Sonos devices have supported Amazon's Alexa voice assistant for almost five years now. The Sonos One from 2017 was the first speaker the company made with built-in microphones, and almost every speaker it's made since has worked with Alexa, not to mention Google Assistant. Despite supporting those popular services, though, Sonos has decided to build its own voice assistant. Dubbed Sonos Voice Control, the feature is specifically designed to work with music only, so this isn't exactly a competitor to Alexa and Google Assistant. Instead, it's meant to control your music as quickly as possible, and with privacy in mind.
Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
Amazon and Disney has just announced a new voice assistant called "Hey, Disney!". Built on Amazon's Alexa technology, this new Disney assistant will be available in your home Echo as well as in Echo devices located in Walt Disney World Resort hotel rooms. You can use it to interact with characters from Disney, Pixar, Marvel, Star Wars and more. It is the first time an Alexa custom assistant will be available on Echo devices. According to Amazon, this voice assistant will give you access to interactive storytelling experiences and entertainment featuring Disney characters. You can also play games and access jokes set in the Disney world.
Zhang, Daniel, Mishra, Saurabh, Brynjolfsson, Erik, Etchemendy, John, Ganguli, Deep, Grosz, Barbara, Lyons, Terah, Manyika, James, Niebles, Juan Carlos, Sellitto, Michael, Shoham, Yoav, Clark, Jack, Perrault, Raymond
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
Khatri, Chandra, Hedayatnia, Behnam, Venkatesh, Anu, Nunn, Jeff, Pan, Yi, Liu, Qing, Song, Han, Gottardi, Anna, Kwatra, Sanjeev, Pancholi, Sanju, Cheng, Ming, Chen, Qinglang, Stubel, Lauren, Gopalakrishnan, Karthik, Bland, Kate, Gabriel, Raefer, Mandal, Arindam, Hakkani-Tur, Dilek, Hwang, Gene, Michel, Nate, King, Eric, Prasad, Rohit
Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses. The 2018 competition also included the provision of a suite of tools and models to the competitors including the CoBot (conversational bot) toolkit, topic and dialog act detection models, conversation evaluators, and a sensitive content detection model so that the competing teams could focus on building knowledge-rich, coherent and engaging multi-turn dialog systems. This paper outlines the advances developed by the university teams as well as the Alexa Prize team to achieve the common goal of advancing the science of Conversational AI. We address several key open-ended problems such as conversational speech recognition, open domain natural language understanding, commonsense reasoning, statistical dialog management and dialog evaluation. These collaborative efforts have driven improved experiences by Alexa users to an average rating of 3.61, median duration of 2 mins 18 seconds, and average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch of the 2018 competition. For conversational speech recognition, we have improved our relative Word Error Rate by 55% and our relative Entity Error Rate by 34% since the launch of the Alexa Prize. Socialbots improved in quality significantly more rapidly in 2018, in part due to the release of the CoBot toolkit, with new entrants attaining an average rating of 3.35 just 1 week into the semifinals, compared to 9 weeks in the 2017 competition.
Cutting cable TV is certainly a forward-thinking move, but controlling your streaming TV device by voice is even more futuristic. Using voice commands can be a lot faster than fiddling with a remote, and with smart speakers such as the Amazon Echo and Google Home, you don't even need to use your hands. Still, not all streaming devices are on the same level when it comes to voice control. In the interest of sorting it all out, here's a rundown of everything you can and can't do, sorted by streaming platform: Not every Roku player has voice controls built into its remote, but for those that do, you can hit the microphone button to control playback, launch apps, search for things to watch, and launch music from Pandora, iHeartRadio, or TuneIn. Roku's biggest limitation is its inability to launch specific TV shows, movies, or live channels with voice alone.
Recent breakthroughs that have brought us modern-day voice assistants tend to have a common element, the cloud. Deep learning neural networks have proven to be important developments for the speed and robustness of natural language processing (NLP) solutions, but they require a lot of processing power. That typically means voice assistants in smart speakers, on smartphones and in other devices require high speed internet access to function. However, there are many use cases where internet connectivity may be unreliable or unavailable for a period of time. Automobiles are one such situation where offline voice assistant performance is critical.
Conversational AI is a subfield of artificial intelligence focused on producing natural and seamless conversations between humans and computers. We've seen several amazing advances on this front in recent years, with significant improvements in automatic speech recognition (ASR), text to speech (TTS), and intent recognition, as well as the rocketship growth of voice assistant devices like the Amazon Echo and Google Home, with estimates of close to 100 million devices in homes in 2018. But we're still a long way away from the fluent human-machine conversation promised in science fiction. Here are some key advances we should see over the next decade that could get us closer to that long-term vision. Machine learning, and in particular deep learning, has become an extremely popular technique within the field of AI over the past few years.