scholarly journals Social aspects of Artificial Intelligence – selected issues

2021 ◽  
pp. 279-294
Author(s):  
Marcin Kowalczyk
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Free Will ◽  
Biometric Identification ◽  
Surveillance Systems ◽  
Web Browsers ◽  
Human Beings ◽  
Digital Era ◽  
Dating Apps ◽  
On Line

The paper presents findings regarding AI and Machine Learning and how “thinking machines” differ from human beings? In the next part the paper presents the issue of AI and Machine Learning’s impact on day-to-day activities in the following areas: 1. Microtargetting and psychometrics – with the examples from the business and politics; 2. Surveillance systems, biometric identification, COVID 19 tracing apps etc. – the issue of privacy in the digital era; 3. The question of choice optimization (AI-driven Web browsers and dating apps, chatbots and virtual assistants etc.); whether free will still exist in the AI supported on-line environment? The article is summed up with conclusions.

scholarly journals Urban living environment assessment index system based on psychological security

2019 ◽  
Author(s):  
Xia Huiyi ◽  
◽  
Nankai Xia ◽  
Liu Liu ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Collection ◽  
Urban Environment ◽  
Evaluation System ◽  
Living Environment ◽  
Mobile Internet ◽  
Practical Significance ◽  
Human Beings ◽  
Urban Living

With the development of urbanization and the continuous development, construction and renewal of the city, the living environment of human beings has also undergone tremendous changes, such as residential community environment and service facilities, urban roads and street spaces, and urban public service formats. And the layout of the facilities, etc., and these are the real needs of people in urban life, but the characteristics of these needs or their problems will inevitably have a certain impact on the user's psychological feelings, thus affecting people's use needs. Then, studying the ways in which urban residents perceive changes in the living environment and how they perceive changes in psychology and emotions will have practical significance and can effectively assist urban management and builders to optimize the living environment of residents. This is also the long-term. One of the topics of greatest interest to urban researchers since then. In the theory of demand hierarchy proposed by American psychologist Abraham Maslow, safety is the basic requirement second only to physiological needs. So safety, especially psychological security, has become one of the basic needs of people in the urban environment. People's perception of the psychological security of the urban environment is also one of the most important indicators in urban environmental assessment. In the past, due to the influence of technical means, the study of urban environmental psychological security often relied on the limited investigation of a small number of respondents. Low-density data is difficult to measure the perceptual results of universality. With the leaping development of the mobile Internet, Internet image data has grown geometrically over time. And with the development of artificial intelligence technology in recent years, image recognition and perception analysis based on machine learning has become possible. The maturity of these technical conditions provides a basis for the study of the urban renewal index evaluation system based on psychological security. In addition to the existing urban visual street furniture data obtained through urban big data collection combined with artificial intelligence image analysis, this paper also proposes a large number of urban living environment psychological assessment data collection strategies. These data are derived from crowdsourcing, and the collection method is limited by the development of cost and technology. At present, the psychological security preference of a large number of users on urban street images is collected by forced selection method, and then obtained by statistical data fitting to obtain urban environmental psychology. Security sense training set. In the future, when the conditions are mature, the brainwave feedback data in the virtual reality scene can be used to carry out the machine learning of psychological security, so as to improve the accuracy of the psychological security data.

scholarly journals Artificial Intelligence: A New Tool in Oncologist's Armamentarium

2021 ◽  
Author(s):  
Vineet Talwar ◽  
Kundan Singh Chufal ◽  
Srujana Joga
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Organs At Risk ◽  
Elderly Care ◽  
External Validation ◽  
Data Access ◽  
Human Beings ◽  
Area Of Interest ◽  
Segmentation Image

