Melisa GÖZET, Ulaş FİLİZ, Asım Egemen YILMAZ

568130

Üretken Yapay Zekâ

Bir makine, insan yapımı eserlerden ayırt edilemeyecek, benzersiz içerikler oluşturabilir mi? Bunu, karmaşık gerçek dünya veri örneklerinin yapısını öğrenerek ve aynı yapıya bağlı benzer sentetik örnekler üreterek, üretken çekişmeli ağların (GAN'lar) yardımıyla yapmak mümkün müdür? Üretken modellerin gelişmesindeki ilerlemelere göre, cevap en azından bir dereceye kadar evet gibi görünüyor. Üretken modellerde hedef, mevcut veri setine yakın sentetik örnekler üretmektir; diğer bir deyişle, verilen bir veri setindeki örneklerin türetildiği dağılımı öğrenip bu dağılımdan yeni örnekler üretmektir. Genel olarak veri setleri sınırlı sayıda örnekler içerdiğinden, her zaman veri seti dağılımını tam olarak öğrenmek mümkün olmamaktadır. Bu nedenle gerçek veri dağılımına mümkün olduğunca benzer bir dağılım modellenmeye çalışılmaktadır. Üretilen verilerin kalitesini ölçmek için kapsamlı ve kapsayıcı bir metriğe ihtiyaç duyulmaktadır. Bu çalışmada, GAN’ların değerlendirme puanlarını önemli ölçüde etkileyebilecek gerçek veri kümelerindeki küçük değişikliklerin ve ince farklılıkların etkilerini hafifletmeye yardımcı olabilecek yeni bir yöntemden bahsedilmiş olup çeşitli uygulamalar yapılmıştır. Ayrıca GAN’lar ile ilgili etik ve hukuki problemlere değinilmiştir.
Anahtar Kelimeler:

Üretken Çekişmeli Ağlar, Üretken Çekişmeli Ağların Uygulamaları, Yapay Zekâ, Derin Öğrenme, Etik

Generative Artificial Intelligence

Can a machine attempt to approach the task of creating unique content that would be indistinguishable from human-produced artefacts? Is it possible to do this with the help of generative adversarial networks (GANs) by learning the structure of the complex real-world data examples and generating similar synthetic examples that are bound by the same structure? According to the recent advances in the development of generative models, the answer seems to be yes, at least to an extent. In generative models, the goal is to generate synthetic samples close to the existing dataset; In other words, it is to learn the distribution from which the samples in a given data set are derived and to generate new samples from this distribution. In general, since the datasets contain a limited number of samples, it is not always possible to learn the dataset distribution exactly. For this reason, it is tried to model a distribution as similar as possible to the actual data distribution. A comprehensive and inclusive metric is needed to measure the quality of the data produced. In this study, a new method that can help mitigate the effects of small changes and subtle differences in real datasets that can significantly affect the evaluation scores of GANs is mentioned and various applications are made. In addition, ethical and legal problems related to GANs are mentioned.
Keywords:

Generative Adversarial Networks, GANs Applications, Ethic, Artificial Intelligence, Deep Learning,

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International Journal of Multidisciplinary Studies and Innovative Technologies-Cover
  • ISSN: 2602-4888
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 2017
  • Yayıncı: SET Teknoloji
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