Back to the future: The return of cognitive functionalism

2017 ◽  
Vol 40 ◽  
Author(s):  
Leyla Roskan Çağlar ◽  
Stephen José Hanson
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Knowledge Representation ◽  
Learning Process ◽  
Explicit Knowledge ◽  
Model Building ◽  
Causal Models ◽  
Learning Systems ◽  
The Future ◽  
Implicit And Explicit

AbstractThe claims that learning systems must build causal models and provide explanations of their inferences are not new, and advocate a cognitive functionalism for artificial intelligence. This view conflates the relationships between implicit and explicit knowledge representation. We present recent evidence that neural networks do engage in model building, which is implicit, and cannot be dissociated from the learning process.

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.

The Future of Technology Enhanced Active Learning

2008 ◽  
pp. 348-375 ◽  
Author(s):  
Claus Pahl ◽  
Claire Kenny
Keyword(s):  
Active Learning ◽  
Best Practices ◽  
Learning Process ◽  
Knowledge Construction ◽  
Active Participation ◽  
Learning By Doing ◽  
Learning Systems ◽  
Active Involvement ◽  
Future Developments ◽  
The Future

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learner-content interaction, is a central aspect of technology- enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined, and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap.

scholarly journals Automating Cyber Attacks

2020 ◽  
Author(s):  
Ben Buchanan ◽  
John Bansemer ◽  
Dakota Cary ◽  
Jack Lucas ◽  
Micah Musser
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Attacks ◽  
Learning Systems ◽  
Depth Analysis ◽  
The Future

Based on an in-depth analysis of artificial intelligence and machine learning systems, the authors consider the future of applying such systems to cyber attacks, and what strategies attackers are likely or less likely to use. As nuanced, complex, and overhyped as machine learning is, they argue, it remains too important to ignore.

scholarly journals Improving Security and Fairness in Federated Learning Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 37-53
Author(s):  
Andrew R. Short ◽  
Τheofanis G. Orfanoudakis ◽  
Helen C. Leligou
Keyword(s):  
Artificial Intelligence ◽  
Learning Process ◽  
Learning Systems ◽  
Point System ◽  
User Engagement ◽  
User Performance ◽  
Simulation Environment ◽  
Verification Algorithm ◽  
Model Improvement

The ever-increasing use of Artificial Intelligence applications has made apparent that the quality of the training datasets affects the performance of the models. To this end, Federated Learning aims to engage multiple entities to contribute to the learning process with locally maintained data, without requiring them to share the actual datasets. Since the parameter server does not have access to the actual training datasets, it becomes challenging to offer rewards to users by directly inspecting the dataset quality. Instead, this paper focuses on ways to strengthen user engagement by offering “fair” rewards, proportional to the model improvement (in terms of accuracy) they offer. Furthermore, to enable objective judgment of the quality of contribution, we devise a point system to record user performance assisted by blockchain technologies. More precisely, we have developed a verification algorithm that evaluates the performance of users’ contributions by comparing the resulting accuracy of the global model against a verification dataset and we demonstrate how this metric can be used to offer security improvements in a Federated Learning process. Further on, we implement the solution in a simulation environment in order to assess the feasibility and collect baseline results using datasets of varying quality.

Homo Ludens, Machine Ludens: The Way to Ideal Neural Network and Background of Posthumanism

2021 ◽  
Vol 13 (1-1) ◽  
pp. 151-165
Author(s):  
Maria Ivanchenko ◽  
◽  
Pavel Arkhipov ◽  
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Homo Sapiens ◽  
Philosophical Concept ◽  
Critical Methodology ◽  
Strong Artificial Intelligence ◽  
The Future ◽  
History Of ◽  
The Creation

