scholarly journals Artificial intelligence is an oxymoron

AI & Society ◽  
2021 ◽  
Author(s):  
Jakob Svensson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Evolutionary Biology ◽  
Unintended Consequences ◽  
Present Moment ◽  
Automated Systems ◽  
Cognitive Sciences ◽  
Nicholas Of Cusa ◽  
Organic Body

AbstractDeparting from popular imaginations around artificial intelligence (AI), this article engages in the I in the AI acronym but from perspectives outside of mathematics, computer science and machine learning. When intelligence is attended to here, it most often refers to narrow calculating tasks. This connotation to calculation provides AI an image of scientificity and objectivity, particularly attractive in societies with a pervasive desire for numbers. However, as is increasingly apparent today, when employed in more general areas of our messy socio-cultural realities, AI- powered automated systems often fail or have unintended consequences. This article will contribute to this critique of AI by attending to Nicholas of Cusa and his treatment of intelligence. According to him, intelligence is equally dependent on an ability to handle the unknown as it unfolds in the present moment. This suggests that intelligence is organic which ties Cusa to more contemporary discussions in tech philosophy, neurology, evolutionary biology, and cognitive sciences in which it is argued that intelligence is dependent on having—and acting through—an organic body. Understanding intelligence as organic thus suggests an oxymoronic relationship to artificial.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

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.

scholarly journals Reports on the 2017 AAAI Spring Symposium Series

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 99-106
Author(s):  
Jeannette Bohg ◽  
Xavier Boix ◽  
Nancy Chang ◽  
Elizabeth F. Churchill ◽  
Vivian Chu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
User Experience ◽  
Object Perception ◽  
Learning Systems ◽  
Language Understanding ◽  
Social Good ◽  
Symposium Series ◽  
The Social

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27–29, 2017 on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01); Computational Construction Grammar and Natural Language Understanding (SS-17-02); Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03); Designing the User Experience of Machine Learning Systems (SS-17-04); Interactive Multisensory Object Perception for Embodied Agents (SS-17-05); Learning from Observation of Humans (SS-17-06); Science of Intelligence: Computational Principles of Natural and Artificial Intelligence (SS-17-07); and Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place.

scholarly journals Measurement of Ethical Issues in Software Products

2020 ◽  
Author(s):  
Francesco Di Tria
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Ethical Issues ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Software Products ◽  
Ethics Research ◽  
Software Product ◽  
Formal Requirements

Ethics is a research field that is obtaining more and more attention in Computer Science due to the proliferation of artificial intelligence software, machine learning algorithms, robot agents (like chatbot), and so on. Indeed, ethics research has produced till now a set of guidelines, such as ethical codes, to be followed by people involved in Computer Science. However, a little effort has been spent for producing formal requirements to be included in the design process of software able to act ethically with users. In the paper, we investigate those issues that make a software product ethical and propose a set of metrics devoted to quantitatively evaluate if a software product can be considered ethical or not.

scholarly journals Supervised Learning Methods and Applications in Medical Research

2022 ◽  
pp. 27-34
Author(s):  
Yung Ming ◽  
Lily Yuan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Feature Extraction ◽  
Job Performance ◽  
Computational Linguistics ◽  
New Technologies ◽  
Voice Recognition ◽  
Autonomous Driving ◽  
Automated Systems

Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.

Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors

2021 ◽  
Vol 5 (2) ◽  
pp. 113
Author(s):  
Youngseok Lee ◽  
Jungwon Cho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Model Concept ◽  
Core Technology ◽  
Machine Learning Model ◽  
The Social ◽  
Education Model ◽  
Intelligence Education ◽  
Near Future

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. AI is being rapidly applied across industries and is developing as a core technology to enable a society led by knowledge and information. An AI education focused on problem solving and learning is efficient for computer science education. Thus, the time has come to prepare for AI education along with existing software education so that they can adapt to the social and job changes enabled by AI. In this paper, we explain a classification method for AI machine learning models and propose an AI education model using teachable machines. Non-computer majors can understand the importance of data and the AI model concept based on specific cases using AI education tools to understand and experiment with AI even without the knowledge of mathematics, and use languages such as Python, if necessary. Through the application of the machine learning model, AI can be smoothly utilized in their field of interest. If such an AI education model is activated, it will be possible to suggest the direction of AI education for collaboration with AI experts through the application of AI technology.

scholarly journals Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

2009 ◽  
Vol 2009 ◽  
pp. 1-25 ◽  
Author(s):  
Ryan J. Urbanowicz ◽  
Jason H. Moore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Discovery ◽  
Evolutionary Biology ◽  
Historical Review ◽  
Machine Learning Algorithms ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Rule Based ◽  
Learning Classifier

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.

scholarly journals Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias

2021 ◽  
Author(s):  
Markus Langer ◽  
Cornelius J. König ◽  
Caroline Back ◽  
Victoria Hemsing
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Personnel Selection ◽  
Automated System ◽  
Ethical Standards ◽  
Automated Systems ◽  
Trust Repair ◽  
Selection Tasks ◽  
Decision Tasks

Introducing automated systems based on artificial intelligence and machine learning for ethically sensitive decision tasks requires investigating of trust processes in relation to such tasks. In an example of such a task (personnel selection), this study investigates trustworthiness, trust, and reliance in light of a trust violation relating to ethical standards and a trust repair intervention. Specifically, participants evaluated applicant preselection outcomes by either a human or an automated system across twelve personnel selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds five through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to a violation of ethical standards. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). Results showed that participants initially perceived automated systems to be less trustworthy, and had less intention to trust automated systems. Specifically, participants perceived systems to be less able, and flexible, but also less biased – a result that was sustained even in light of unfair bias. Furthermore, in regard to the automated system the trust violation and the trust repair intervention had weaker effects. Those effects were partly stronger when highlighting imperfection for the automated system. We conclude that it is crucial to investigate trust processes in relation to automated systems in ethically sensitive domains such as personnel selection as insights from classical areas of automation might not translate to application contexts where ethical standards are central to trust processes.

Quantum-Computing-Inspired Algorithms in Machine Learning

2018 ◽  
pp. 1-26
Author(s):  
Deeksha Kaul ◽  
Harika Raju ◽  
B. K. Tripathy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Quantum Computing ◽  
Computer Science ◽  
Real Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

scholarly journals Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness

2019 ◽  
Author(s):  
Chelsea Chandler ◽  
Peter W Foltz ◽  
Brita Elvevåg
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Multidisciplinary Approach ◽  
Clinical Applications ◽  
Complicated Process ◽  
Rigorous Framework ◽  
Suitable Combination ◽  
Wild West

Abstract The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current “wild west”; a multidisciplinary approach that is very technical and complex, yet seems to produce findings that resonate. These studies are hard to review as the methods are often opaque and it is tricky to find the suitable combination of reviewers. This issue will only get more complex in the absence of a rigorous framework to evaluate such studies and thus nurture trustworthiness. Therefore, our paper discusses the urgency of the field to develop a framework with which to evaluate the complex methodology such that the process is done honestly, fairly, scientifically, and accurately. However, evaluation is a complicated process and so we focus on three issues, namely explainability, transparency, and generalizability, that are critical for establishing the viability of using artificial intelligence in psychiatry. We discuss how defining these three issues helps towards building a framework to ensure trustworthiness, but show how difficult definition can be, as the terms have different meanings in medicine, computer science, and law. We conclude that it is important to start the discussion such that there can be a call for policy on this and that the community takes extra care when reviewing clinical applications of such models..

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