Fonti di distorsione

Un'azienda tecnologica ha creato un modello di intelligenza artificiale generativa per aiutare le aziende a reclutare personale. L'intelligenza artificiale crea e promuove le descrizioni dei lavori, controlla e classifica i curriculum e prepara domande personalizzate per il colloquio per ogni candidato.

Quali dei seguenti elementi potrebbero essere fonte di pregiudizi in questo scenario?

Questo esercizio fa parte del corso

Concetti di IA generativa

Visualizza il corso

Esercizio pratico interattivo

Passa dalla teoria alla pratica con uno dei nostri esercizi interattivi

Inizia esercizio

Questo esercizio fa parte del corso

Concetti di IA generativa

InicianteNível de habilidade

4.8+

Inizia il corso gratis

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: Valutare e ridurre i pregiudizi sociali Exercise 2: Fonti di distorsione

Esercizio in corso

Exercise 3: Individuare e ridurre i pregiudizi Exercise 4: Copyright e proprietà Exercise 5: Cose legali e sulla privacy Exercise 6: Identificare le rivendicazioni di proprietà intellettuale Exercise 7: Applicazioni di IA generativa responsabili Exercise 8: Principi di utilizzo Exercise 9: Mettere in atto pratiche responsabili nell'uso dell'intelligenza artificiale Exercise 10: Intelligenza artificiale generale (AGI)Exercise 11: Caratteristiche dell'AGI Exercise 12: Risultati AGI

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!