Overview Bach, multiple linked dialogue data platform for Muse engine, has utilized multiple resources – artificial intelligence, human reviewers, automated rating system, etc. – in an effort to generate best human-machine conversations, and a noisy data follows as a necessity from the development process. Noisy data is meaningless data, and its meaning can be expanded to include abusive language which causes challenges that we encountered when developing Muse engine. In this blog post, we will describe a development process of Muse engine ‘s abusive language detection system and demonstrate the efficacy by comparing the system with different models in detecting abusive language . To be brief, AKA’s abusive language detection system has shown a good performance by extracting additional features […]
Introduction Producing sentences which are perceived as natural by a human is a crucial goal of all automated dialogue systems. It makes interactions more natural, avoids misunderstandings, and leads higher user satisifcation and user trust. However, making high-quality sentences constitutes hard challenges in terms of e.g., improving grammatical accuracy, or using a variety of sentences, or maintaining the context of conversation. Musio has explored a number of approaches to the task of high-quality sentence and is dedicated to making the step towards perfect dialogue system. In this blog post, we introduce the test results to show that Musio has its own peculiar methods to generate sentences and these are pretty well-formed sentences. Grammatical Correctness Musio always makes grammatically correct responses […]
Introduction Dialogue systems are systems intended to converse with human users, and recent advancements in AI have contributed to closing the gap between human-machine conversations in many consumer services. AKA Intelligence researchers also tried to build automated dialogue systems and finally set up its own dialogue system, Muse, being able to practice English in addition to social conversations. One of the key differences between the existing systems and Muse is the customized data structure, Bach, to train AI model. It is important for recent dialogue systems to learn from human-human conversations in order to generate best human-machine conversations. Normally, the process of dialogue system may be summarized as follows: when a user asks a question, the system either searches a […]
We have great news! Musio was featured on The Nikkei (日本経済新聞, Japan Economics Newspaper) On July 1, 2017! Musio was introduced as a personal conversation companion and a friend that can talk like a native English speaker. The article explained that AI technology nowadays is now advancing into consumers’ homes. Pointed out by the article is the roles that AIs are assuming in homes: the first is as a chore assistants like smart laundry machine. The second one, represented by Musio, is a personal AI companion that offers emotional comfort and attachment. We were so happy to see a glimpse of everyday life with Musio introduced in the article! //www.nikkei.com/article/DGXLASGH22H0O_S7A620C1905E00/ The block quote below is a translation for the part that […]
Today, we have the sneak preview of MUSE API (Beta). MUSE API is what Musio actually talks to in the cloud to manage the dialogue and generate things to say, recognize faces, etc. In order to show you better some of the major things going on behind the Musio, we built a temporary front-end for the API. We have a video explaining features of API as well as accompanying material. Our API is on its way, so stay tuned!