scholarly journals Psychological Artificial Intelligence Service, Tess: Delivering On-demand Support to Patients and Their Caregivers: Technical Report

Cureus ◽  
2019 ◽  
Author(s):  
Angela Joerin ◽  
Michiel Rauws ◽  
Mary Lou Ackerman
Keyword(s):  
Artificial Intelligence ◽  
On Demand ◽  
Technical Report ◽  
Intelligence Service

The platform as factory: Crowdwork and the hidden labour behind artificial intelligence

2020 ◽  
Vol 44 (2) ◽  
pp. 145-158 ◽  
Author(s):  
Moritz Altenried
Keyword(s):  
Artificial Intelligence ◽  
Automated Measurement ◽  
On Demand ◽  
Wage Labour ◽  
Production Development ◽  
Crucial Component ◽  
The World ◽  
Domestic Labour ◽  
Private Homes ◽  
And Control

This article analyses crowdwork platforms where various forms of digital labour are outsourced to digital workers across the globe. The labour of these workers is, among other things, a crucial component in the production, development and support of artificial intelligence. Crowdwork platforms are an extreme example of new forms of automated measurement, management and control of labour allowing, in turn, for the creation of hyperflexible and highly scalable workforces. Particularly on so-called microtask platforms, work is characterised by decomposition, standardisation, automated management and surveillance, as well as algorithmically organised cooperation between a great number of workers. Analysing these platforms as a paradigmatic example of an emerging digital Taylorism, the article goes on to argue that this allows the platforms to assemble a deeply heterogeneous set of workers while bypassing the need to spatially and subjectively homogenise them. These platforms create a global on-demand workforce, working in their private homes or Internet cafes. As a result, crowdwork taps into labour pools hitherto almost inaccessible to wage labour. The second part of the article investigates this tendency by looking at two sets of workers: women shouldering care responsibilities, who now can work on crowdwork platforms while performing domestic labour, as well as digital workers in the Global South. While there are clear specifics of digital crowdwork, it is also an expression of broader transformations within the world of work, concerning, for example, new forms of algorithmic management just as the return of very old forms of exploitation such as the piece wage.

scholarly journals An artificial intelligence based crowdsensing solution for on-demand accident scene monitoring

2020 ◽  
Vol 170 ◽  
pp. 303-310
Author(s):  
May El Barachi ◽  
Faouzi Kamoun ◽  
Jannatul Ferdaos ◽  
Mouna Makni ◽  
Imed Amri
Keyword(s):  
Artificial Intelligence ◽  
On Demand ◽  
Accident Scene

scholarly journals Reports of the AAAI 2012 Spring Symposia

AI Magazine ◽  
2012 ◽  
Vol 33 (3) ◽  
pp. 109
Author(s):  
Harith Alani ◽  
Bo An ◽  
Manish Jain ◽  
Takashi Kido ◽  
George Konidaris ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Game Theory ◽  
Computer Science ◽  
Social Computing ◽  
Collective Intelligence ◽  
Intelligent Robots ◽  
Personal Wellness ◽  
Technical Report Series ◽  
Symposium Series ◽  
Technical Report

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, was pleased to present the 2012 Spring Symposium Series, held Monday through Wednesday, March 26–28, 2012 at Stanford University, Stanford, California USA. The six symposia held were AI, The Fundamental Social Aggregation Challenge (cochaired by W. F. Lawless, Don Sofge, Mark Klein, and Laurent Chaudron); Designing Intelligent Robots (cochaired by George Konidaris, Byron Boots, Stephen Hart, Todd Hester, Sarah Osentoski, and David Wingate); Game Theory for Security, Sustainability, and Health (cochaired by Bo An and Manish Jain); Intelligent Web Services Meet Social Computing (cochaired by Tomas Vitvar, Harith Alani, and David Martin); Self-Tracking and Collective Intelligence for Personal Wellness (cochaired by Takashi Kido and Keiki Takadama); and Wisdom of the Crowd (cochaired by Caroline Pantofaru, Sonia Chernova, and Alex Sorokin). The papers of the six symposia were published in the AAAI technical report series.

