Artificial Intelligence and Virtual Assistant—Working Model

Arora, Shakti; Athavale, Vijay Anant; Maggu, Himanshu; Agarwal, Abhay Kumar

doi:10.1007/978-981-15-7130-5_12

Cited by 13 publications

(9 citation statements)

References 1 publication

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“…Virtual assistants use artificial intelligence (AI), machine learning, and natural language processing (NLP) to convert speech into text. NLP is based on deep learning (DL) and neural networks to recognize patterns, categorize, contextualize and translate them into text and perform a voice-based interaction [ 57 ]. DL application training and optimization rely strongly on stochastic procedures [ 58 ], so they can result in non-deterministic outcomes.…”

Section: Discussionmentioning

confidence: 99%

Brain–Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications

Velasco-Álvarez

Fernández-Rodríguez

Vizcaíno-Martín

et al. 2021

Sensors

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Brain–computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.

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Section: Discussionmentioning

confidence: 99%

Brain–Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications

Velasco-Álvarez

Fernández-Rodríguez

Vizcaíno-Martín

et al. 2021

Sensors

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“…Top2Vec [48], a novel text embedding technique, was recently employed in a task to retrieve precedents. This study measured similarity among the Top2Vec embeddings using BM25 and achieved better results than those obtained with BM25 only [49]. The latest study of this SLR is an extensive investigation of 56 different assemblies, crossing eight document text representation techniques and seven similarity measurement techniques [50].…”

Section: Rq1 (How Has the Task Of Automating The Identification Of Pr...mentioning

confidence: 96%

A Rapid Semi-automated Literature Review on Legal Precedents Retrieval

Mentzingen¹,

António²,

Bação³

2022

Progress in Artificial Intelligence

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Precedents constitute the starting point of judges' reasoning in national legal systems. Precedents are also an essential input for case-based reasoning (CBR) methodologies. Although considerable research has been done on CBR applied to legal practice, the precedent retrieval techniques are a relatively new and unexplored field of AI & Law. Only a few works have tested or developed methods for identifying such previous similar cases. This work uses text mining (TM), natural language processing (NLP), and data visualization methods to provide a semi-automated rapid literature review and identify how justice courts and legal practitioners may use AI to retrieve similar cases. Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), automation techniques were used to expedite the literature review. In this study, we confirmed the feasibility of automation tools for expediting literature reviews and provided an overview of the current research state on legal precedents retrieval.

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“…In the 9th article [21] , the authors are investigating the factors that influence users' continued use of smart voice assistants. The 10th article [22] discusses how NLP and other supporting technologies such as IoT and AI are used to enable virtual assistants to work as real-time assistants, providing technical and social assistance to users in various modes, such as secretarial work, customer service support, and web editing tasks. The authors also designed an intelligent virtual assistant that could be integrated with Google virtual services and work with the Google virtual assistant interface, using speech recognition, a knowledge base, and machine learning techniques to make the conversation between humans and software more natural.…”

Section: Sourcesmentioning

confidence: 99%

Inquiring Natural Language Processing Capabilities on Robotic Systems through Virtual Assistants: A Systemic Approach

Giachos

Papakitsos

Savvidis

et al. 2023

j. of Comput. sci. res.

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This paper attempts to approach the interface of a robot from the perspective of virtual assistants. Virtual assistants can also be characterized as the mind of a robot, since they manage communication and action with the rest of the world they exist in. Therefore, virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing (NLP) module for conducting communication in their human-robot interface. This work is focused on inquiring and enhancing the capabilities of this module. The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants. Therefore, any new attempt of developing such a capability has to start from scratch. Accordingly, to include corresponding capabilities to a developing NLP system of a virtual assistant, a method of systemic semantic modelling is proposed and applied. For this purpose, the paper briefly reviews the evolution of virtual assistants from the first assistant, in the form of a game, to the latest assistant that has significantly elevated their standards. Then there is a reference to the evolution of their services and their continued offerings, as well as future expectations. The paper presents their structure and the technologies used, according to the data provided by the development companies to the public, while an attempt is made to classify virtual assistants, based on their characteristics and capabilities. Consequently, a robotic NLP interface is being developed, based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants, being tested through a small natural language dictionary in Greek.

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Artificial Intelligence and Virtual Assistant—Working Model

Cited by 13 publications

References 1 publication

Brain–Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications

Brain–Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications

A Rapid Semi-automated Literature Review on Legal Precedents Retrieval

Inquiring Natural Language Processing Capabilities on Robotic Systems through Virtual Assistants: A Systemic Approach

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