scholarly journals Talk to me

2016 ◽  
Vol 138 (11) ◽  
pp. 32-37
Author(s):  
Alan S. Brown
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Operating System ◽  
Natural Language ◽  
Product Design ◽  
Voice Recognition ◽  
Radio Stations ◽  
Learning Software ◽  
Smart Cars ◽  
Smart Factories

This article explores the collaboration of artificial intelligence and voice recognition in day-to-day living. As everyday products grow smarter and more capable, voice promises to simplify how we communicate with smart cars, smart homes, smart offices, and smart factories. Instead of mastering one new app after another, voice could make it simpler to command them all. Incorporating voice interfaces is expected to transform product design. Voice recognition is also expanding its beachhead in physical products. Many new cars use voice to place calls, set the GPS, write and receive texts, change radio stations, and adjust the temperature. The machine learning software behind voice recognition analyzes data from actual interactions to improve its performance. It is expected that by coupling natural language requests to the deepest workings of the operating system, we may soon have new types of products that will give anyone access to features that only a professional could manipulate.

scholarly journals Depression Therapy Using Chatbot

2020 ◽  
Vol 6 (12) ◽  
pp. 323-327
Author(s):  
Arkodeep Biswas and Ajay Kaushik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Operating System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Text Chat ◽  
Communication History ◽  
Chat Bot

The objective of this paper is to build a Web Application based on Virtual voice and chat Assistant. The current study focuses on development of voice and text/chat bot specifically. It is specially being built for people who feel depressed and insists them to talk open mindedly which in turn pacifies them. As the name of the application suggests, App: An application to pacify people and make them as happy as a cat would be with his or her mother (the reason why a cat purrs). We will be using Dialog flow for the application design and Machine Learning as a part of Artificial Intelligence for Natural Language Processing (NLP), an easiest way to use Machine Learning libraries. At the back-end we will be using a database to store the communication history between the user and the bot. This application will only work on devices with Web operating system version-5.0 and above.

scholarly journals What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

2019 ◽  
Vol 33 ◽  
pp. 6674-6681 ◽  
Author(s):  
Irene Li ◽  
Alexander R. Fabbri ◽  
Robert R. Tung ◽  
Dragomir R. Radev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Courses ◽  
Specific Topic ◽  
Reading List ◽  
University Courses

Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of “what should one learn first,”we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available 1. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.

scholarly journals Tecnologías de Mantenimiento industrial en la industria 4.0

2019 ◽  
pp. 20-24
Author(s):  
Manuel Meraz-Méndez ◽  
Claudia Lerma-Hernández
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Teaching Practices ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Industrial Maintenance ◽  
Technological University ◽  
Engineering Career ◽  
Smart Factories ◽  
Technical Area

Industry 4.0 is the incorporation of digital technologies in factories such as: artificial intelligence, machine learning, 3D printing, drones, robotics, IOT, big data, virtual reality, automation, among others, which aim to digitalize processes productive in the factories, these are also called smart factories. The objective of this article is to identify the technologies applicable to industrial maintenance in Industry 4.0, the final result of this research determine the teaching practices that must be carried out in the Industrial Maintenance Engineering career at the Technological University of Chihuahua, and how the students must be prepared with the competences and skills necessary to face this challenge, at the same time the new teaching practices and strategies that a teacher in the technical area of Industrial Maintenance must apply in laboratory practices with a focus on Industry 4.0.

scholarly journals Supervised Learning Methods and Applications in Medical Research

2022 ◽  
pp. 27-34
Author(s):  
Yung Ming ◽  
Lily Yuan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Feature Extraction ◽  
Job Performance ◽  
Computational Linguistics ◽  
New Technologies ◽  
Voice Recognition ◽  
Autonomous Driving ◽  
Automated Systems

Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.

scholarly journals The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning

2018 ◽  
Vol 8 (5) ◽  
pp. 259
Author(s):  
Mohammed Ali
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Brain ◽  
Language Learning ◽  
Language Processing ◽  
Human Intelligence ◽  
Computer Assisted ◽  
Learning Programs ◽  
Face Detection And Recognition ◽  
Learning Software