The article consists of an introduction, a main part with three sections and a conclusion. The purpose of the study is to disclose the content of the concepts of “A Man Playing”, “A Machine Playing”, “Posthumanism” and “Essentiocognitivism”; review current advances in artificial intelligence and neural networks. The article focuses on the philosophy of posthumanism in the context of its application in machine learning, as well as a new philosophical concept called “essentiocognitivism” in its relation to artificial intelligence. The object of the study is the philosophical concept of essentiosocognitivism. The subject of the article is the consideration of certain aspects of this concept related to artificial intelligence as a “playing machine” and the positioning of a human being in the world of posthumanism. In the course of the work, critical methodology was used, on the basis of which the strengths and weaknesses of artificial neural networks were highlighted, the current state of the most famous playing neural networks, such as OpenAI and Alpha series from DeepMind, was analyzed, and the upcoming development of AI is considered in the context of a technological singularity. A philosophical comprehension has been made of certain aspects of essentiocognitivism, which play an important role in the history of the development of posthumanism. It is noted that the future of neural networks is largely determined by the gaming industry and moves towards the creation of a strong artificial intelligence, like the Playing Machine. Scientific novelty consists in examining a fundamentally new concept in the history of philosophy and substantiating the place and role of AI in the evolution of intelligent man. In the course of work, it was revealed that AI and, in particular, promising neural networks allow us to predict the probable future of mankind. As a basic thesis, we use the position derived from biological sciences that the evolution of the species Homo sapiens is not over, and will continue in a technological manner. As a result of the study, a working concept of essentiocognitivism was introduced, and the conclusion was made that trans- and posthumanism can solve many global problems of mankind. It is emphasized that the future lies in the creation of a strong AI.

scholarly journals Implementasi Jaringan Syaraf Tiruan (JST) untuk Mengenali Pola Tanda Tangan dengan Metode Backpropagation

2020 ◽  
Vol 3 (2) ◽  
pp. 51-57
Author(s):  
Aldo Vyan Martha ◽  
Mukhtar Hanafi ◽  
Auliya Burhanuddin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computer Technology ◽  
Learning Process ◽  
Learning Algorithms ◽  
Complex Data ◽  
Sample Data ◽  
Backpropagation Method ◽  
Signature Pattern

Artificial Neural Networks (ANN) is a computer technology in the field of artificial intelligence that is able to understand complex data patterns. One of ANN's technological capabilities is being able to predict solutions based on training patterns provided during the system learning process. This study aims to apply the signature pattern by applying ANN using the Backpropagation method. Backpropagation method is one of the learning algorithms related to the preparation of weights based on the value of errors in learning. The image will be processed using the Backpropagation method which will be obtained by the introduction. The results introduce 50 signature data samples and 50 signature sample data. The test is carried out using 50 samples, where each sample will be requested once. From the results of the research that has been done it can be concluded that the results obtained from the parameters with a learning rate of 0.5, epoch 100, objectives 1e-5 and momentum 0.9 with the results of 68% system testing.

scholarly journals AI Accidents: An Emerging Threat

2021 ◽  
Author(s):  
Zachary Arnold ◽  
◽  
Helen Toner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Systems ◽  
Wide Range ◽  
Policy Suggestions ◽  
The Future ◽  
Modern Machine ◽  
Artificial Intelligence Systems

As modern machine learning systems become more widely used, the potential costs of malfunctions grow. This policy brief describes how trends we already see today—both in newly deployed artificial intelligence systems and in older technologies—show how damaging the AI accidents of the future could be. It describes a wide range of hypothetical but realistic scenarios to illustrate the risks of AI accidents and offers concrete policy suggestions to reduce these risks.

scholarly journals Learning of N-layers neural network

2005 ◽  
Vol 53 (6) ◽  
pp. 75-84 ◽  
Author(s):  
Vladimír Konečný ◽  
Anděla Matiášová ◽  
Ivana Rábová
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Learning Process ◽  
Process Management ◽  
Error Function ◽  
Back Propagation ◽  
Neural Net ◽  
Learning Factor ◽  
The Stability ◽  
Quality Decision

In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE). The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

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