Durian runtuh himpunan data?

Holodeck Inc. sedang mengembangkan ruang bertenaga generative AI yang menerima prompt audio dan merespons dengan lingkungan virtual 3D. Misalnya, pengguna menyebutkan sebuah prompt dan model merespons dengan membuat ruangan tampak seperti lingkungan yang dideskripsikan.

Holodeck Inc. telah memperoleh himpunan data masif berupa rekaman video mentah dari operator drone otonom. Himpunan ini terdiri dari beberapa petabita (jutaan gigabita) rekaman dari penerbangan pada ketinggian rendah di area yang jarang hingga padat penduduk. Data telah dienkripsi sepenuhnya dan disimpan di server privat Holodeck Inc. dengan akses yang dipantau dan dikendalikan.

Manakah dari hal berikut yang kemungkinan menjadi masalah pada himpunan data ini?

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Konsep Generative AI

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Latihan ini adalah bagian dari kursus

Konsep Generative AI

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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: Perancangan model dan pengumpulan data Exercise 2: Riset dan perancangan Exercise 3: Durian runtuh himpunan data?

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Exercise 4: Pelatihan model Exercise 5: Faktor yang memengaruhi pelatihan model Exercise 6: Teknik untuk mengembangkan model khusus Exercise 7: Evaluasi model Exercise 8: Animasi PixAIr Exercise 9: Membantu Intellibrand melakukan evaluasi

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: Evaluating and mitigating social bias Exercise 2: Sources of bias Exercise 3: Detecting and mitigating bias Exercise 4: Copyright and ownership Exercise 5: Legal and privacy considerations Exercise 6: Identifying IP ownership claims Exercise 7: Responsible generative AI applications Exercise 8: Usage principles Exercise 9: Implementing responsible AI practices Exercise 10: Artificial general intelligence (AGI)Exercise 11: AGI characteristics Exercise 12: AGI outcomes

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!