scholarly journals Evolution of a Robust AI System: A Case Study of AAAI’s AI-Alert

AI Magazine ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 17-38
Author(s):  
Joshua Eckroth
Keyword(s):  
Artificial Intelligence ◽  
User Engagement ◽  
Daily Lives ◽  
New Approach ◽  
News Stories ◽  
Design Considerations ◽  
System A ◽  
Story Selection ◽  
The Web

Since mid-2018, we have used a suite of artificial intelligence (AI) technologies to automatically generate the Association for the Advancement of Artificial Intelligence’s AI-Alert, a weekly email sent to all Association for the Advancement of Artificial Intelligence members and thousands of other subscribers. This alert contains ten news stories from around the web that focus on some aspect of AI, such as new AI inventions, AI’s use in various industries, and AI’s impacts in our daily lives. This alert was curated by-hand for a decade before we developed AI technology for automation, which we call “NewsFinder.” Recently, we redesigned this automation and ran a six-month experiment on user engagement to ensure the new approach was successful. This article documents our design considerations and requirements, our implementation (which involves web crawling, document classification, and a genetic algorithm for story selection), and our reflections after a year and a half since deploying this technology.

scholarly journals Thematic Dynamics of Internet of Things (IoT): Impact on Digital Personalized Healthcare (PHC)

2021 ◽  
Author(s):  
Bongs Lainjo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Successful Implementation ◽  
Daily Lives ◽  
Personalized Healthcare ◽  
Iot Platform ◽  
Iot Platforms ◽  
Almost All

Abstract Background: Information technology has continued to shape contemporary thematic trends. Advances in communication have impacted almost all themes ranging from education, engineering, healthcare, and many other aspects of our daily lives. Method: This paper attempts to review the different dynamics of the thematic IoT platforms. A select number of themes are extensively analyzed with emphasis on data mining (DM), personalized healthcare (PHC), and thematic trends of a select number of subjectively identified IoT-related publications over three years. In this paper, the number of IoT-related-publications is used as a proxy representing the number of apps. DM remains the trailblazer, serving as a theme with crosscutting qualities that drive artificial intelligence (AI), machine learning (ML), and data transformation. A case study in PHC illustrates the importance, complexity, productivity optimization, and nuances contributing to a successful IoT platform. Among the initial 99 IoT themes, 18 are extensively analyzed using the number of IoT publications to demonstrate a combination of different thematic dynamics, including subtleties that influence escalating IoT publication themes. Results: Based on findings amongst the 99 themes, the annual median IoT-related publications for all the themes over the four years were increasingly 5510, 8930, 11700, and 14800 for 2016, 2017, 2018, and 2019 respectively; indicating an upbeat prognosis of IoT dynamics. Conclusion: The vulnerabilities that come with the successful implementation of IoT systems are highlighted including the successes currently achieved by institutions promoting the benefits of IoT-related systems like the case study. Security continues to be an issue of significant importance.

A New Approach to the Design of Combined Cycle Plants

1992 ◽  
Author(s):  
Ir. Ted Wiekmeijer
Keyword(s):  
Gas Turbine ◽  
Waste Heat ◽  
Combined Cycle ◽  
Waste Heat Boiler ◽  
New Developments ◽  
New Approach ◽  
System A ◽  
New Development ◽  
Asme Paper

The paper will deal with new developments on basis of the ideas, laid down in ASME paper 90-GT-180, presented at the Brussels Conference. In this former paper a combination of incinerators and cogen systems was described. New development show, that some of these ideas can also be used in cogen plants, in which all steam is raised and superheated in a waste heat boiler behind a high grade fuel fired gas turbine (natural gas or equivalent). This paper will deal give a description of the new system. A comparison will be made with conventional cogen systems, comprising of a gas turbine, a dual pressure non-fired waste heat boiler and a condensing steam turbine. On basis of a particular case study both the technical and financial performances will be compared with each other.

