The company has invited press to its Seattle headquarters for a secretive hardware event, which starts at 1 p.m. ET. It could unveil eight new Echo devices, a device for the car and even a microwave, according to a CNBC report. More Echos make sense for the company. The reign of Alexa is only beginning for Amazon. It wants the chipper voice assistant to be available in more places, including cars and the kitchen.

Where will you meet your next bae? If Facebook has anything to do with it, it'll be through its new dating feature, which it's now testing in Colombia. Announced back in May at the F8 developer's conference, Facebook wants to help its 200 million single users find more meaningful, deeper connections – and given its monopoly in online social interactions, it's pretty well-placed to do that. Online dating is hardly anything new, though, so what sets Facebook's dating feature apart from all the rest? Well, Facebook obviously has access to mountains of data on you that other dating sites don't. Your likes, the types of events you go to, the places you hang out, the circles you move in -- all of this intel will help glean better matches than apps that rely on looks, for example.

For those who've been wondering just when Amazon's mysterious event is going to be, you need wait no longer. That day is today, and it'll take place in the company's Seattle headquarters at 1:30 PM ET / 10:30 AM PT. We don't know too much about what will be unveiled just yet, but if it's anything like last year's event, chances are good that Alexa will be the prevailing theme. According to a CNBC report earlier this week, at least eight new devices will be unveiled at the event. They include microwave oven, a subwoofer, an amplifier, an in-car gadget of some kind as well as a smart plug that'll add Alexa functionality to anything that hooks into it.

Artificial intelligence is one of the most compelling areas of computer science research. AI technologies have gone through periods of innovation and growth but never has AI research and development seemed as promising as it does now. This is due in part to amazing developments in machine learning, deep learning, and neural networks. Machine learning, a cutting-edge branch of artificial intelligence, is propelling the AI field further than ever before. While AI assistants like Siri, Cortana, and Bixby are useful, if not amusing, applications of AI, they lack the ability to learn, self-correct, and self-improve.

Facebook begins publicly testing its online-dating product, called Dating, in Colombia today. The service was first announced at the annual F8 conference in May this year, and will likely be available in other locations in the future. For now, users aged 18 and older in Colombia will be able to create dating profiles and, once those reach a critical mass, find some matches. WIRED got to preview an early version of the service, and it looks promising--especially for users looking for meaningful long-term relationships rather than hookups. In other words, you can expect to find exactly zero swiping.

The scene of the woman talking to Alexa is drawn from a 2017 promotional video advertising the latest version of the Amazon Echo. The video begins, "Say hello to the all-new Echo" and explains that the Echo will connect to Alexa (the artificial intelligence agent) in order to "play music, call friends and family, control smart home devices, and more." The device contains seven directional microphones, so the user can be heard at all times even when music is playing. The device comes in several styles, such as gunmetal grey or a basic beige, designed to either "blend in or stand out." But even the shiny design options maintain a kind of blankness: nothing will alert the owner to the vast network that subtends and drives its interactive capacities.

You might just be looking at some of the Alexa-powered devices due to show at Amazon's rumored hardware event this month. Pocket-lint has spotted listings (since pulled) for the Echo Sub wireless subwoofer (above) and Smart Plug (below), both of which see Amazon venturing into unfamiliar territory. The Sub adds 100W of bass to an Echo or Echo Plus setup, whether it's one speaker or two -- yes, stereo pairing would also be new. It reportedly cost £75 (about $99) when it shipped on October 11th, which might be a small price to pay if your rap or trance isn't sufficiently room-shaking. The Smart Plug, meanwhile, is fairly self-explanatory: it's a wall wart that brings basic Alexa voice control to any device, no smart home hub required.

Amazon is giving users a new way to shop. The tech giant has launched'Scout,' a new unit of its sprawling e-commerce site, that uses machine learning to serve up personalized product recommendations. Users simply click the thumbs up or thumbs down button and Amazon will respond by showing other products based on their choices. Amazon has launched'Scout,' a new unit of its sprawling e-commerce site, that uses machine learning to serve up personalized product recommendations based on users' interests The firm hasn't yet promoted the site, but it's being tested on the Amazon site and in the app, according to CNBC, which first spotted the move. For now, Scout is limited to offering recommendations for home furniture, kitchen and dining products, women's shoes, patio furniture, lighting and bedding.

Eighteen teams were selected for the inaugural competition last year. To build their socialbots, the students combined state-of-the-art techniques with their own novel strategies in the areas of natural language understanding and conversational AI. This article reports on the research conducted over the 2017-2018 year. While the 20-minute grand challenge was not achieved in the first year, the competition produced several conversational agents that advanced the state of the art, that are interesting for everyday users to interact with, and that help form a baseline for the second year of the competition. We conclude with a summary of the human conversation have applicability in both work that we plan to address in the second year of professional and everyday domains. The first generation of such assistants -- Amazon's Alexa, Apple's Siri, Google The Alexa Prize competition received hundreds of Assistant, and Microsoft's Cortana -- have been applications from interested universities. After a focused on short, task-oriented interactions, such as detailed review of the applications, Amazon playing music or answering simple questions, as announced 12 sponsored and 6 unsponsored teams opposed to the longer free-form conversations that as the inaugural cohort for the Alexa Prize. The teams occur naturally in social and professional human that went live for the 2017 competition, listed alphabetically interaction. Conversational AI is the study of techniques by university, were DeisBot (Brandeis University), for creating software agents that can engage Magnus (Carnegie Mellon University), in natural conversational interactions with humans.

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.