scholarly journals Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts

2020 ◽  
Vol 34 (09) ◽  
pp. 13381-13388
Author(s):  
Phoebe Lin ◽  
Jessica Van Brummelen ◽  
Galit Lukin ◽  
Randi Williams ◽  
Cynthia Breazeal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Young Children ◽  
Mental Models ◽  
Small Groups ◽  
Smart Devices ◽  
The Novel ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Limited Knowledge

Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.

A smarthome conversational agent performing implicit demand-response application planning

2021 ◽  
pp. 1-19
Author(s):  
Anastasios Alexiadis ◽  
Angeliki Veliskaki ◽  
Alexandros Nizamis ◽  
Angelina D. Bintoudi ◽  
Lampros Zyglakis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demand Response ◽  
Smart Devices ◽  
Ai Planning ◽  
Conversational Agent ◽  
Intent Recognition ◽  
Complex Sentences ◽  
Energy Domain ◽  
Planning Methods ◽  
Personal Agents

In recent years, the growing use of Intelligent Personal Agents in different human activities and in various domains led the corresponding research to focus on the design and development of agents that are not limited to interaction with humans and execution of simple tasks. The latest research efforts have introduced Intelligent Personal Agents that utilize Natural Language Understanding (NLU) modules and Machine Learning (ML) techniques in order to have complex dialogues with humans, execute complex plans of actions and effectively control smart devices. To this aim, this article introduces the second generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA framework and utilizes two machine learning models for NLU and dialogue flow classification. CIPA-Generation B provides a dialogue-story generator that is based on the idea of adjacency pairs and multiple intents, that are classifying complex sentences consisting of two users’ intents into two automatic operations. More importantly, the agent can form a plan of actions for implicit Demand-Response and execute it, based on the user’s request and by utilizing AI Planning methods. The introduced CIPA-Generation B has been deployed and tested in a real-world scenario at Centre’s of Research & Technology Hellas (CERTH) nZEB SmartHome in two different domains, energy and health, for multiple intent recognition and dialogue handling. Furthermore, in the energy domain, a scenario that demonstrates how the agent solves an implicit Demand-Response problem has been applied and evaluated. An experimental study with 36 participants further illustrates the usefulness and acceptance of the developed conversational agent-based system.

Essential Features and Critical Issues With Educational Chatbots

2021 ◽  
pp. 246-262
Author(s):  
Jeremy Riel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Formal Learning ◽  
Automated Systems ◽  
Conversational Agents ◽  
Teacher Instruction ◽  
Practical Applications ◽  
Significant Potential ◽  
Critical Issues ◽  
Future Work

Conversational agents, also known as chatbots, are automated systems for engaging in two-way dialogue with human users. These systems have existed in one form or another for at least 60 years but have recently demonstrated significant potential with advances in machine learning and artificial intelligence technologies. The use of conversational agents or chatbots for education can potentially reduce costs and supplement teacher instruction in transformative ways for formal learning. This chapter examines the design and status of chatbots and conversational agents for educational purposes. Common design functions and goals of educational chatbots are described, along with current practical applications of chatbots for educational purposes. Finally, this chapter considers issues about pedagogical commitments, ethics, and equity to suggest future work in the field.

Machine Learning and Artificial Intelligence in Marketing Applications During COVID-19 Pandemic

2020 ◽  
pp. 21-31
Author(s):  
L. Kuladeep Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Real World ◽  
Current Situation ◽  
Advanced Technology ◽  
The Novel ◽  
Model Design ◽  
Economic Activities

Since the outbreak of the novel SARS-CoV-2, machine learning and artificial intelligence (ML/AI) have become the powerful marketing tools to mitigate economic activities during COVID-19 pandemic. The goal of ML/AI technology is to provide data and insights so that brands can understand what’s working and what’s not. This will help marketers understand and anticipate what sort of communications work and how to deliver them. Therefore, these are such promising methods employed by various marketing providers. AI uses machine learning to adapt and make changes which impact marketing in real time. The exact impact of events such as the COVID-19 pandemic is hard to predict, but AI will help us track and anticipate these circumstances, as well as provide us with the data needed to proceed. This chapter deals with recent studies that use such advanced technology to increase researchers from different perspectives, address problems and challenges by using such an algorithm to assist marketing experts in real-world issues. This chapter also discusses suggestions conveying researchers on ML/AI-based model design, marketing experts, and policymakers on few errors encountered in the current situation while tackling the current pandemic.

