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In this course, you will:
- Understand the notion of generative AI and its potential to create content, as well as its real-world uses and limitations.
- Examine the distinctions between classic machine learning models, generative AI, and artificial general intelligence (AGI), as well as the major causes driving generative AI research.
- Examine the critical phases in developing generative AI models, including research and design, data collecting, model training, and evaluation.
- We investigate the significance of various datasets, sophisticated training approaches, and evaluation methods, as well as their strengths and limits.
- The emphasis is on the responsible application of generative AI. We cover the obstacles and techniques for reducing social bias, as well as intellectual property and privacy concerns, as well as ethical considerations for preventing misuse.
- We finish by delving into the enormous possibilities and risks of Artificial Generative Intelligence (AGI), as well as ways for controlling its consequences.
- Discusses significant contributors to AI development, ranging from academia to corporations, and investigates societal AI adaptations.
- It goes at the consequences of artificial intelligence for productivity, employment dynamics, education, media, entertainment, scientific developments, and ethical concerns.
Syllabus:
- Introduction to Generative AI
- Developing Generative AI Models
- Using AI Models and Generated Content Responsibly
- Getting Ready for the Age of Generative AI
