Yanlılık kaynakları

Bir teknoloji şirketi, şirketlere işe alım konusunda yardımcı olmak için bir üretken yapay zeka modeli geliştirdi. Yapay zeka, iş tanımları oluşturup tanıtıyor, özgeçmişleri inceleyip sıralıyor ve her başvuru sahibi için kişiselleştirilmiş mülakat soruları oluşturuyor.

Aşağıdakilerden hangileri bu senaryodaki potansiyel yanlılık endişeleridir?

Bu egzersiz

Üretken Yapay Zeka Kavramları

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

Üretken Yapay Zeka Kavramları

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Familiarize yourself with the concept of generative AI and its ability to create content is introduced, along with its real-world applications and limitations. You'll delve into the differences between traditional machine learning models, generative AI, and artificial general intelligence (AGI), and explore the key factors driving the development of generative AI.

Exercise 1: What is generative AI?Exercise 2: Getting to know generative AI Exercise 3: Working with a generative AI model Exercise 4: Applications of generative AI Exercise 5: Generative AI in the machine learning landscape Exercise 6: Picking the right tool for the job Exercise 7: Differentiating generative AI from traditional ML Exercise 8: More dogs and GANs Exercise 9: The evolution of generative AI Exercise 10: Driving the field forward Exercise 11: Transformers Exercise 12: Generative AI breakthroughs

In this chapter, we cover the essential steps in creating generative AI models: research and design, data collection, model training, and evaluation. We examine the significance of diverse datasets and advanced training techniques, as well as various evaluation methods, while discussing their strengths and limitations.

Exercise 1: Model design and data collection Exercise 2: Research and design Exercise 3: A dataset windfall?Exercise 4: Model training Exercise 5: Factors impacting model training Exercise 6: Techniques for developing specialized models Exercise 7: Model evaluation Exercise 8: PixAIr animation Exercise 9: Helping Intellibrand evaluate

This chapter focuses on the responsible use of generative AI. We discuss the challenges and strategies to mitigate social bias, intellectual property and privacy issues, and ethical considerations to prevent misuse. We conclude by exploring the immense potential and risks of Artificial Generative Intelligence (AGI), along with the approaches to control its outcomes.

Exercise 1: Toplumsal önyargıları değerlendirme ve azaltma Exercise 2: Yanlılık kaynakları

Geçerli Egzersiz

Exercise 3: Yanlılığın tespit edilmesi ve azaltılması Exercise 4: Telif hakkı ve mülkiyet Exercise 5: Hukuk ve gizlilikle ilgili hususlar Exercise 6: IP sahipliği iddialarının belirlenmesi Exercise 7: Sorumlu üretken yapay zeka uygulamaları Exercise 8: Kullanım ilkeleri Exercise 9: Sorumlu yapay zeka uygulamalarının hayata geçirilmesi Exercise 10: Yapay genel zeka (AGI)Exercise 11: AGI özellikleri Exercise 12: AGI sonuçları

Chapter 4 examines the potential, impact, and integration of generative AI into human workflows. It discusses key contributors to AI development, from universities to companies, and explores societal adaptations to AI. It delves into AI's implications for productivity, job dynamics, education, media, entertainment, scientific advancements, and ethical considerations.

Exercise 1: Bringing new AI into old workflows Exercise 2: Leveraging generative AI for data science Exercise 3: Designing a new marketing campaign Exercise 4: Progress in generative AI Exercise 5: To open source or not to open source Exercise 6: Super Duper GANs Exercise 7: Preparing for a future of generative AI Exercise 8: Generative AI in education Exercise 9: The value of skills in an AI world Exercise 10: You made it!