The Use of Copyrighted Works by AI Systems: Art Works in the Data Mill

2019 ◽  
Vol 11 (1) ◽  
pp. 51-69
Author(s):  
Mirko DEGLI ESPOSTI ◽  
Francesca LAGIOIA ◽  
Giovanni SARTOR
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Representation ◽  
Creative Process ◽  
Legal Aspects ◽  
Text Generation ◽  
Art Works ◽  
Systems Art ◽  
Applications Of Ai

We shall first introduce the use of artificial intelligence (AI) in producing new intellectual creations, distinguishing approaches based on knowledge representation and on machine learning. Then we shall provide an overview of some significant applications of AI to the production of intellectual creations, distinguishing the extent to which they depend on pre-existing works, and the different ways in which such pre-existing works are used in the creative process. In addition, we shall discuss some methods to automatically assess the similarity of works and styles, in the context of AI technologies for text generation. Finally, we shall discuss the legal aspects of AI-reuse of copyrighted works, focusing on the rights of the authors of such works relative to the process and the outputs of AI.

scholarly journals The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine

2019 ◽  
Vol 28 (01) ◽  
pp. 027-034 ◽  
Author(s):  
Laszlo Balkanyi ◽  
Ronald Cornet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Frequency Analysis ◽  
Medical Subject Headings ◽  
Subject Headings

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.

scholarly journals CUSTOMER PREDICTIVE ANALYTICS USING ARTIFICIAL INTELLIGENCE

2020 ◽  
pp. 1-12
Author(s):  
SITI ZULAIKHA ◽  
HAZIK MOHAMED ◽  
MASMIRA KURNIAWATI ◽  
SULISTYA RUSGIANTO ◽  
SYLVA ALIF RUSMITA
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Historical Analysis ◽  
Review Of The Literature ◽  
Use Of Technology ◽  
Customer Targeting ◽  
Conceptual Paper ◽  
Customer Lifecycle ◽  
Applications Of Ai

This conceptual paper exclusively focused on how artificial intelligence (AI) serves as a means to identify a target audience. Focusing on the marketing context, a structured discussion of how AI can identify the target customers precisely despite their different behaviors was presented in this paper. The applications of AI in customer targeting and the projected effectiveness throughout the different phases of customer lifecycle were also discussed. Through the historical analysis, behavioral insights of individual customers can be retrieved in a more reliable and efficient way. The review of the literature confirmed the use of technology-driven AI in revolutionizing marketing, where data can be processed at scale via supervised or unsupervised (machine) learning.

scholarly journals Role of Machine Learning & Artificial Intelligence Techniques in Software Testing

2021 ◽  
Vol 12 (6) ◽  
pp. 2913-2921
Author(s):  
Nilofar Mulla, Dr. Naveenkumar Jayakumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Machine Learning Techniques ◽  
Business Growth ◽  
Growth Machine ◽  
Learning Techniques ◽  
Test Suites ◽  
Applications Of Ai

This study provides information about the use of artificial intelligence (AI) and machine learning (ML) techniques in the field of software testing. The use of AI in software testing is still in its initial stages. Also the automation level is lesser compared to more evolved areas of work.AI and ML can be used to help reduce tediousness and automate tasks in software testing. Testing can be made more efficient and smarter with the help of AI. Researchers recognize potential of AI to bridge the gap between human and machine driven testing capabilities. There are still number of challenges to fully utilize AI and ML techniques in testing but it will definitely enhance the entire testing process and skills of testers and will contribute in business growth. Machine learning research is a subset of overall AI research. The life-cycle of software is increasingly shortening and becoming more complicated. There is a struggle in software development between the competing pressures of developing software and meeting deadlines. AI-powered automated testing makes conducting full test suites in a timely manner on every change. In this article a detailed overview about the various applications of AI in software testing have been demonstrated. Also the implementation of machine learning in software testing has been discussed in detail and use of different machine learning techniques has been explained as well.

Artificial intelligence--- Future of Anesthesiology!!

