Bach vs. Others This document presents a comparison between curated open-domain dialogue datasets available in the public domain and the data produced by AKA’s Bach data platform. The current report focuses on quantitative measurement which could be done in a transparent manner and represent objective differences found in the data. The analysis was performed using the following criteria: Total Number of Tokens Number of tokens is a measure of the overall size of the dataset. It is very important for training the modern Deep Learning-based models. Bach dataset displays clear superiority to others. Higher is better. Vocabulary Size Vocabulary size is the number of unique tokens appearing in the dataset. It represents the variety of speech in dialogues. Our dataset […]
AKA’s paper is accepted by International Conference HIMS (Health Informatics and Medical System) //americancse.org/events/csce2020/conferences/hims20 (July, 2020)
As human beings live longer, the number of people diagnosed with dementia is growing. Many studies have proved that dementia tends to degenerate cognitive abilities. Since dementia patients endure different types of symptoms, it is important to monitor dementia patients individually. Furthermore, old people are generally lack of understanding technology, which brings a low self-motivation to use technologies. To enhance the cognitive abilities of old people, we propose a mobile plat-form called ReSmart which embeds six distinct levels of the brain training task, based on five cognitive areas to detect different types of individual symptoms. Those brain training tasks are presented in a game-like format that aims to not lose the elder ‘s motivation for technology use and keeping interested. […]
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 […]
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!