Behavior Morphologies of Machine Learning Agents in Media Artworks

Leonardo ◽  
2019 ◽  
pp. 1-10
Author(s):  
Sofian Audry
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Life ◽  
Adaptive Systems ◽  
Learning Systems ◽  
Artificial Agents ◽  
Adaptive Agents ◽  
Learning Agents ◽  
The Aesthetic

Since the 1950s, a range of artists have used artificial agents in their work, in parallel with scientific research in cybernetics, artificial intelligence (AI) and artificial life (AL). In particular, an increasing number of artists work with machine learning and other adaptive systems. Through my own engagement with such systems, I analyze adaptive agents within the broader context of the aesthetic of behavior. As a result, I propose an aesthetic framework for understanding behaviors which considers the role of the observer as an adaptive perceiving agent, the unfathomable character of machine learning systems, and the morphology of behaviors as time-based phenomenon.

scholarly journals A grounded theory of abstraction in artificial intelligence

2003 ◽  
Vol 358 (1435) ◽  
pp. 1293-1309 ◽  
Author(s):  
Jean-Daniel Zucker
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Grounded Theory ◽  
Computational Complexity ◽  
Adaptive Systems ◽  
Point Of View ◽  
Information Quantity ◽  
Complementary Role ◽  
Representation Language

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

A Behavioral Economics Approach to Digitalization

2019 ◽  
pp. 107-122
Author(s):  
Dirk Beerbaum ◽  
Julia Margarete Puaschunder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Behavioral Economics ◽  
Field Research ◽  
Learning Systems ◽  
Business Reporting ◽  
Intelligent Machines ◽  
Private Investors ◽  
Coding Rules

Technological improvement in the age of information has increased the possibilities to control the innocent social media users or penalize private investors and reap the benefits of their existence in hidden persuasion and discrimination. This chapter takes as a case the transparency technology XBRL (eXtensible Business Reporting Language), which should make data more accessible as well as usable for private investors. Considering theoretical literature and field research, a representation issue for principles-based accounting taxonomies exists, which intelligent machines applying artificial intelligence (AI) nudge to facilitate decision usefulness. This chapter conceptualizes ethical questions arising from the taxonomy engineering based on machine learning systems and advocates for a democratization of information, education, and transparency about nudges and coding rules.

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 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.

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