The Influence of Crowdsourcing Business Model Into Artificial Intelligence

2019 ◽  
pp. 546-556
Author(s):  
Anna Szopa
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Business Process ◽  
Machine Intelligence ◽  
Human Computation ◽  
Human Intelligence ◽  
Efficiency Of Algorithms ◽  
Large Databases ◽  
Hybrid Solutions ◽  
The Web

We observe situation in which human computation processes leverage the abilities of people via the Web to perform complex computation. In this context human intelligence is harnessed to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. The aim of this paper is to achieve an in-depth understanding of the influence of crowdfunding business process into Artificial Intelligence and what kind of computational problems can be efficiently answered using human computation. Wisdom of crowd has been applied successfully to a range of tasks, from translating text and marking images, collecting information to building large databases and to solve problems that are difficult for machines to solve. Presented hybrid solutions will be considered as optimization- leveraging machine intelligence to help improve the accuracy and efficiency of algorithms.

The Influence of Crowdsourcing Business Model into Artificial Intelligence

Crowdsourcing ◽  
2019 ◽  
pp. 246-256
Author(s):  
Anna Szopa
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Business Process ◽  
Machine Intelligence ◽  
Human Computation ◽  
Human Intelligence ◽  
Efficiency Of Algorithms ◽  
Large Databases ◽  
Hybrid Solutions ◽  
The Web

We observe situation in which human computation processes leverage the abilities of people via the Web to perform complex computation. In this context human intelligence is harnessed to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. The aim of this paper is to achieve an in-depth understanding of the influence of crowdfunding business process into Artificial Intelligence and what kind of computational problems can be efficiently answered using human computation. Wisdom of crowd has been applied successfully to a range of tasks, from translating text and marking images, collecting information to building large databases and to solve problems that are difficult for machines to solve. Presented hybrid solutions will be considered as optimization- leveraging machine intelligence to help improve the accuracy and efficiency of algorithms.

The Influence of Crowdsourcing Business Model into Artificial Intelligence

2017 ◽  
pp. 15-28
Author(s):  
Anna Szopa
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Business Process ◽  
Machine Intelligence ◽  
Human Computation ◽  
Human Intelligence ◽  
Efficiency Of Algorithms ◽  
Large Databases ◽  
Hybrid Solutions ◽  
The Web

We observe situation in which human computation processes leverage the abilities of people via the Web to perform complex computation. In this context human intelligence is harnessed to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. The aim of this paper is to achieve an in-depth understanding of the influence of crowdfunding business process into Artificial Intelligence and what kind of computational problems can be efficiently answered using human computation. Wisdom of crowd has been applied successfully to a range of tasks, from translating text and marking images, collecting information to building large databases and to solve problems that are difficult for machines to solve. Presented hybrid solutions will be considered as optimization- leveraging machine intelligence to help improve the accuracy and efficiency of algorithms.

Employing artificial intelligence techniques as a mechanism for investigation, scrutiny, and detection of fake news and rumors

2021 ◽  
Vol 12 (1) ◽  
pp. 117-117
Author(s):  
Yosra Sobeih ◽  
El Taieb EL Sadek
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Machine Intelligence ◽  
The Internet ◽  
Human Intelligence ◽  
Fake News ◽  
The Media ◽  
Media Work ◽  
False News ◽  
Editorial Processing

Modern communication means have imposed many changes on the media work in the different stages of content production, starting from gathering news, visual and editorial processing, verification and verification of the truthfulness of what was stated in it until its publication, so the changes that were stimulated by modern means and technologies and artificial intelligence tools have affected all stages of news and media production, since the beginning of the emergence of rooms. Smart news that depends on human intelligence and then machine intelligence, which has become forced to keep pace with the development in communication means, which has withdrawn in the various stages of production, and perhaps the most important of which is the process of investigation and scrutiny and the detection of false news and rumors in our current era, which has become the spread of information very quickly through the Internet and websites Social media and various media platforms

On Abstract Intelligence

2012 ◽  
pp. 14-31
Author(s):  
Yingxu Wang
Keyword(s):  
Artificial Intelligence ◽  
Formal Model ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
Engineering Applications ◽  
Measurement Framework ◽  
Wide Range ◽  
Intelligence Theories ◽  
The Brain ◽  
Intelligent Quotient

Abstract intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the Generic Abstract Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract intelligence theories, the compatibility of natural and machine intelligence is revealed in order to investigate into a wide range of paradigms of abstract intelligence such as natural, artificial, machinable intelligence, and their engineering applications.

