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AI Agents: Unveiling the Next Frontier of Business Transformation
Acknowledging the potential of AI agents, a survey conducted by Khmel indicates that 82% of businesses plan to deploy these systems into their organizations in the next three years (2024). By the end of 2025, there will be approximately 50 to 100 billion AI agents integrated into various types of businesses, providing human workers with the “necessary tools, data, and capabilities” to increase efficiency and bring about a new form of human-machine collaboration, generating immense values in all business sectors.
The FPT AI Agents platform enables effortless development and operations of multi-lingual AI agents, utilizing powerful generative AI technologies to cultivate the future of human-AI collaboration. Businesses can easily manage FPT AI Agents using natural language without the need for technical expertise. Currently, FPT AI Agents can process many languages, such as Vietnamese, English, Indonesian, and Japanese.
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Do you need a workthrough of our platform? Let us know
Contact center remains a “priority investment” for businesses to quickly and effectively serve customers. Artificial Intelligence (AI) has proven itself to be the key solution to sophisticated business problems, in which 79% of call centers expect to invest in this technology (Deloitte, 2021). Prominently, Conversational AI solution will help automate two-way interaction with customers, allowing … Continued
Content Authors: Tran Duc Dung; Delowar Hossain; Shin-ichiro Kaneko; Genci Kapi Abstract: Robot localization is an important task for mobile robot navigation. There are many methods focused on this issue. Some methods are implemented in indoor and outdoor environments. However, robot localization in textureless environments is still a challenging task. This is because in these environments, the scene … Continued
Content Authors: Tran Duc Dung; Genci Capi Comments: Accepted at the Conference PACLIC 2020 Abstract: Mapping is a crucial task for robot navigation. Especially, in order to develop a fully autonomous robot that can interact well with human, the map is not only required to contain geometry but also the semantic contents. Building a detailed map of the … Continued
Content Authors: Delowar Hossain, Sivapong Nilwong, Duc Dung Tran, Genci Capi Abstract: Many objects in household and industrial environments are commonly found partially occluded. In this paper, we address the problem of recognizing objects for use in partially occluded object recognition. To enable the use of more expensive features and classifiers, a region proposal network (RPN) which … Continued
Content Authors: Luong Chi Tho, Tran Thi Oanh Abstract: This paper presents the task of deeply analyzing user requests: the situation in ordering bots where users input an utterance, the bots would hopefully extract its full product descriptions and then parse them to recognize each product information (PI). This information is useful to help bots better understand … Continued
ContentAuthors: Luong Chi Tho, Tran Thi Oanh Abstract: This paper1 presents a study on understanding what the users say in chatbot systems: the situation where users input utterances bots would hopefully detect intents and recognize corresponding contexts implied by utterances. This helps bots better understand what users are saying, and act upon a much wider … Continued
ContentAuthors: Le Hong Phuong, Dang Hoang Vu Abstract: This paper presents a joint syntactic-semantic embedding model which not only uses syntactic information to enrich the word embeddings but also generates distributed representations for the syntactic structures themselves. The syntactic input to our model comes from a Lexicalized Tree-Adjoining Grammar parser. The word embeddings from our … Continued
Content Authors: Tran The Trung, Nguyen Minh Hai, Ha Minh Hoang, Hoang Thai Dinh, Eryk Dutkiewicz, Diep N. Nguyen Abstract: The minimum dominating set problem (MDSP) aims to construct the minimum-size subset D⊂VD⊂V of a graph G=(V,E)G=(V,E) such that every vertex has at least one neighbor in D. The problem is proved to be NP-hard. In a recent industrial application, we encountered a more general variant … Continued
Content Authors: Oanh Tran, Tu Pham, Vu Dang, Bang Nguyen Abstract: This paper introduces a large-scale human-labeled dataset for the Vietnamese POS tagging task on conversational texts. To this end, wepropose a new tagging scheme (with 36 POS tags) consisting of exclusive tags for special phenomena of conversational words, developthe annotation guideline and manually annotate 16.310K sentences … Continued
Content Authors: Dang Hoang Vu, Van Huy Nguyen, Phuong Le-Hong Abstract: Hybrid models of speech recognition combine a neural acoustic model with a language model, which rescores the output of the acoustic model to find the most linguistically likely transcript. Consequently the language model is of key importance in both open and domain specific speech recognition and … Continued
Content Authors: Luong Chi Tho, Le Hong Phuong Abstract: This work investigates the task-oriented dialogue problem in mixed-domain settings. We study the effect of alternating between different domains in sequences of dialogue turns using two related state-of-the-art dialogue systems. We first show that a specialized state tracking component in multiple domains plays an important role and gives … Continued
Content Authors: The-Tuyen Nguyen; Xuan-Luong Vu; Phuong Le-Hong Abstract: In this paper, we report our work on building linguistic resources for Vietnamese social network text analysis in multiple domains. We first describe our annotation methodology including guidelines development, annotation softwares and quality assurance. We then present results of the first pilot phase of the project. Finally, we outline some … Continued
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