No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
YAPAY ZEKÂNIN KAMUOYU ALGISININ YÖNETİLMESİ NOKTASINDA KULLANILABİLMESİNE DAİR BİR DEĞERLENDİRME
Abstract
Günümüzde kamuoyu algısının oluşturulması ve kontrol edilmesi noktasında yapay zekâ destekli sistemler tarafından üretilen teknolojilerden daha sıklıkla istifade edilmektedir. Bu kapsamda burada bu çalışmada makina öğrenmesi, derin öğrenme gibi yapay zekâ destekli teknikler tarafından üretilen algoritmaların, kamuoyu algısının oluşturulması noktasında kullanılabilirliğine dair bir inceleme gerçekleştirilmiştir. Bu bağlamda ilkin yapay zekâ aracılığıyla üretilen algoritmalara ve yapay zekâ algoritmalarının oluşturulmasında makina öğrenmesi ve derin öğrenme tekniklerinin katkı ve önemine dair bir inceleme yapılmıştır. Müteakiben, üretilen yapay zekâ algoritmaları aracılığıyla algıların manipüle edilebilmesine dair pratik hususlar üzerine kavramsal bazda bazı izahatlar yapılmıştır. Sonrasında ise pratikte kamuoyu algısı oluşturulması açısından yapay zekâ algoritmalarının kullanılması ile alakalı olarak dikkate alınması gereken hususlarla ilgili bir değerlendirme gerçekleştirilmiş ve müteakip araştırmalar için önerilerde bulunulmuştur.
ResearchGate has not been able to resolve any citations for this publication.
The Fourth Industrial Revolution introduced numerous disruptive technologies that are changing human life faster than ever before. Right on its heel, the Fifth Industrial Revolution is introducing robotics using Artificial Intelligence aided by Machine Learning. While these are considered the future, the disruption caused by them is forcing changes in how we operate and perceive things. One such area is the 'military'. Though Artificial Intelligence and robotics are being experimented with as a replacement for humans to de-risk their lives, they are found to be advantageous to reduce the cost of training, rations, supply and maintenance of personnel. They are thus receiving increasing investments for development and deployment. This in return is altering how warfighting is perceived and conducted. To ensure a smooth progression of humans with the numerous technological developments being made in the field of Artificial Intelligence, the article looks at the advances made in these fields for military applications that are improving their sustainability and control, the world over. In addition, to assist decision-makers, a broad-based roadmap for the use of Artificial Intelligence in the military is discussed.
The Social Credit System (SCS) is on the verge of becoming a new regulatory reality. The CCP is changing its governance tools by combining social management and big data. This article provides an overview on the possible impact of the SCS on corporations (CSCS) and analyses its potential extraterritorial effects. The role of transnational law (TL) as a political project is discussed and TL is thus a subject of this paper. Yet, concepts of TL are also used methodologically, as an analytical framework, to make subtle extraterritorial effect and potential legal change visible. The paper places the CSCS in the centre of the analysis and considers technological development, a changing geopolitical landscape, economic interdependence and the behaviour of individuals. The CSCS is an emerging example of the strategic and flexible norm diffusion of powerful states—which underlines the importance of TL as a problem orientated and border-transcending analytical framework.
The severe spread of the COVID-19 pandemic has created a situation of public health emergency and global awareness. In our research, we analyzed the demographical factors affecting the global pandemic spread along with the features that lead to death due to the infection. Modeling results stipulate that the mortality rate increase as the age increase and it is found that most of the death cases belong to the age group 60–80. Cluster-based analysis of age groups is also conducted to analyze the maximum targeted age-groups. An association between positive COVID-19 cases and deceased cases are also presented, with the impact on male and female death cases due to corona. Additionally, we have also presented an artificial intelligence-based statistical approach to predict the survival chances of corona infected people in South Korea with the analysis of the impact on the exploratory factors, including age-groups, gender, temporal evolution, etc. To analyze the coronavirus cases, we applied machine learning with hyperparameters tuning and deep learning models with an autoencoder-based approach for estimating the influence of the disparate features on the spread of the disease and predict the survival possibilities of the quarantined patients in isolation. The model calibrated in the study is based on positive corona infection cases and presents the analysis over different aspects that proven to be impactful to analyze the temporal trends in the current situation along with the exploration of deceased cases due to coronavirus. Analysis delineates key points in the outbreak spreading, indicating that the models driven by machine intelligence and deep learning can be effective in providing a quantitative view of the epidemical outbreak.