In this study, the researcher has advocated the importance of human intelligence in language learning since software or any Learning Management System (LMS) cannot be programmed to understand the human context as well as all the linguistic structures contextually. This study examined the extent to which language learning is perilous to machine learning and its programs such as Artificial Intelligence (AI), Pattern Recognition, and Image Analysis used in much assistive learning techniques such as voice detection, face detection and recognition, personalized assistants, besides language learning programs. The researchers argue that language learning is closely associated with human intelligence, human neural networks and no computers or software can claim to replace or replicate those functions of human brain. This study thus posed a challenge to natural language processing (NLP) techniques that claimed having taught a computer how to understand the way humans learn, to understand text without any clue or calculation, to realize the ambiguity in human languages in terms of the juxtaposition between the context and the meaning, and also to automate the language learning process between computers and humans. The study cites evidence of deficiencies in such machine learning software and gadgets to prove that in spite of all technological advancements there remain areas of human brain and human intelligence where a computer or its software cannot enter. These deficiencies highlight the limitations of AI and super intelligence systems of machines to prove that human intelligence would always remain superior.

Artificial Intelligence and Investing

2011 ◽  
pp. 237-240 ◽  
Author(s):  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Asset Valuation ◽  
Stock Valuation ◽  
Knowledge Based ◽  
Processing Techniques

The techniques of artificial intelligence include knowledgebased, machine learning, and natural language processing techniques. The discipline of investing requires data identification, asset valuation, and risk management. Artificial intelligence techniques apply to many aspects of financial investing, and published work has shown an emphasis on the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. However, in the future, knowledge-based, machine learning, and natural language processing techniques will be integrated into systems that simultaneously address data identification, asset valuation, and risk management.

scholarly journals An Analysis of the Applications of Natural Language Processing in Various Sectors

2021 ◽  
Author(s):  
Priya B ◽  
Nandhini J.M ◽  
Gnanasekaran T
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Health Sector ◽  
Automated System ◽  
Human Language ◽  
Agriculture Sector ◽  
Prediction Of Diabetes

Natural Language processing (NLP) dealing with Artificial Intelligence concept is a subfield of Computer Science, enabling computers to understand and process human language. Natural Language Processing being a part of artificial intelligence provides understanding of human language by computers for the purpose of extracting information or insights and create meaningful response. It involves creating algorithms that transform text in to words labeling With the emerging advancements in Machine learning and Deep Learning, NLP can contributed a lot towards health sector, education, agriculture and so on. This paper summarizes the various aspects of NLP along with case studies associated with Health Sector for Voice Automated System, prediction of Diabetes Millets, Crop Detection technique in Agriculture Sector.

scholarly journals Ai and Ml Based Google Assistant for an Organization using Google Cloud Platform and Dialogflow

2020 ◽  
Vol 8 (5) ◽  
pp. 2722-2727
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language ◽  
Language Processing ◽  
User Interaction ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Cloud Platform ◽  
Organization Process ◽  
Academic Information

Many people adopting Smart Assistant Devices such as Google Home. Now a days of solely engaging with a service through a keyboard are over. The new modes of user interaction are aided in part by this research will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services. In particular, it will look at the development of google assistants as a channel for information distribution. This project is aimed to implement an android-based chatbot to assist with Organization basic processes, using google tools such as Dialogflow that uses Natural language processing NLP, Actions on Google and Google Cloud Platform that expose artificial intelligence and Machine Learning methods such as natural language understanding. Allowing users to interact with the google assistant using natural language as input and to train the chatbot i.e. google assistant using Dialogflow Machine learning tool and some appropriate methods so it will be able to generate a dynamic response. The chatbot will allow users to view all their personal academic information, schedule meetings with higher officials, automating the organization process and organization resources information all from within the chatbot i.e. Google Assistant. This project uses the OAuth authentication for security purpose. The Dialogflow helps to understand the users query by using machine learning algorithms. By using this google assistant we are going to use the Cloud Vision API for advancement. We will use Dialogflow as key part to develop Google assistant.

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