scholarly journals Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 354
Author(s):  
Octavian Sabin Tătaru ◽  
Mihai Dorin Vartolomei ◽  
Jens J. Rassweiler ◽  
Oșan Virgil ◽  
Giuseppe Lucarelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Pathology ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Data Sets ◽  
Human Beings ◽  
Proper Actions ◽  
Patient Will

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.

scholarly journals Performance Analysis and Study of Chatbots

2021 ◽  
pp. 345-348
Author(s):  
Prof. Ahlam Ansari ◽  
Fakhruddin Bootwala ◽  
Owais Madhia ◽  
Anas Lakdawala
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Performance Analysis ◽  
User Experience ◽  
Language Processing ◽  
Conversational Agents ◽  
Learning Machines ◽  
The Past

Artificial intelligence, machine learning and deep learning machines are being used as conversational agents. They are used to impersonate a human and provide the user a human-like experience. Conversational software agents that use natural language processing is called a chatbot and it is widely used for interacting with users. It provides appropriate and satisfactory answers to the user. In this paper we have analyzed and compared various chatbots and provided a score to each of them on different parameters. We have asked each chatbot the same questions, and we have evaluated each answer, whether it’s satisfactory or not. This analysis is based on user experience rather than analyzing the software of each chatbot. This paper proves that even though chatbot performance has highly increased compared to the past, there is still quite a lot of room for improvement.

scholarly journals Intersection of Artificial Intelligence, Machine Learning, and Internet of Things – An Economic Overview

2020 ◽  
Vol 9 (2) ◽  
pp. 119-128
Author(s):  
Mani Manavalan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Large Scale ◽  
Smart Devices ◽  
Time Data ◽  
Business World ◽  
Course Of Action ◽  
To Come ◽  
Set Up

Internet of Things (IoT) has become one of the mainstream advancements and a supreme domain of research for the technical as well as the scientific world, and financially appealing for the business world. It supports the interconnection of different gadgets and the connection of gadgets to people. IoT requires a distributed computing set up to deal with the rigorous data processing and training; and simultaneously, it requires artificial intelligence (AI) and machine learning (ML) to analyze the information stored on various cloud frameworks and make extremely quick and smart decisions w.r.t to data. Moreover, the continuous developments in these three areas of IT present a strong opportunity to collect real-time data about every activity of a business. Artificial Intelligence (AI) and Machine Learning are assuming a supportive part in applications and use cases offered by the Internet of Things, a shift evident in the behavior of enterprises trying to adopt this paradigm shift around the world. Small as well as large-scale organizations across the globe are leveraging these applications to develop the latest offers of services and products that will present a new set of business opportunities and direct new developments in the technical landscape. The following transformation will also present another opportunity for various industries to run their operations and connect with their users through the power of AI, ML, and IoT combined. Moreover, there is still huge scope for those who can convert raw information into valuable business insights, and the way ahead to do as such lies in viable data analytics. Organizations are presently looking further into the data streams to identify new and inventive approaches to elevate proficiency and effectiveness in the technical as well as business landscape. Organizations are taking on bigger, more exhaustive research approaches with the assistance of continuous progress being made in science and technology, especially in machine learning and artificial intelligence. If companies want to understand the valuable capacity of this innovation, they are required to integrate their IoT frameworks with persuasive AI and ML algorithms that allow ’smart devices/gadgets’ to imitate behavioral patterns of humans and be able to take wise decisions just like humans without much of an intervention. Integrating both artificial intelligence and machine learning with IoT networks is proving to be a challenging task for the accomplishment of the present IoT-based digital ecosystems. Hence, organizations should direct the necessary course of action to identify how they will drive value from intersecting AI, ML, and IoT to maintain a satisfactory position in the business in years to come. In this review, we will also discuss the progress of IoT so far and what role AI and ML can play in accomplishing new heights for businesses in the future. Later the paper will discuss the opportunities and challenges faced during the implementation of this hybrid model.

scholarly journals Risk prediction with office and ambulatory blood pressure using artificial intelligence

2020 ◽  
Author(s):  
Pedro Guimarães ◽  
Andreas Keller ◽  
Michael Böhm ◽  
Lucas Lauder ◽  
José L. Ayala ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Blood Pressure ◽  
Neural Networks ◽  
Ambulatory Blood Pressure ◽  
Deep Neural Networks ◽  
White Coat ◽  
Risk Scores ◽  
Support Vector ◽  
The Novel

