The course "Generative AI" provides an in-depth exploration of generative AI, focusing on both the theory and practical applications of transformers, large language models, and symbolic AI. By completing the course, learners will gain a comprehensive understanding of how these technologies work and how they can be integrated to solve complex problems and generate new content. Through real-world case studies, students will analyze the strengths and weaknesses of generative AI systems, preparing them for the challenges and opportunities they will face in AI leadership roles.
Generative AI
Generative AI
This course is part of AI Strategy and Project Management Specialization
Instructor: Ian McCulloh
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the theory and applications of generative AI, including transformers, large language models, and symbolic reasoning for content creation.
Explore how AI integrates with generative models to improve explainability, control, and responsible AI solutions in real-world applications.
Learn how to manage AI projects at scale, focusing on integrating generative and symbolic AI to address ethical considerations.
Skills you'll gain
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Recently updated!
December 2024
Assessments
6 assignments
Taught in English
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This course is part of the AI Strategy and Project Management Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
This course explores the theory and application of generative AI, focusing on the differences between stochastic AI, expert systems, and symbolic AI. You will learn how symbolic AI can be generative and how both stochastic and symbolic approaches can be integrated. Emphasis is placed on creating holistic, responsible AI solutions. Through practical examples, you will gain a deep understanding of AI's capabilities and ethical considerations.
What's included
1 reading1 plugin
This module explores the fundamentals and applications of Large Language Models (LLMs) and Transformers. It covers the foundations, capabilities, and fine-tuning of LLMs like ChatGPT, as well as their use in image generation. The module also addresses challenges such as hallucinations, vulnerabilities, and model competence, providing a comprehensive understanding of LLMs and their real-world implications.
What's included
9 videos2 readings3 assignments
This module explores the intersection of symbolic and generative AI, focusing on how symbolic AI informs and enhances generative processes. Building on prior knowledge of generative AI, it integrates symbolic reasoning with stochastic models to create responsible AI solutions. Key topics include symbolic AI, formal methods, relational calculus, and data integration, essential for enabling systems to generate insights in diverse environments. The module emphasizes how combining rule-based reasoning with generative AI fosters explainable, transparent systems that align with ethical and regulatory standards.
What's included
13 videos3 readings3 assignments
Instructor
Offered by
Recommended if you're interested in Machine Learning
Johns Hopkins University
Johns Hopkins University
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