AbstractArtificial intelligence (AI) has become an essential tool in human life because of its pivotal role in communications, transportation, media, and social networking. Inspired by the complex neuronal network and its functions in human beings, AI, using computer-based algorithms and training, had been explored since the 1950s. To tackle the enormous amount of patients' clinical data, imaging, histopathological data, and the increasing pace of research on new treatments and clinical trials, and ever-changing guidelines for treatment with the advent of novel drugs and evidence, AI is the need of the hour. There are numerous publications and active work on AI's role in the field of oncology. In this review, we discuss the fundamental terminology of AI, its applications in oncology on the whole, and its limitations. There is an inter-relationship between AI, machine learning and, deep learning. The virtual branch of AI deals with machine learning. While the physical branch of AI deals with the delivery of different forms of treatment—surgery, targeted drug delivery, and elderly care. The applications of AI in oncology include cancer screening, diagnosis (clinical, imaging, and histopathological), radiation therapy (image acquisition, tumor and organs at risk segmentation, image registration, planning, and delivery), prediction of treatment outcomes and toxicities, prediction of cancer cell sensitivity to therapeutics and clinical decision-making. A specific area of interest is in the development of effective drug combinations tailored to every patient and tumor with the help of AI. Radiomics, the new kid on the block, deals with the planning and administration of radiotherapy. As with any new invention, AI has its fallacies. The limitations include lack of external validation and proof of generalizability, difficulty in data access for rare diseases, ethical and legal issues, no precise logic behind the prediction, and last but not the least, lack of education and expertise among medical professionals. A collaboration between departments of clinical oncology, bioinformatics, and data sciences can help overcome these problems in the near future.

Applications of Machine Learning in Cyber Security Domain

2020 ◽  
pp. 64-82 ◽  
Author(s):  
Sailesh Suryanarayan Iyer ◽  
Sridaran Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
Traditional Society ◽  
Future Trends ◽  
Human Beings ◽  
Online Transactions ◽  
Network Intrusion ◽  
The World ◽  
Applications Of Machine Learning

Knowledge revolution is transforming the globe from traditional society to a technology-driven society. Online transactions have compounded, exposing the world to a new demon called cybercrime. Human beings are being replaced by devices and robots, leading to artificial intelligence. Robotics, image processing, machine vision, and machine learning are changing the lifestyle of citizens. Machine learning contains algorithms which are capable of learning from historical occurrences. This chapter discusses the concept of machine learning, cyber security, cybercrime, and applications of machine learning in cyber security domain. Malware detection and network intrusion are a few areas where machine learning and deep learning can be applied. The authors have also elaborated on the research advancements and challenges in machine learning related to cyber security. The last section of this chapter lists the future trends and directions in machine learning and cyber security.

A Short Introduction to the Ethics of Artificial Intelligence

2020 ◽  
pp. 1-42
Author(s):  
S. Matthew Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Systems ◽  
Ethical Issues ◽  
Black Box ◽  
Human Beings ◽  
Key Issues ◽  
Long Term Impact ◽  
Bad Data

This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

scholarly journals A Generic Model of the World and Intelligence

10.29007/s6vh ◽  
2019 ◽  
Author(s):  
Harris Wang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Object Oriented ◽  
Generic Model ◽  
Human Beings ◽  
Primary Methods ◽  
The World ◽  
Object Hierarchies

The resurgence of interest in Artificial Intelligence and advances in several fronts of AI, machine learning with neural network in particular, have made us think again about the nature of intelligence, and the existence of a generic model that may be able to capture what human beings have in their mind about the world to empower them to present all kinds of intelligent behaviors. In this paper, we present Constrained Object Hierarchies (COHs) as such a generic model of the world and intelligence. COHs extend the well-known object-oriented paradigm by adding identity constraints, trigger constraints, goal constraints, and some primary methods that can be used by capable beings to accomplish various intelligence, such as deduction, induction, analogy, recognition, construction, learning and many others.In the paper we will first argue the need for such a generic model of the world and intelligence, and then present the generic model in detail, including its important constructs, the primary methods capable beings can use, as well as how different intelligent behaviors can be implemented and achieved with this generic model.

What does it mean to be me?