AbstractBackgroundTo develop and validate a novel, machine learning-derived model for prediction of cardiovascular (CV) mortality risk using office (OBP) and ambulatory blood pressure (ABP), to compare its performance with existing risk scores, and to assess the possibility of predicting ABP phenotypes (i.e. white-coat, ambulatory and masked hypertension) utilizing clinical variables.MethodsUsing data from 63,910 patients enrolled in the Spanish ABP monitoring registry, machine-learning approaches (logistic regression, support vector machine, gradient boosted decision trees, and deep neural networks) and stepwise forward feature selection were used for the classification of the data.ResultsOver a median follow-up of 4.7 years, 3,808 deaths occurred from which 1,295 were from CV causes. The performance for all tested classifiers increased while adding up to 10 features and converged thereafter. For the prediction of CV mortality, deep neural networks yielded the highest clinical performance. The novel mortality prediction models using OBP (CV-MortalityOBP) and ABP (CV-MortalityABP) outperformed all other risk scores. The area under the curve (AUC) achieved by the novel approach, using OBP variables only, was already significantly higher when compared with the AUC of Framingham score (0.685 vs 0.659, p = 1.97×10−22), the SCORE (0.679 vs 0.613, p = 6.21×10−22), and ASCVD (0.722 vs 0.639, p = 8.03×10−30) risk score. However, prediction of CV mortality with ABP instead of OBP data led to a significant increase in AUC (0.781 vs 0.752, p = 1.73×10−42), accuracy, balanced accuracy and sensitivity. The sensitivity and specificity for detection of ambulatory, masked, and white-coat hypertension ranged between 0.653-0.661 and 0.573-0.651, respectively.ConclusionWe developed a novel risk calculator for CV death using artificial intelligence based on a large cohort of patients included in the Spanish ABP monitoring registry. The receiver operating characteristic curves for CV-MortalityOBP and CV-MortalityABP with deep neural networks models outperformed all other risk metrics. Prediction of CV mortality using ABP data led to a significant increase in performance metrics. The prediction of ambulatory phenotypes using clinical characteristics, including OBP, was limited.

scholarly journals Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

2020 ◽  
Vol 29 (157) ◽  
pp. 200181
Author(s):  
Danai Khemasuwan ◽  
Jeffrey S. Sorensen ◽  
Henri G. Colt
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Medical Imaging ◽  
Healthcare Delivery ◽  
Predictive Modelling ◽  
Pulmonary Medicine ◽  
The Novel ◽  
Digital Revolution ◽  
Massive Growth

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

scholarly journals Encoding Equality: The Case for Greater Regulation of Artificial Intelligence and Automated Decision Making in New Zealand

2020 ◽  
Vol 51 (1) ◽  
pp. 1
Author(s):  
Ella Brownlie
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Current Approach ◽  
Learning Processes ◽  
The Novel ◽  
General Data Protection Regulation ◽  
Urgent Case ◽  
General Data ◽  
Automated Decision Making

Automated decision-making systems, developed using artificial intelligence and machine learning processes, are being used by companies, organisations and governments with increasing frequency. The purpose of this article is to outline the urgent case for regulating automated decision-making and examine the possible options for regulation. This article will argue that New Zealand's current approach to regulating decision-making is inadequate. It will then analyse art 22 of the European Union's General Data Protection Regulation, concluding that this regime also has significant flaws. Finally, this article will propose an alternative regulatory solution to address the novel challenge posed by automated decision-making. This solution aims to strike a balance between the interests of organisations in capitalising on the benefits of automated decision-making technology and the interests of individuals in ensuring that their right to freedom from discrimination is upheld.

The Evolution and Future of Retailing and Retailing Education

2018 ◽  
Vol 40 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Dhruv Grewal ◽  
Scott Motyka ◽  
Michael Levy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Phones ◽  
Smart Devices ◽  
The Internet ◽  
Classroom Discussions ◽  
The Past ◽  
Blockchain Technology ◽  
The Internet Of Things ◽  
And Robotics

The pace of retail evolution has increased dramatically, with the spread of the Internet and as consumers have become more empowered by mobile phones and smart devices. This article outlines significant retail innovations that reveal how retailers and retailing have evolved in the past several decades. In the same spirit, the authors discuss how the topics covered in retail education have shifted. This article further details the roles of current technologies, including social media and retailing analytics, and emerging areas, such as the Internet of things, machine learning, artificial intelligence, blockchain technology, and robotics, all of which are likely to change the retail landscape in the future. Educators thus should incorporate these technologies into their classroom discussions through various means, from experiential exercises to interactive discussions to the reviews of recent articles.

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