Interest in applying sociological tools to analysing the social nature, antecedents and consequences of artificial intelligence (AI) has been rekindled in recent years. However, for researchers new to this field of enquiry, navigating the expansive literature can be challenging. This paper presents a practical way to help these researchers to think about, search and read the literature more effectively. It divides the literature into three categories. Research in each category is informed by one analytic perspective and analyses one “type” of AI. Research informed by the “scientific AI” perspective analyses “AI” as a science or scientific research field. Research underlain by the “technical AI” perspective studies “AI” as a meta‐technology and analyses its various applications and subtechnologies. Research informed by the “cultural AI” perspective views AI development as a social phenomenon and examines its interactions with the wider social, cultural, economic and political conditions in which it develops and by which it is shaped. These analytic perspectives reflect the evolution of “AI” from chiefly a scientific research subject during the twentieth century to a widely commercialised innovation in recent decades and increasingly to a distinctive socio‐cultural phenomenon today.
Statistics draws population inferences from a sample and machine learning finds generalizable predictive patterns.
Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision-making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI, and examines how each can bring their own strength in organizational decision making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans' cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision-making. This premise mirrors the idea of 'intelligence augmentation': AI systems should be designed with the intention of augmenting, not replacing, human contributions.
The paper describes modeling of cognition-emotion interaction implemented in a neural network named Crossbar Adaptive Array in 1981. The architecture was proposed to meet two challenges: solving the delayed reinforcement learning problem for neural networks, and building a self-learning system (no advice and no reinforcement from environment) based on a neural network. The architecture introduced computation of feelings, and their interaction with learning and decision making mechanisms. It also introduced genetic environment as provider of initial emotions to the neural network. Receiving initial emotions from the genetic environment, the architecture learns using the proposed emotion backpropagation mechanism. Some developments after 1981 are also discussed.
Recent years have seen an increase in the
popularity of neural network (NN) research. The mammalian
brain, which consists of billions of interconnected neurons, is
renowned for its ability to perform computationally hard tasks,
such as face recognition, body motion planning, and muscle
activity control. In an effort to emulate the effectiveness of
biological neural networks in learning, artificial neural
networks (ANNs) were developed. The NN technique has been
the topic of many studies over the last few decades, with
applications in many fields including control engineering,
automation, aerospace, psychology, economics, healthcare, and
energy science. The objective of the discipline of machine
learning is to create computers that can independently learn and
improve. In this chapter, we have attempted to depict the types
of neural networks and machine learning as well as their
applications in different industrial disciplines such as science,
commerce, and medicine.
TRT Akademi Dergisinin Eylül 2021'de yayınlanan "Yapay Zekâ" temalı 13. sayısı için kaleme alınmış editör yazısıdır.
Sürdürülebilir rekabet avantajı, işletmelerin sahip oldukları kaynaklara bağlıdır ve bu yüzden kaynaklarla ilgili alınacak kararlar önem arz etmektedir. Bu kaynakların etkin ve verimli kullanılabilmesi de eş zamanlı bilgiye sahip olmayı gerektirmektedir. Yapay zeka uygulamaları eş zamanlı bilgiye erişme problemini ortadan kaldırmakta ve karar verme sürecini hızlandırmaktadır. Bu yüzden işletmelerin karar verici konumunda bulunanlar, geliştirilen yapay zeka uygulamaları sayesinde iş yapış şekillerinde ve kullanılan geleneksel bilgi sistemleri uygulamalarında değişimlere gitmek durumundadırlar. Bu araştırmanın amacı, işletmelerin birbirleriyle olan rekabetlerinde yapay zeka uygulamalarının karar verme sürecinde stratejik bir önem taşıdığını ve yapay zeka uygulamalarının karar verici konumunda bulunan insan faktörü olmadan da var olup olamayacağını tartışmaktır. Bu çerçevede yapılan incelemeler sonucunda, gelecekte yapay zeka uygulamaları ve teknolojilerinin daha da gelişeceği ve işletmelerin karar verme süreçlerinde kullanımının önemli ölçüde artacağı öngörülmektedir.
Over the coming decades, Artificial Intelligence will profoundly impact the way we live, work, wage war, play, seek a mate, educate our young, and care for our elderly. It is likely to greatly increase our aggregate wealth, but it will also upend our labor markets, reshuffle our social order, and strain our private and public institutions. Eventually it may alter how we see our place in the universe, as machines pursue goals independent of their creators and outperform us in domains previously believed to be the sole dominion of humans. Whether we regard them as conscious or unwitting, revere them as a new form of life or dismiss them as mere clever appliances, is beside the point. They are likely to play an increasingly critical and intimate role in many aspects of our lives. The emergence of systems capable of independent reasoning and action raises serious questions about just whose interests they are permitted to serve, and what limits our society should place on their creation and use. Deep ethical questions that have bedeviled philosophers for ages will suddenly arrive on the steps of our courthouses. Can a machine be held accountable for its actions? Should intelligent systems enjoy independent rights and responsibilities, or are they simple property? Who should be held responsible when a self-driving car kills a pedestrian? Can your personal robot hold your place in line, or be compelled to testify against you? If it turns out to be possible to upload your mind into a machine, is that still you? The answers may surprise you.