2018 ◽  
Author(s):  
Andrew Briggs ◽  
Hans Halvorson ◽  
Andrew Steane
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Theory ◽  
Free Will ◽  
Learning Systems ◽  
Evolutionary Development ◽  
The Bible ◽  
Recent Advances ◽  
Autonomous Machines

The chapter poses questions about personhood, and explores them through some philosophy, extended examples from machine learning and artificial intelligence, and religious reflection. Parfit’s Reasons and Persons and the use of game theory is explored. The question of human free will is framed as centring on the issue of responsibility. Recent advances in AI, especially learning systems such as AlphaGo, are presented. These do not settle any fundamental questions about the nature of consciousness, but they do encourage us to ask what our attitude to autonomous machines should be. The discussion then turns to human evolutionary development, and to what makes humans distinctive, touching on scientific, philosophical, and theological issues. Some aspects of philosophy and theology can be productively approached through storytelling; this fruitful method is seen at work in the Bible. To be responsible lies at the heart of what it means to be human.

scholarly journals Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 354
Author(s):  
Octavian Sabin Tătaru ◽  
Mihai Dorin Vartolomei ◽  
Jens J. Rassweiler ◽  
Oșan Virgil ◽  
Giuseppe Lucarelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Pathology ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Data Sets ◽  
Human Beings ◽  
Proper Actions ◽  
Patient Will

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.

scholarly journals Learning by Enhancing Half-Baked AI Projects

2021 ◽  
Author(s):  
Ken Kahn ◽  
Niall Winters
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Image Recognition ◽  
Language Processing ◽  
Speech Synthesis ◽  
Web Browsers ◽  
School Students ◽  
Hands On

AbstractWe have developed thirty sample artificial intelligence (AI) programs in a form suitable for enhancement by non-expert programmers. The projects are implemented in the Snap! blocks language and can be run in modern web browsers. These projects have been designed to be modifiable by school students and have been iteratively developed with over 100 students. The projects involve speech synthesis, speech and image recognition, natural language processing, and deep machine learning. They illustrate a variety of AI capabilities, concepts, and techniques. The intent is to provide students with hands-on experience with AI programming so they come to understand the possibilities, problems, strengths, and weaknesses of AI today.

Artificial Intelligence: Creating Post-Human Beings

ITNOW ◽  
2020 ◽  
Vol 62 (2) ◽  
pp. 62-63
Author(s):  
Blair Melsom
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Science Fiction ◽  
Learning Technologies ◽  
Human Beings ◽  
Award Winning

Abstract What does it mean to be human? That’s the existential question award-winning artist Cecilie Waagner Falkenstrøm has lately been using machine learning technologies to explore. Here, she talks to Blair Melsom AMBCS about how art, science fiction and algorithms converge to provoke thoughts on the ethics of future humanised technology.

Artificial Intelligence and Learning

Education ◽  
2021 ◽  
Author(s):  
Jaekyung Lee ◽  
Richard Lamb ◽  
Sunha Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Cognitive Abilities ◽  
Human Beings ◽  
Technological Advances ◽  
Future Society ◽  
Self Driving Cars ◽  
Selection Of ◽  
Applications Of Ai

Rapid technological advances, particularly recent artificial intelligence (AI) revolutions such as digital assistants (e.g., Alexa, Siri), self-driving cars, and cobots and robots, have changed human lives and will continue to have even bigger impact on our future society. Some of those AI inventions already shocked people across the world by wielding their power of surpassing human intelligence and cognitive abilities; see, for example, the examples of Watson (IBM’s supercomputer) and AlphaGo (Google DeepMind’s AI program) beating the human champions of Jeopardy and Go games, respectively. Then many questions arise. How does AI affect human beings and the larger society? How should we educate our children in the AI age? What changes are necessary to help humans better adapt and flourish in the AI age? What are the key enablers of the AI revolution, such as big data and machine learning? What are the applications of AI in education and how do they work? Answering these critical questions requires interdisciplinary research. There is no shortage of research on AI per se, since it is a highly important and impactful research topic that cuts across many fields of science and technology. Nevertheless, there are no effective guidelines for educational researchers and practitioners that give quick summaries and references on this topic. Because the intersection of AI and education/learning is an emerging field of research, the literature is in flux and the jury is still out. Thus, our goal here is to give readers a quick introduction to this broad topic by drawing upon a limited selection of books, reports, and articles. This entry is organized into three major sections, where we present commentaries along with a list of annotated references on each of the following areas: (1) AI Impacts on the Society and Education; (2) AI Enablers: Big Data in Education and Machine Learning; and (3) Applications of AI in Education: Examples and Evidence.

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