This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ's high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data. Experiments show that D3Q significantly outperforms DDQ by controlling the quality of simulated experience used for planning. The effectiveness and robustness of D3Q is further demonstrated in a domain extension setting, where the agent's capability of adapting to a changing environment is tested.

Recommendation algorithms are widely adopted in marketplaces to help users find the items they are looking for. The sparsity of the items by user matrix and the cold-start issue in marketplaces pose challenges for the off-the-shelf matrix factorization based recommender systems. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper summarizes five lessons we learned from experimenting with state-of-the-art deep learning recommenders at the leading Norwegian marketplace \textit{FINN.no}. We design a hybrid recommender system that takes the user-generated contents of a marketplace (including text, images and meta attributes) and combines them with user behavior data such as page views and messages to provide recommendations for marketplace items. Among various tactics we experimented with, the following five show the best impact: staged training instead of end-to-end training, leveraging rich user behaviors beyond page views, using user behaviors as noisy labels to train embeddings, using transfer learning to solve the unbalanced data problem, and using attention mechanisms in the hybrid model. This system is currently running with around 20\% click-through-rate in production at \textit{FINN.no} and serves over one million visitors everyday.

Many well-established recommender systems are based on representation learning in Euclidean space. In these models, matching functions such as the Euclidean distance or inner product are typically used for computing similarity scores between user and item embeddings. This paper investigates the notion of learning user and item representations in Hyperbolic space. In this paper, we argue that Hyperbolic space is more suitable for learning user-item embeddings in the recommendation domain. Unlike Euclidean spaces, Hyperbolic spaces are intrinsically equipped to handle hierarchical structure, encouraged by its property of exponentially increasing distances away from origin. We propose HyperBPR (Hyperbolic Bayesian Personalized Ranking), a conceptually simple but highly effective model for the task at hand. Our proposed HyperBPR not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in Hyperbolic space.

Labor Day means a lot of things to a lot of people. For some, it honors America's strong labor force, and for others it means the autumn semester is starting and summer is over. For tech buyers, it's one of the best times to find good deals. A number of retailers are holding labor day sales for TVs, laptops, and other personal electronics. With some help from the team at TechBargains, we've highlighted some of the very best sales below.

Saint Louis University (SLU) has rolled out 2,300 Alexa-powered Echo Dot virtual assistants to all of its student living spaces to provide answers to university-related queries about events, speakers on campus and more. The university also plans to extend use of the artificial intelligence assistant into classrooms and meeting rooms in future and aims to use the technology to support workplace productivity for its faculty staff, according to CIO, David Hakanson. Students arriving at SLU this month can access a custom skill that answers questions relating to university services, such as "When does the library open?" or "Where is the registrar's office?" Already, 130 university-related queries can be answered using the Alexa, and the university is working on more. The idea is that Alexa can reduce the time it takes for students to access information online from seconds to minutes.

At CES 2017, Earin unveiled a revamped version of its true wireless earbuds, called M-2. Over a year and a half later, you can finally get your hands on them. The company was an early entrant into the completely wireless earbud market and although we had reservations about the earlier model, perhaps some of the features M-2 packs in could mean Earin has improved its product from the previous generation. The latest earbuds have Google Assistant integration, which you can activate with a long press. They can also handle calls (using a quartet of microphones that filter out background noise) and let you control your music.

Changing the channel on your television with only a spoken command will soon be possible for the owners of a new range of Toshiba televisions. Starting in 2019, the Japanese firm's OLED, 4K HDR, and Full HD Smart TV products will boast access to Amazon's Alexa voice assistant. Viewers will be able to request channel changes, volume increases and decreases and more by starting their spoken command with'Alexa'. This signals to the always-listening voice assistant that you are about to issue an instruction, which is recorded and parsed in the cloud by Amazon. Toshiba sets will have access to all 45,000 skills already available for Alexa, allowing television viewers to control smart lightbulbs, buy items from Amazon, order takeaways, and check the latest news and weather updates.

Google's AI assistant can now understand people even if they repeatedly switch between two languages during a conversation. This marks the first time an AI-powered voice assistant has been able to distinguish between two different languages during the same interaction. Google Assistant can now understand any pair of languages from English, German, French, Spanish, Italian, and Japanese. More languages are planned for a future update, Google says. Google hopes the upgrade will make using its Assistant easier in bilingual households, and says it could help people who are trying to learn a new language.

Artificially intelligent systems map our journeys, unlock our homes, feed us entertainment, and foretell the weather. But could our electronic assistants also start to learn our emotions and use that knowledge to serve us better? In other words, does Alexa know when you get mad? Close up of a boy's face aged 8 years wearing Clown make up face paint with rainbow markings on his arm. In fact, Amazon teams have been working on analyzing your emotions from your vocal intonations for over a year.

When Samsung launched the Galaxy Home speaker earlier this month, people were quick to point out how its name seemed ripped off from Google. Not to be outdone, Huawei is unveiling its own AI speaker here at IFA 2018, and it's clearly borrowed much more from the Google Home... just not the name. The AI Cube is a cylindrical speaker that looks like a stretched out version of Google's device, though it will offer Amazon's Alexa instead of Assistant. Like Samsung, Huawei is promising high-quality audio on its speaker. That's not all -- the AI Cube is also a 4G router.