2019 ◽  
pp. 247-249
Author(s):  
Tariq H. Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intensive Care ◽  
Routine Practice ◽  
Surgical Practice ◽  
Anesthesia Practice ◽  
The Face ◽  
To Come ◽  
The Many ◽  
Applications Of Ai

The term artificial intelligence (AI) was introduced in 1950. There have been many attempts to develop machines capable of performing cognitive and skill based tasks of anesthesiologist based on the principles of AI. These attempts have not been successful because of the complexities of anesthesia practice. Recent innovations in AI, especially machine learning, will continue to grow in importance in the years to come and will greatly revolutionize the face of anesthesia along with surgical practice, perioperative medicine practiced in clinics, and imaging interpretation. Anesthesiologists should continue to embrace this technology, stay up to date with the advances in AI, and also make genuine efforts to smoothly assimilate it in their routine practice now so that they can be the revolutionaries of their own future. We hope to see an ever-widening spectrum of the uses of AI in all fields of medical practice, and anesthesiology is not an exception. Its time our friends start visualizing the many applications of AI in their practice. Citation: Khan FH, Fazal M. Artificial intelligence--- Future of Anesthesiology!! Anaesth pain & intensive care 2019;23(3):247-249

scholarly journals Introduction to the TPLP Special Issue on User-oriented Logic Programming and Reasoning Paradigms

2019 ◽  
Vol 19 (2) ◽  
pp. 109-113
Author(s):  
STEFAN ELLMAUTHALER ◽  
CLAUDIA SCHULZ
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Unstructured Data ◽  
Knowledge Representation And Reasoning ◽  
Special Issue

With the rise of machine learning, and more recently the overwhelming interest in deep learning, knowledge representation and reasoning (KRR) approaches struggle to maintain their position within the wider Artificial Intelligence (AI) community. Often considered as part of thegood old-fashioned AI(Haugeland 1985) – like a memory of glorious old days that have come to an end – many consider KRR as no longer applicable (on its own) to the problems faced by AI today (Blackwell 2015; Garneloet al.2016). What they see are logical languages with symbols incomprehensible by most, inference mechanisms that even experts have difficulties tracing and debugging, and the incapability to process unstructured data like text.

scholarly journals The Potential of Artificial Intelligence, Machine Learning, and Novel Analytic Methods to Promote Successful Aging

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 655-655
Author(s):  
Walter Boot
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Older Adults ◽  
Autonomous Vehicles ◽  
Successful Aging ◽  
Potential Role ◽  
Gerontological Society ◽  
Analytic Methods ◽  
Applications Of Ai

Abstract The Gerontological Society of America is celebrating its75th anniversary and in those75 years the world has undergone an amazing technological revolution. During this period, computers transformed from systems that once filled entire rooms to much more powerful devices that fit in our pockets. We have seen the introduction of wireless technologies, augmented and virtual reality, smart home devices, autonomous vehicles, and much more. This session focuses on a new technological advance that has the potential to support the health, wellbeing, and independence of older adults and caregivers: artificial intelligence (AI). This session will present applications of AI, Machine Learning (ML), and other novel analytic methods and how they have the potential to impact the lives of older adults in a variety of context. As AI is increasingly being involved in workplace hiring, the first talk focuses on older adults’ attitudes toward the role of AI in this decision making process. Next, novel ML approaches applied to social media are discussed in terms of understanding the needs of Alzheimer’s caregivers. Next, ML techniques are discussed in terms of developing biomarkers that can be applied in diagnosis and assessment of therapeutic responses by detecting mood, which may have important implications for older adults living with dementia. Then, the potential role of AI is discussed in terms of developing reminder systems to promote older adults’ adherence to technology-based health activities. Finally, novel analytic approaches are discussed in terms of harnessing digital metrics to detect the risk of cognitive decline.

scholarly journals Crossing Point of Artificial Intelligence in Cybersecurity

2015 ◽  
Vol 2 (3) ◽  
pp. 121-128
Author(s):  
Praveen Kumar Donepudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Cyber Threats ◽  
Crossing Point ◽  
Insight Into ◽  
Distinct Approach ◽  
Learning Data ◽  
Applications Of Ai

There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.  

AI in actuarial science – a review of recent advances – part 2

2020 ◽  
pp. 1-29 ◽  
Author(s):  
Ronald Richman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Actuarial Science ◽  
Interested Reader ◽  
Supplementary Appendix ◽  
New Techniques ◽  
Modern Approach ◽  
Mortality Modelling ◽  
Applications Of Ai

Abstract Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate, but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning”. This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. Part 1 of this paper provides background on machine learning and deep learning, as well as an heuristic for where actuaries might benefit from applying these techniques. Part 2 of the paper then surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. Part 2 concludes with an outlook on the potential for actuaries to integrate deep learning into their activities. Finally, a supplementary appendix discusses further resources providing more in-depth background on machine learning and deep learning.

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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