Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.

Virtual Assistants can be quite literal at times. If the user says "tell Bob I love him," most virtual assistants will extract the message "I love him" and send it to the user's contact named Bob, rather than properly converting the message to "I love you." We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.

One thing that makes voice-activated helpers like Google Assistant so powerful is that you don't need to interact with a visual software interface to get the most out of its functionality. For those who have trouble seeing or don't have fine motor control, the fact they can use their voice to communicate with Assistant means they can get just as much out of the software as anyone else. But not everyone can speak easily. So to make Assistant even more accessible, Google is partnering with Tobii Dynavox. The company makes software and devices designed to help those with speech and language disabilities.

Recommender systems have been part of the Internet for almost two decades. Dozens of vendors have built recommendation technologies and taken them to market in two waves, roughly aligning with the web 1.0 and 2.0 revolutions. Today recommender systems are found in a multitude of online services. They have been developed using a variety of techniques and user interfaces. They have been nurtured with millions of users' explicit and implicit preferences (most often with their permission).

In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. In this article, we discuss how our practical system architecture was designed to leverage Autodesk's existing Customer Involvement Program (CIP) data to deliver in-product contextual recommendations to end-users.

To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5 million dollar competition that challenges university teams to build conversational agents, or "socialbots", that can converse coherently and engagingly with humans on popular topics for 20 minutes. The Alexa Prize offers the academic community a unique opportunity to perform research at scale with real conversational data obtained by interacting with millions of Alexa users, along with user-provided ratings and feedback, over several months. This enables teams to effectively iterate, improve and evaluate their socialbots throughout the competition. Sixteen 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.

From Shakey the Robot to self-driving cars, from the personal computer to personal assistants on our phones, the Defense Advanced Research Projects Agency (DARPA) has led the development of integrated artificial intelligence (AI) systems for more than half a century. From the earliest days of AI, it was apparent that a robust, generally intelligent system should include a complete set of capabilities: perception, memory, reasoning, learning, planning, and action; and when DARPA initiated AI research in the 1960s, ambitious projects such as Shakey the Robot went after the complete package. As DARPA realized the challenges, they backed away from the ultimate goal of integrated AI and tried to make progress on the individual problems of image understanding, speech and language understanding, knowledge representation and reasoning, planning and decision aids, machine learning, and robotic manipulation. Yet, even as researchers struggled to make progress in these subdisciplines, DARPA periodically resurrected the challenge of integrated intelligent systems and pushed the community to try again. In the 1980s, DARPA's Strategic Computing Initiative took on challenges of integrated AI projects such as the Autonomous Land Vehicle and the Pilot's Associate.

Amazon's most compact smart display is now the cheapest it's ever been. The Echo Show 5 dropped to $45 today, which is 50 percent off its normal price. The last time it was even close to that price ($50 to be precise) was back in May. This is an early Prime Day deal, so you must be a Prime member to get the savings (and yes, you're still able to get a 30-day free trial of Prime if you're a new subscriber). The Echo Show 5 was one of the many Alexa-enabled devices made by Amazon that did not receive an update two weeks ago.

As one of the leading trends in technology, Artificial Intelligence (AI) continues to gain in popularity for marketers and sales professionals, and has grown to be an essential tool for brands that wish to provide a hyper-personalized, exceptional customer experience. The availability of AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software has brought AI to the enterprise without the high costs that were previously associated with the technology. A report on the Future of Work from RobertHalf indicated that 39% of IT leaders are currently using AI or machine learning, 33% said that they expect to use AI within the next three years, and 19% expect to use it within five years. AI has many applications for enterprise businesses, and in this article, we will discuss 4 ways that it can be used to improve the customer experience. The combination of AI and machine learning for gathering and analyzing social, historical and behavioral data enables brands to gain a much more accurate understanding of its customers.

The Apple Watch Series 6 is as beautiful as it is useful. With a wide range of features, an understated design, and no major weaknesses the Apple Watch Series 6 is the best smartwatch you can buy. It can track your fitness, provide insights into your health, handle phone calls and messaging, help you navigate or listen to music, and ensure you stay on top of your schedule, all controlled by elegant and intuitive software. The Apple Watch comes in two case sizes (40mm and 44mm) and in a wide array of finishes and bands. Whether you want an aluminum case with a sports band primarily to help you stay healthy, or a stainless steel case with a Milanese loop band to keep you connected during a busy workday, there's an Apple Watch for you. Setup is a breeze, and the Apple Watch Series 6 is the perfect partner for your iPhone. It can help you find your iPhone by triggering an alert sound, and even be used as a remote control for the iPhone camera. If you opt for an LTE model, you can leave the iPhone at home, though the watch will require its own data plan. The OLED touchscreen is bright, sharp, and surprisingly easy to swipe and tap your way around. The Digital Crown on the side can be rotated for fine control in menus and pressed to call Siri into service.