scholarly journals An Artificial Intelligence Chatbot for Young People’s Sexual and Reproductive Health in India (SnehAI): Instrumental Case Study

10.2196/29969 ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. e29969
Author(s):  
Hua Wang ◽  
Sneha Gupta ◽  
Arvind Singhal ◽  
Poonam Muttreja ◽  
Sanghamitra Singh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reproductive Health ◽  
Language Processing ◽  
Sexual And Reproductive Health ◽  
Processing System ◽  
User Engagement ◽  
Social Good ◽  
Instrumental Case Study ◽  
Instrumental Case

Background Leveraging artificial intelligence (AI)–driven apps for health education and promotion can help in the accomplishment of several United Nations sustainable development goals. SnehAI, developed by the Population Foundation of India, is the first Hinglish (Hindi + English) AI chatbot, deliberately designed for social and behavioral changes in India. It provides a private, nonjudgmental, and safe space to spur conversations about taboo topics (such as safe sex and family planning) and offers accurate, relatable, and trustworthy information and resources. Objective This study aims to use the Gibson theory of affordances to examine SnehAI and offer scholarly guidance on how AI chatbots can be used to educate adolescents and young adults, promote sexual and reproductive health, and advocate for the health entitlements of women and girls in India. Methods We adopted an instrumental case study approach that allowed us to explore SnehAI from the perspectives of technology design, program implementation, and user engagement. We also used a mix of qualitative insights and quantitative analytics data to triangulate our findings. Results SnehAI demonstrated strong evidence across fifteen functional affordances: accessibility, multimodality, nonlinearity, compellability, queriosity, editability, visibility, interactivity, customizability, trackability, scalability, glocalizability, inclusivity, connectivity, and actionability. SnehAI also effectively engaged its users, especially young men, with 8.2 million messages exchanged across a 5-month period. Almost half of the incoming user messages were texts of deeply personal questions and concerns about sexual and reproductive health, as well as allied topics. Overall, SnehAI successfully presented itself as a trusted friend and mentor; the curated content was both entertaining and educational, and the natural language processing system worked effectively to personalize the chatbot response and optimize user experience. Conclusions SnehAI represents an innovative, engaging, and educational intervention that enables vulnerable and hard-to-reach population groups to talk and learn about sensitive and important issues. SnehAI is a powerful testimonial of the vital potential that lies in AI technologies for social good.

scholarly journals An Artificial Intelligence Chatbot for Young People’s Sexual and Reproductive Health in India (SnehAI): Instrumental Case Study (Preprint)

2021 ◽  
Author(s):  
Hua Wang ◽  
Sneha Gupta ◽  
Arvind Singhal ◽  
Poonam Muttreja ◽  
Sanghamitra Singh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reproductive Health ◽  
Language Processing ◽  
Sexual And Reproductive Health ◽  
Processing System ◽  
User Engagement ◽  
Social Good ◽  
Instrumental Case Study ◽  
Instrumental Case

BACKGROUND Leveraging artificial intelligence (AI)–driven apps for health education and promotion can help in the accomplishment of several United Nations sustainable development goals. SnehAI, developed by the Population Foundation of India, is the first Hinglish (Hindi + English) AI chatbot, deliberately designed for social and behavioral changes in India. It provides a private, nonjudgmental, and safe space to spur conversations about taboo topics (such as safe sex and family planning) and offers accurate, relatable, and trustworthy information and resources. OBJECTIVE This study aims to use the Gibson theory of affordances to examine SnehAI and offer scholarly guidance on how AI chatbots can be used to educate adolescents and young adults, promote sexual and reproductive health, and advocate for the health entitlements of women and girls in India. METHODS We adopted an instrumental case study approach that allowed us to explore SnehAI from the perspectives of technology design, program implementation, and user engagement. We also used a mix of qualitative insights and quantitative analytics data to triangulate our findings. RESULTS SnehAI demonstrated strong evidence across fifteen functional affordances: accessibility, multimodality, nonlinearity, compellability, queriosity, editability, visibility, interactivity, customizability, trackability, scalability, glocalizability, inclusivity, connectivity, and actionability. SnehAI also effectively engaged its users, especially young men, with 8.2 million messages exchanged across a 5-month period. Almost half of the incoming user messages were texts of deeply personal questions and concerns about sexual and reproductive health, as well as allied topics. Overall, SnehAI successfully presented itself as a trusted friend and mentor; the curated content was both entertaining and educational, and the natural language processing system worked effectively to personalize the chatbot response and optimize user experience. CONCLUSIONS SnehAI represents an innovative, engaging, and educational intervention that enables vulnerable and hard-to-reach population groups to talk and learn about sensitive and important issues. SnehAI is a powerful testimonial of the vital potential that lies in AI technologies for social good. CLINICALTRIAL

