't want a hug. So I am doomed, instead, to online dating in the context of a pandemic. Let me walk you through the torture. It starts typically enough, with endless scrolling through profiles of now-offensively-irrelevant travel photos.

Dive into the data science & machine learning behind Netflix's recommendation algorithms.

Car manufacturers are catching on the new tech wave. After the pandemic hit and social-distancing became our new normal, a personal vehicle became one of the safest ways to travel -- with proper sanitation, of course. And as we're all moving to a touchless future, the automotive industry is no exception. According to the study by Voicebot.ai While analysts from Frost & Sullivan predict that the importance of digital voice assistants in automotive branding will increasingly grow. We believe that in the next few years, voice technology will become one of the key drivers transforming the automotive industry.

The Cairo-based startup Elves has raised $2 million in seed funding from Egyptian VC fund Sawari Ventures. According to Menabytes, part of the investment was received in February and the other in July. Sawari Ventures was the investor in both cases. If we are to factor in this recent investment, the startup will have thus far raised $5 million. In 2017, Elves raised a record sum of investment in what was MENA region's largest seed round.

Dialogue systems, also called chatbots, are now used in a wide range of applications. However, they still have some major weaknesses. One key weakness is that they are typically trained from manually-labeled data and/or written with handcrafted rules, and their knowledge bases (KBs) are also compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, the level of user satisfactory is often low. In this paper, we propose to dramatically improve this situation by endowing the system the ability to continually learn (1) new world knowledge, (2) new language expressions to ground them to actions, and (3) new conversational skills, during conversation or "on the job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and are better and better able to understand diverse natural language expressions and improve their conversational skills. A key approach to achieving these is to exploit the multi-user environment of such systems to self-learn through interactions with users via verb and non-verb means. The paper discusses not only key challenges and promising directions to learn from users during conversation but also how to ensure the correctness of the learned knowledge.

Stanford researchers hope their open virtual assistant Almond can increase competition and better protect privacy.

The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to capture item transitions within it, ignoring complex transitions between items in different sessions that have been generated by other users. These item transitions include potential collaborative information and reflect similar behavior patterns, which we assume may help with the recommendation for the target session. In this paper, we propose a novel method, namely Dual-channel Graph Transition Network (DGTN), to model item transitions within not only the target session but also the neighbor sessions. Specifically, we integrate the target session and its neighbor (similar) sessions into a single graph. Then the transition signals are explicitly injected into the embedding by channel-aware propagation. Experiments on real-world datasets demonstrate that DGTN outperforms other state-of-the-art methods. Further analysis verifies the rationality of dual-channel item transition modeling, suggesting a potential future direction for session-based recommendation.

Recommendation has been a long-standing problem in many areas ranging from e-commerce to social websites. Most current studies focus only on traditional approaches such as content-based or collaborative filtering while there are relatively fewer studies in hybrid recommender systems. Due to the latest advances of deep learning achieved in different fields including computer vision and natural language processing, deep learning has also gained much attention in Recommendation Systems. There are several studies that utilize ID embeddings of users and items to implement collaborative filtering with deep neural networks. However, such studies do not take advantage of other categorical or continuous features of inputs. In this paper, we propose a new deep neural network architecture which consists of not only ID embeddings but also auxiliary information such as features of job postings and candidates for job recommendation system which is a reciprocal recommendation system. Experimental results on the dataset from a job-site show that the proposed method improves recommendation results over deep learning models utilizing ID embeddings.