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

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 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.

scholarly journals Intonation Characteristics of Singing Based on Artificial Intelligence Technology and Its Application in Song-on-Demand Scoring System

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
E. Wei
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Scoring System ◽  
Ant Colony Algorithm ◽  
Scoring Function ◽  
On Demand ◽  
Hit Rate ◽  
Artificial Intelligence Technology ◽  
Mvc Mode ◽  
Retrieval Engine

With the continuous progress of my country’s cultural industry, how to apply artificial intelligence technology to song on demand has become an issue of concern. This research mainly discusses the research of singing intonation characteristics based on artificial intelligence technology and its application in song-on-demand scoring system. This paper uses the combination of ant colony algorithm and DTW algorithm to measure the similarity between speech signals with the average distortion distance, so as to expect accurate recognition results. The design of the song-on-demand scoring function module uses a combination of MVC mode and command mode based on artificial intelligence technology. The view component in the MVC mode is mainly used to display the content that the user needs to sing and realize the interaction with the user. The singer selects a song to start playing, and the scoring terminal device queries the music library server for song information according to the song number, then starts playing the song through the FTP file sharing service according to the audio file path in the song information, and at the same time displays the song on the display according to the timeline Show song and pitch information. The singer sings according to the screen prompts. The microphone collects the voice signal and transmits it to the scoring terminal. After the scoring algorithm is calculated, the result is fed back to the screen in real time. The singer can view his singing status in real time and make corresponding adjustments to obtain a higher score. After the singing, the scoring terminal will display the final result on the screen to inform the user and upload the singing record to the server for recording. In the tested on-demand retrieval engine, the average hit rate of the top 3 has reached more than 90% under various humming methods, basically maintaining the high hit rate characteristics of the original retrieval engine. The system designed in this research helps to effectively improve the singing level.

scholarly journals Sparsity Increases Uncertainty Estimation in Deep Ensemble

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 54
Author(s):  
Uyanga Dorjsembe ◽  
Ju Hong Lee ◽  
Bumghi Choi ◽  
Jae Won Song
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Energy Efficient ◽  
Deep Neural Networks ◽  
Black Box ◽  
Uncertainty Estimation ◽  
Point Estimation ◽  
On Demand

Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members’ disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement im-plies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.

scholarly journals Detection and Prevention of Manet using Hybrid SVM with Ann

2019 ◽  
Vol 9 (2) ◽  
pp. 4463-4469
Keyword(s):  
Artificial Intelligence ◽  
Ad Hoc ◽  
Optimization Approach ◽  
On Demand ◽  
Power Resources ◽  
Adjacent Position ◽  
Routing Mechanism ◽  
Demand Routing ◽  
Hoc Networks ◽  
Artificial Neural Network Ann

Mobile Ad hoc Networks (MANET) have been exceptionally vulnerable against attacks because of the dynamic and self-configurable nature of its system foundation. This kind of wireless network is appropriate for temporary communication linked due to its nature of less-foundation and there is no any control of centralized manner. Design a routing mechanism that are security aware with higher QoS parameter is very competetive and the major tasks involved in ad hoc types of network as per the limited power resources and their dynamic routing topology. This paper mainly focused on the design of a secure and trusts based on-demand routing mechanism using Ad-hoc on demand distance vector (AODV) protocol to compute trust-based produces path initialed from source up to destination that will fulfill minimum two end-to-end QoS parameters of network. So here, the generalized AODV routing protocol has been extended from traditional routing mechanism to analyze the performance of this model with combination of artificial intelligence concept. The proposed ad hoc based routing mechanism is used to found possible routes that are prevented through trust adjacent position of security validation protocols and enhanced link optimized route computes on the basis of Artificial Neural Network (ANN) as an artificial intelligence algorithm for well-organized communication in MANET. In addition, this research demonstrates the effectiveness of bio inspired Firefly Algorithm (FFA) as an optimization approach with the consideration of several performance QoS metrics of network. The results have been measured in terms of throughput and PDR with SVM and ANN approach. It has been observed that the throughput and PDR measured using ANN approach is better compared to SVM approach an average of 0.755 PDR value has been obtained using ANN approach.

scholarly journals Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110160
Author(s):  
Gemma Newlands
Keyword(s):  
Artificial Intelligence ◽  
Critical Analysis ◽  
Data Preparation ◽  
On Demand ◽  
Paradoxical Situation ◽  
Human Labour ◽  
Unpaid Labour ◽  
Human Effort ◽  
Do So

Artificial Intelligence-as-a-Service (AIaaS) empowers individuals and organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms. However, institutional separation between development, training and deployment can lead to critical opacities, such as obscuring the level of human effort necessary to produce and train AI services. Information about how, where, and for whom AI services have been produced are valuable secrets, which vendors strategically disclose to clients depending on commercial interests. This article provides a critical analysis of how AIaaS vendors manipulate the visibility of human labour in AI production based on whether the vendor relies on paid or unpaid labour to fill interstitial gaps. Where vendors are able to occlude human labour in the organisational ‘backstage,’ such as in data preparation, validation or impersonation, they do so regularly, further contributing to ongoing techno-utopian narratives of AI hype. Yet, when vendors must co-produce the AI service with the client, such as through localised AI training, they must ‘lift the curtain’, resulting in a paradoxical situation of needing to both perpetuate dominant AI hype narratives while emphasising AI’s mundane limitations.

scholarly journals Intelligent on-demand design of phononic metamaterials

Nanophotonics ◽  
2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Yabin Jin ◽  
Liangshu He ◽  
Zhihui Wen ◽  
Bohayra Mortazavi ◽  
Hongwei Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Structural Design ◽  
Dimensional Space ◽  
Computational Effort ◽  
Machine Learning Algorithms ◽  
Atomic Scale ◽  
Acoustic Metamaterials ◽  
On Demand ◽  
Accurate Design

Abstract With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficiency of the designs may be not sufficient. With the help of third-wave artificial intelligence technologies, the design schemes of these materials are undergoing a new revolution. As an important branch of artificial intelligence, machine learning paves the way to new technological innovations by stimulating the exploration of structural design. Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures. Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design. In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. Then discuss machine learning driven on-demand design of phononic metamaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties. Finally, we summarize the current state of the art and provide a prospective of the future development directions.

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