scholarly journals Development of intelligent agents through collaborative innovation

2019 ◽  
Vol 11 (3) ◽  
pp. 29-37
Author(s):  
Mateusz Kot ◽  
Grzegorz Leszczyński
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Machine Learning Algorithms ◽  
User Engagement ◽  
Collaborative Development ◽  
New Approach ◽  
Collaborative Innovation ◽  
The Impact ◽  
Depth Interviews ◽  
Agent Development

Abstract This study focuses on the development of a specific type of Intelligent Agents — Business Virtual Assistants (BVA). The paper aims to identify the scope of collaboration between users and providers in the process of agent development and to define the impact that user interpretations of a BVA agent have on this collaboration. This study conceptualises the collaboration between providers and users in the process of the BVA development. It uses the concept of the collaborative development of innovation and sensemaking. The empirical part presents preliminary exploratory in-depth interviews conducted with CEOs of BVA providers and analyses the use of the scheme offered by Miles and Hubermann (1994). The main results show the scope of the collaboration between BVA users and providers in the process of the BVA development. User engagement is crucial in the development of BVA agents since they are using machine learning algorithms. The user interpretation through sensemaking influences the process as their attitudes guide their behaviour. Apart from that, users have to adjust to this new kind of entity in the market and learn how to use it in line with savoir-vivre rules. This paper suggests the need to develop a new approach to the collaborative development of innovation when Artificial Intelligence is involved.

A New Approach to Matrix Sandstone Acidizing Using a Single Step HF System: A Niger Delta Case Study

2006 ◽  
Author(s):  
Chike Valentine Uchendu ◽  
Linus Ayajuru Nwoke ◽  
Olatunji Akinlade ◽  
James Arukhe
Keyword(s):  
Niger Delta ◽  
Single Step ◽  
New Approach ◽  
System A

scholarly journals A Genetic Algorithm for Finding a Small and Diverse Set of Recent News Stories on a Given Subject: How We Generate AAAI’s AI-Alert

2019 ◽  
Vol 33 ◽  
pp. 9357-9364
Author(s):  
Joshua Eckroth ◽  
Eric Schoen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Supervised Learning ◽  
A Priori ◽  
News Stories ◽  
Scoring Algorithm ◽  
Representative Selection ◽  
Story Selection ◽  
Selection Of

This paper describes the genetic algorithm used to select news stories about artificial intelligence for AAAI’s weekly AIAlert, emailed to nearly 11,000 subscribers. Each week, about 1,500 news stories covering various aspects of artificial intelligence and machine learning are discovered by i2k Connect’s NewsFinder agent. Our challenge is to select just 10 stories from this collection that represent the important news about AI. Since stories and topics do not necessarily repeat in later weeks, we cannot use click tracking and supervised learning to predict which stories or topics are most preferred by readers. Instead, we must build a representative selection of stories a priori, using information about each story’s topics, content, publisher, date of publication, and other features. This paper describes a genetic algorithm that achieves this task. We demonstrate its effectiveness by comparing several engagement metrics from six months of “A/B testing” experiments that compare random story selection vs. a simple scoring algorithm vs. our new genetic algorithm.

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