Nest Audio costs $99.99 and comes in five colors. There hasn't been a new Google Home speaker since way back in 2016 and now Google is giving its OG smart speaker a major upgrade with the Nest Audio. We're still getting to know the Nest Audio, but here are some important specs to note: The speaker keeps the same fabric-wrapped look of previous Google speakers like the Nest Mini, but it takes on a more unique oval-shape and it's taller than many other smart speakers on the market. It doesn't come with any USB-C or auxiliary input ports, but unlike the Sonos One, it supports both WiFi and Bluetooth connection. As you'd expect in a modern smart speaker, the Nest can be paired with other Nest speakers for multi-room audio.

It's been three years since Google released a smart speaker with music quality in mind. The Google Home Max was the polar opposite of the Home Mini, a large and powerful speaker that could fill a room with great-sounding audio. But unsurprisingly, cheap and tiny smart speakers like the Home Mini and Amazon's Echo Dot have had an easier time gaining traction than more expensive options like Apple's HomePod and the Home Max. Google is trying to split the difference with the Nest Audio, a compact smart speaker that the company says will offer a robust music experience without breaking the bank. I've been testing the Nest Audio for a few days, and while it doesn't sound as good as bigger and more advanced music-focused speakers, I can say it's a major upgrade over something like the Nest Mini or Echo Dot.

The old Google Home that looked like an air freshener has been reinvented, renamed and redesigned to rock out. Now known as Nest Audio, the new editions look more like a tiny, traditional speaker, this time in a multitude of colors (pink, blue, green, white and black), sell for less than the original Home ($99.99 versus $129.99) and the big news is a major sound upgrade. Nest Audio, available today, is still being sold as a personal assistant to run your smart home, answer trivia questions, set reminders, get news updates, translate languages and, of course, play music and podcasts. But the speaker, which runs on the Google Assistant, still lags Amazon's Echo speakers and the Alexa system in doing many obvious tasks that various Google help pages claims it can do, but either can't or require so much setup that consumers will be stymied. But let's start with what does work well: playing music. For $99, you get a speaker with vastly improved sound than the original, in a slightly larger body.

The replacement for Google's original, now discontinued smart speaker is here, and after listening to it for a few days, I'm not missing the old Google Home one bit. The rectangular Nest Audio delivers truly impressive sound considering its $100 price tag, and its sturdy, fabric-covered design looks handsome in person. With Google Assistant on board, the Nest Audio can also respond to voice commands and take charge of smart home devices. Waiting in the wings, however, is Amazon's refreshed Echo speaker, which also costs $100 and packs in stereo (rather than mono) sound, plus a Zigbee smart home hub. From the moment pre-announcement photos of the speaker appeared online, the Nest Audio's industrial design provoked furrowed brows and even some derision; indeed, I wrote that it looks like a "potato sack" or "a pillow standing on end."

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.