Algorithms are the hidden methods that computers apply to process information and make decisions. The book tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and events behind the genesis of the world’s most important algorithms. Along the way, it explains, with the aid of examples and illustrations, how the most influential algorithms work. The first algorithms were invented in Mesopotamia 4,000 years ago. The ancient Greeks refined the concept, creating algorithms for finding prime numbers and enumerating Pi. Al-Khawrzmi’s 9th century books on algorithms ultimately became their conduit to the West. The invention of the electronic computer during World War II transformed the importance of the algorithm. The first computer algorithms were for military applications. In peacetime, researchers turned to grander challenges - forecasting the weather, route navigation, choosing marriage partners, and creating artificial intelligences. The success of the Internet in the 70s depended on algorithms for transporting data and correcting errors. A clever algorithm for ranking websites was the spark that ignited Google. Recommender algorithms boosted sales at Amazon and Netflix, while the EdgeRank algorithm drove Facebook’s NewsFeed. In the 21st century, an algorithm that mimics the operation of the human brain was revisited with the latest computer technology. Suddenly, algorithms attained human-level accuracy in object and speech recognition. An algloirthm defeated the world champion at Go - the most complex of board games. Today, algorithms for cryptocurrencies and quantum computing look set to change the world.
Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer engineering. The modern field of AI began at a small summer workshop at Dartmouth College in 1956. Since then, AI applications made possible by machine learning (ML), an AI subdiscipline, include Internet searches, e‐commerce sites, goods and services recommender systems, image and speech recognition, sensor technologies, robotic devices, and cognitive decision support systems (DSSs). As more applications are integrated into everyday life, AI is predicted to have a globally transformative influence on economic and social structures similar to the effect that other general‐purpose technologies, such as steam engines, railroads, electricity, electronics, and the Internet, have had. Novel AI applications in the workplace of the future raise important issues for occupational safety and health. This commentary reviews the origins of AI, use of ML methods, and emerging AI applications embedded in physical objects like sensor technologies, robotic devices, or operationalized in intelligent DSSs. Selected implications on the future of work arising from the use of AI applications, including job displacement from automation and management of human‐machine interactions, are also reviewed. Engaging in strategic foresight about AI workplace applications will shift occupational research and practice from a reactive posture to a proactive one. Understanding the possibilities and challenges of AI for the future of work will help mitigate the unfavorable effects of AI on worker safety, health, and well‐being.
This article contains the first detailed historical study of one of the new high-frequency trading (HFT) firms that have transformed many of the world’s financial markets. The study, of Automated Trading Desk (ATD), one of the earliest and most important such firms, focuses on how ATD’s algorithms predicted share price changes. The article argues that political-economic struggles are integral to the existence of some of the ‘pockets’ of predictable structure in the otherwise random movements of prices, to the availability of the data that allow algorithms to identify these pockets, and to the capacity of algorithms to use these predictions to trade profitably. The article also examines the role of HFT algorithms such as ATD’s in the epochal, fiercely contested shift in US share trading from ‘fixed-role’ markets towards ‘all-to-all’ markets.
In recent years, deep neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical
survey compactly summarises relevant work, much of it from the previous
millennium. Shallow and deep learners are distinguished by the depth of their
credit assignment paths, which are chains of possibly learnable, causal links
between actions and effects. I review deep supervised learning (also
recapitulating the history of backpropagation), unsupervised learning,
reinforcement learning & evolutionary computation, and indirect search for
short programs encoding deep and large networks.
After the setbacks suffered in the 1970s as a result of the ‘Lighthill Report’ (Lighthill, 1973), the science of Artificial Intelligence (AI) has undergone a dramatic revival of fortunes in the 1980s. But despite the obvious enormity and complexity of the problems tackled by AI, it still remains rather parochial in relation to the import of alternative though potentially fruitful ideas from other disciplines. With this in mind, the aim of the present paper is to utilise ideas from the sociology of science in order to explore some current issues in AI and, in particular, the branch of expert systems.
It is argued that the sociology of sciences shares a common focus of enquiry along with AI — namely, the nature of knowledge — and has a role to play in the understanding, design and future development of expert systems.