scholarly journals Ai Healthcare Interactive Talking Agent using Nlp

2019 ◽  
Vol 9 (1) ◽  
pp. 3470-3473
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Weight Maintenance ◽  
Computer Application ◽  
Active Lifestyle ◽  
Language Understanding ◽  
Self Help ◽  
Physical Exercises ◽  
Good Nutrition ◽  
Intelligence Service

Good nutrition plays an important part in leading an active lifestyle. Combined with physical exercises, the diet can benefit people to maintain their weight, reduce the possibility of diseases and improve overall health. A self-help motivational tool for weight maintenance is a good option. This paper presents an interactive talking agent that is a chatbot. A chatbot is a piece of software that operates a conversation using textual methods. Chatbot will start communication with the user and help to solve the concern by initiating a human way conversation using Language Understanding Intelligence Service (LUIS) concept. Natural language processing (NLP) is the capability of a computer application for understanding human dialect. It is one of the part of Artificial Intelligence (AI). Each language has a different morphology the chatbot has to be able to separate words into individual morphemes. Morphology is one of the tasks that NLP should be able to handle.

scholarly journals Artificial intelligence: future impacts, challenges and recommendations on healthcare services

2020 ◽  
Vol 7 (4) ◽  
pp. 1596
Author(s):  
Deepkumar Patel ◽  
Shruti Ashok Kore
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Professional ◽  
Healthcare Providers ◽  
Relevant Information ◽  
Healthcare Services ◽  
On Demand ◽  
Healthcare It ◽  
Healthcare Executives ◽  
Legal Challenges ◽  
Clinical Confidence

In this report, we review the market impact of artificial intelligence (AI) in healthcare and future predictions. AI is a rapidly advancing technology in healthcare. It provides rich and relevant information to patients and healthcare providers with on-demand medical and clinical confidence, AI can greatly advance healthcare professional and patient communications. Interest and investment in artificial intelligence continues to grow. At the same time there exists some practical challenges that will determine the course of this market trend. We will discuss the macroeconomic, ethical and legal challenges that pertain to this industry and make recommendations to the healthcare executives.

Integration of Feature Based Design and Feature Recognition

1995 ◽  
Author(s):  
JungHyun Han ◽  
Aristides A. G. Requicha
Keyword(s):  
Artificial Intelligence ◽  
Process Planning ◽  
Feature Recognition ◽  
Uncertain Reasoning ◽  
Machining Features ◽  
Artificial Intelligence Techniques ◽  
On Demand ◽  
Machined Parts ◽  
Feature Based ◽  
Feature Based Design

Abstract Process planning for machined parts typically requires that a part be described through machining features such as holes, slots and pockets. This paper presents a novel feature finder, which automatically generates a part interpretation in terms of machining features, by utilizing information from a variety of sources such as nominal geometry, tolerances and attributes, and design features. The feature finder strives to produce a desirable interpretation of the part as quickly as possible. If this interpretation is judged unacceptable by a process planner, alternatives can be generated on demand. The feature finder uses a hint-based approach, and combines artificial intelligence techniques, such as blackboard architecture and uncertain reasoning, with the geometric completion procedures first introduced in the OOFF system previously developed at USC.

Quality evaluation model of artificial intelligence service for startups

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chang Hwa Baek ◽  
Seong-Young Kim ◽  
Sung Uk Lim ◽  
Jie Xiong
Keyword(s):  
Artificial Intelligence ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Evaluation Research ◽  
Great Influence ◽  
Service Selection ◽  
Content Type ◽  
New Era ◽  
Quality Evaluation Model ◽  
Intelligence Service

PurposeThis paper aims to develop a quality evaluation model for artificial intelligence (AI)-based products/services that is applicable to startups utilizing AI technology. Although AI-based service has risen dramatically and replaced many service offerings, in reality, startups are rarely to develop and evaluate AI services. The features of AI service are fundamentally different from the properties of existing services and have a great influence on the customer's service selection.Design/methodology/approachThis paper reviews startups' development process, existing quality evaluation models and characteristics of services utilizing AI technology, and develops a quality evaluation model for AI-based services. A detailed analysis of a survey (application of the model) on customer satisfaction for AI speakers is provided.FindingsThis paper provides seven key features and 24 evaluation items for evaluating AI-based services.Originality/valueThis paper contributes to the growing need for methodologies that reflect the new era of AI-based products/services in quality evaluation research.

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