Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things--numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning is the process that powers many of the services we use today--recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa.

You can get the Echo Show 5 for under $50 right now, plus get cool extras. Purchases you make through our links may earn us a commission. Life gets so hectic sometimes it helps to have an assistant. With the Echo Show 5, that's essentially what you'll be getting: A remarkably resourceful buddy who's always down to help you with your errands and to-do list--all you have to do is ask. As one of Amazon's early Prime Day 2020 deals, Prime members can get this truly marvelous smart display at a major discount--and that's just where the fun begins.

Google is launching a few new privacy features today that include a refreshed Safety Center that's now live in the U.S. and coming soon globally, as well as more prominent alerts when the company expects that your account has been tampered with. The most interesting new feature, however, is a new Guest mode for the Google Assistant on Google-branded devices. Not to be confused with giving guests access to your Google Chromecast, for example, this new Guest mode is more akin to the incognito mode in your browser. With Guest mode on, which you invoke by saying "Hey Google, turn on guest mode," the Assistant won't offer personalized responses and your interactions won't be saved to your account. It'll stay on until you turn it off.

Automation Anywhere, Inc., a global leader in robotic process automation (RPA), announced AARI (Automation Anywhere Robotic Interface) – a smart digital assistant designed for a new era of work that brings consumer experiences to the enterprise. Available via Automation Anywhere's award-winning, cloud-native RPA platform, Enterprise A2019, AARI makes it easy for anyone to participate in the automation of day-to-day business tasks, through business-friendly user interfaces. Like the popular digital assistants Siri and Alexa that have become ubiquitous in our personal lives, AARI provides an easy-to-use, bot-to-human interface that oversees various business processes. AARI enables all users to further simplify everyday tasks, improve collaboration between teams, and provide best-in-class customer service – either on-premises or in the cloud. Now, every employee can participate in the automation economy from the device or application of their choice – from data lookups across multiple systems to complex escalation scenarios.

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.