AKA’s Paper (ReSmart) is accepted by HIMS 2020
AKA’s paper is accepted by International Conference HIMS (Health Informatics and Medical System) //americancse.org/events/csce2020/conferences/hims20 (July, 2020)
Muse ReSmart
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. […]
Performance Evaluation of Bach’s Retrieval & Scoring System
Overview Most current applications of automated dialogue systems involve narrowly focused language understanding and simple models of dialogue interaction. Understanding language and generating natural dialogue are important in building friendly interfaces for dialogue system, but it is particularly critical in settings where the speaker is focused on 1D situation. Real human conversation is highly context-dependent, and human speakers jointly build contributions to the shared context. That is, human dialogue has a very complex structure by itself, and exhibits a complex network of relations between other dialogues. AKA has continuously tried to build friendly dialogue interfaces, and understand situation- and context-dependent interpretation of speaker utterances, including multiple situations. Bach, multiple linked dialogue data platform for AKA’s dialogue system, is our solution […]
Abusive Language Detection for Muse Engine
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 […]
Musio’s High-Level Talks
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 […]
Bach: data architecture for multi-linked dialogues
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 […]
Evaluation for Muse Dialogue Engine 2019
Introduction Recent advances in AI has contributed to the rebirth of a chatbot-type dialogue system being able to interact with people through natural language communication. This could help people better understand the world around them and communicate more effectively with others, effectively bridging communication gaps. Therefore, it is important to understand the quality attributes associated developing and implementing high-quality conversational agents and diaglouge system. Muse is a NLP engine developed by AKA Intelligence, with a focus on natural conversation. Engineers at AKA and Softbank are collaborating to bring the Muse engine into Pepper, Softbank’s humannoid robot, to use Muse as Pepper’s English conversation system. Muse is also expanding into other hardware platforms as well. A typical example is Musio, a […]