scholarly journals Human vs machine intelligence:

2019 ◽  
Vol 9 (1) ◽  
pp. 17-24
Author(s):  
Thomas Bolander
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
The Future ◽  
The Impact

To be able to predict the impact of artificial intelligence (AI) on the required human competences of the future, it is firstand foremost necessary to get an overview of what AI at all is and how it differs from human intelligence. The main goalof this paper is to provide such an overview to readers who are not experts in the area. The focus of the paper is on thesimilarities and differences between human and machine intelligence, since understanding that is of essential importanceto be able to predict which human tasks and jobs are likely to be automatised by AI - and what consequences it will have.

scholarly journals HUMAN INTELLIGENCE ANALYSIS AND DETECTING SCENARIOS USING ARTIFICIAL INTELLIGENCE BASED ON HUMAN COMPUTER INTERACTION IN EDUCATION SYSTEM

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Akash Dilip Hatalge ◽  
Dr. Bashirahamad F. Momin
Keyword(s):  
Artificial Intelligence ◽  
Education System ◽  
Web Application ◽  
Continuous Monitoring ◽  
User Model ◽  
Human Intelligence ◽  
Software Framework ◽  
Intelligence Analysis ◽  
Huge Amount ◽  
The Web

It is different from the situation that people have just experienced normal information from general websites. Users of an educational web application for teaching as faculty and students need more communication with each other. That means they desperately need to communicate with mediators like computers and other resources. Therefore, research conducted on the design of human computers takes a huge amount of value. In this paper, a user model of Educational Web Application is concluded. The Web Application includes that Facial Recognition for login, attendance and to see the student’s behavior in the classroom by continuous monitoring. Knowledge trees and tests are designed for better understanding and to improve yourself in particular or all subjects. The aim of the paper is to develop a system to manage and maintain sentiments and detecting student’s behavior by humancomputer interaction. We propose a software framework to monitor boundaries, analyze them and support users to see how they interact in conversations or lectures and manage their emotions.

Teaching and Explanation: Aligning Priors between Machines and Humans

2021 ◽  
pp. 171-196
Author(s):  
José Hernández-Orallo ◽  
Cèsar Ferri
Keyword(s):  
Artificial Intelligence ◽  
Human Perception ◽  
Human Learning ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
Machine Perception ◽  
Explainable Artificial Intelligence ◽  
The Future ◽  
The Way

Machine intelligence differs signficantly from human intelligence. While human perception has similarities to the way machine perception works, human learning is mostly a directed process, guided by other people: parents, teachers, ... The area of machine teaching is becoming increasingly popular as a different paradigm for making machines learn. In this chapter, we start from recent results in machine teaching that show the relevance of prior alignment between humans and machines. From here, we focus on the scenario when a machine has to teach humans, a situation more and more common in the future. Specifically, we analyse how machine teaching relates to explainable artificial intelligence, and how simplicity priors play a role beyond intelligibility. We illustrate this with a general teaching protocol and a few examples in several representation languages: feature-value vectors and sequences. Some straightforward experiments with humans indicate when a strong simplicity prior is --and is not-- sufficient.

Defining Artificial Intelligence

2016 ◽  
Author(s):  
Jerry Kaplan
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Human Intelligence

What is artificial intelligence? That’s an easy question to ask and a hard one to answer—for two reasons. First, there’s little agreement about what intelligence is. Second, there’s scant reason to believe that machine intelligence bears much relationship to human intelligence, at least so...

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

Intelligence, Artificial and Otherwise

2019 ◽  
Vol 24 (2) ◽  
pp. 241-258
Author(s):  
Paul Dumouchel
Keyword(s):  
Artificial Intelligence ◽  
Human Intelligence

The idea of artificial intelligence implies the existence of a form of intelligence that is “natural,” or at least not artificial. The problem is that intelligence, whether “natural” or “artificial,” is not well defined: it is hard to say what, exactly, is or constitutes intelligence. This difficulty makes it impossible to measure human intelligence against artificial intelligence on a unique scale. It does not, however, prevent us from comparing them; rather, it changes the sense and meaning of such comparisons. Comparing artificial intelligence with human intelligence could allow us to understand both forms better. This paper thus aims to compare and distinguish these two forms of intelligence, focusing on three issues: forms of embodiment, autonomy and judgment. Doing so, I argue, should enable us to have a better view of the promises and limitations of present-day artificial intelligence, along with its benefits and dangers and the place we should make for it in our culture and society.

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