Artificial Intelligence (AI) conversational platforms are changing the manner in which organizations engage their clients and empower their employees. Modern day's intelligent assistants are now loaded with skills. They can check the climate, traffic and sports scores. They can play music, interpret words and send text messages. They can even do the math, make jokes and read stories.

Samsung, Amazon, Roku, Google, Sony, Apple and Microsoft have unveiled their fall lineups of new tech gear with one biggie awaiting a reveal: those new editions of the Apple iPhone, which is expected later this month. Perhaps you missed the announcements. Or wondered when the products would be available. We have our annual fall calendar for you, right here. Several new ones products have already been released, including the updated Series 6 Apple Watch, Amazon's Fire TV streaming players (some so bare-bones, they don't have TV controls or 4K streaming support) and a faster, more powerful $329 iPad.

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.

Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users -- not just the majority group -- and they also need to be fair in how they divide exposure among the items. These fairness considerations can partially be met by adding diversity to the rankings, as done in several recent works, but we show in this paper that user fairness, item fairness and diversity are fundamentally different concepts. In particular, we find that algorithms that consider only one of the three desiderata can fail to satisfy and even harm the other two. To overcome this shortcoming, we present the first ranking algorithm that explicitly enforces all three desiderata. The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally. From its solution, a ranking policy can be derived via a new Birkhoff-von Neumann decomposition algorithm that optimizes diversity. Beyond the theoretical analysis, we provide a comprehensive empirical evaluation on a new benchmark dataset to show the effectiveness of the proposed ranking algorithm on controlling the three desiderata and the interplay between them.

End-to-end task-oriented dialogue systems aim to generate system responses directly from plain text inputs. There are two challenges for such systems: one is how to effectively incorporate external knowledge bases (KBs) into the learning framework; the other is how to accurately capture the semantics of dialogue history. In this paper, we address these two challenges by exploiting the graph structural information in the knowledge base and in the dependency parsing tree of the dialogue. To effectively leverage the structural information in dialogue history, we propose a new recurrent cell architecture which allows representation learning on graphs. To exploit the relations between entities in KBs, the model combines multi-hop reasoning ability based on the graph structure. Experimental results show that the proposed model achieves consistent improvement over state-of-the-art models on two different task-oriented dialogue datasets.

Hold music could one day be a thing of the past thanks to a service coming to Google's smartphones. Hold for Me, which launches on Thursday in the US for owners of Google's Pixel 5 and Pixel 4a phones, involves Google's AI tools taking over as an automatic secretary when on hold to a call centre, leaving the user free to put down the phone and carry on with their life. The service will listen out for when the call is picked up and send a notification when it's time for the user to get back on the phone. In the meantime, Google's assistant will ask the call centre to hold, hopefully stopping them from hanging up because of dead air. "Every business's hold loop is different and simple algorithms can't accurately detect when a customer support representative comes on to the call," Google's Andrew Goodman and Joseph Cherukara said.

This week brought a handful of good tech deals along with the announcement of Amazon Prime Day 2020. While Amazon's annual shopping event will take place on October 13 and 14, there are still some deals worth considering right now (not to mention, a couple of early Prime Day deals for members). Here are the best deals from this week that you can still get today. Prime members can get two 3rd-generation Echo Dots for $20 each when they use the code DOTPRIME2PK at checkout. This brings the price per Echo Dot down to its lowest ever, making this a great deal if you don't absolutely need the redesigned Echo Dot Amazon announced last week.

Where our mission is to create a legacy of wisdom, to seek out discernment and insights, to boldly grow where few have chosen to grow before. Hello, my friend, I am Guthrie Chamberlain, your captain on our journey to increase Wisdom and Create a Living Legacy. Thank you for joining us today as we explore wisdom on our 2nd millennium of podcasts. Today is Day 1488 of our Trek, and our focus on Fridays is the future technological and societal advances, so we call it Futuristic Fridays. My personality is one that has always been very future-oriented.

The period of voice-enabled search and commerce has shown up, and organizations need to prepare themselves for this new paradigm. By implanting brands into conversations, organizations can give customized solutions for pretty much any issue. Voice frameworks have quickly advanced from the simple voice commands to entire ecosystems of applications and interactions. Intelligent speakers like Amazon Echo, Apple Home Pod, and Google Home give various entertainment choices inside your home. These voice AI-empowered systems have cleared another dimension to execute tasks that were initially limited to a screen.

Parents want the best for their children. While the goal is apparent, figuring out how to do it is challenging. Parents want their kids to be healthy, happy, secure, smart, sociable, smart, athletic, etc. That is a lot to do considering that they have to balance it out based on how quality time they can make for their child, how much resources they can put in to child development, meeting socio-economic needs, etc. With everyone turning to artificial intelligent assistants for help, could there be an AI digital assistant for parents doing one of the most human of things: raising their children?