Developers of tomorrow's command and control centers are facing numerous problems related to the vast amount of available information obtained from various sources. On a lower level, huge amounts of uncertain reports from different sensors need to be fused into comprehensible information. On a higher level, representation and management of the aggregated information will be the main task, with prediction of future course of events being the uttermost goal. Unfortunately, traditional agent modeling techniques do not capture situations where commanders make decisions based on other commanders' reasoning about one's own reasoning. To cope with this problem, we propose a decision support tool for command and control situation awareness enhancements based on game theory for inference and coupled with traditional AI methods for uncertainty modeling.
Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
2 Şubat). Algoritma Nedir
- K Enes
Enes, K. (2017, 2 Şubat). Algoritma Nedir?. https://www.eneskamis.com/%EF%BB%BF
algoritma-nedir-algoritma-ne-ise-yarar (Erişim: 11.10.2023).
Bulanık Çok Kriterli Karar Verme Yöntemleri ve Uygulama, Yayımlanmamış Yüksek Lisans Tezi
- N Karakaşoğlu
Karakaşoğlu, N. (2008). Bulanık Çok Kriterli Karar Verme Yöntemleri ve Uygulama,
Yayımlanmamış Yüksek Lisans Tezi, Pamukkale Üniversitesi-Sosyal Bilimler
Enstitüsü.
The Master Algorithm: How the Quest for The Ultimate Learning Machine Will Remake Our World
- D Pedro
Pedro, D. (2015). The Master Algorithm: How the Quest for The Ultimate Learning Machine
Will Remake Our World, New York: Basic Books/Hachette Book Group.
Yapay Zekâ Algoritmalarının Dijital Enstalasyona Dönüşmesi
- A Toprak
Toprak, A. (2020). Yapay Zekâ Algoritmalarının Dijital Enstalasyona Dönüşmesi, Ege
Üniversitesi İletişim Fakültesi Yeni Düşünceler Hakemli E-Dergisi, (14), 47-59.
Karar Ağaçları (Makine Öğrenmesi Serisi-3
- M F Akça
Computer Science Handbook, Second Edition (Section VII: Intelligent Systems)
- M I Jordan
- C M Bishop
Jordan, M. I., Bishop, C. M. (2004). Neural Networks. In Allen. B. Tucker (Ed.). Computer
Science Handbook, Second Edition (Section VII: Intelligent Systems). Boca Raton,
Florida: Chapman & Hall/CRC Press LLC.
Information: A Historical Companion
- A Blair
- P Duguid
- A S Goeing
Blair, A., Duguid, P., Goeing, A. S. vd. (2021). Information: A Historical Companion,
Princeton: Princeton University Press.
Disinformation Reimagined: How AI Could Erode Democracy in the 2024 US Elections
- N R Early
Early, N. R. (2023, 19 Temmuz). Disinformation Reimagined: How AI Could Erode
Democracy in the 2024 US Elections. https://www.theguardian.com/usnews/2023/jul/19/ai-generated-disinformation-us-elections?ref=upstract.com (Erişim:
11.10.2023).
How Emotions Are Made: The Secret Life of the Brain
- B L Feldman
Feldman, B. L. (2016). How Emotions Are Made: The Secret Life of the Brain, Boston:
Houghton Mifflin Harcourt.
Yapay Zekânın Teorik Temelleri, Yayımlanmamış Yüksek Lisans Tezi
- E Kayaönü
Kayaönü, E. (2000). Yapay Zekânın Teorik Temelleri, Yayımlanmamış Yüksek Lisans Tezi,
İstanbul Teknik Üniversitesi-Fen Bilimleri Enstitüsü.
Probabilistic Machine Learning: An Introduction
- K P Murphy
Murphy, K. P. (2021). Probabilistic Machine Learning: An Introduction, Massachusetts: MIT
Press.
How AI will transform the 2024 elections
- D M West
West, D. M. (2023, 3 May). How AI will transform the 2024 elections.
https://www.brookings.edu/articles/how-ai-will-transform-the-2024-elections/ (Erişim:
11.10.2023).
- M R Minar
- J Naher
Minar, M. R., Naher, J. (2018). Recent Advances in Deep Learning: An Overview, ArXiv,
abs/1807.08169.
- J Schmidhuber
Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview, Neural Networks,
61, 85-117.
KNN (K Nearest Neighborhood
- Ş E Şeker
Şeker, Ş. E. (2008, 17 Kasım). KNN (K Nearest Neighborhood, En Yakın K Komşu).
https://bilgisayarkavramlari.com/2008/11/17/knn-k-nearest-neighborhood-en-yakin-kkomsu/ (Erişim: 11.10.2023).