Generative AI and Symbolic Reasoning

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Generative AI and Symbolic Reasoning

Instructor: Ian McCulloh

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3 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 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

Tools you'll learn

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Assessments

6 assignments

Taught in English

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There are 3 modules in this course

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.

What sets this course apart is its focus on the intersection of symbolic AI and generative processes, providing insights into how these models can be enhanced for explainability and control. By examining both stochastic and symbolic AI, learners will understand how these approaches complement each other in creating responsible, ethical, and sustainable AI systems. Whether you're looking to lead AI projects, integrate cutting-edge AI tools, or understand their broader implications, this course equips you with the skills needed to navigate the evolving landscape of generative AI.

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

9 videos Total 106 minutes

Intro to Transformers and Large Language Models 9 minutes
Foundation Models 16 minutes
Large Language Models 12 minutes
Why Does Chat GPT Look Smart? 6 minutes
Specialized LLMs and Fine-Tuning 13 minutes
Image Generation 13 minutes
Image Generation Methods 5 minutes
Competence and Hallucination 17 minutes
LLM Vulnerabilities 14 minutes

2 readings Total 120 minutes

Reading References 60 minutes
Reading References 60 minutes

3 assignments Total 90 minutes

Understanding Transformers: Foundations and Capabilities of Large Language Models 15 minutes
Exploring Applications: Image Generation and Challenges in Large Language Models 15 minutes
Transformers and Large Language Models 60 minutes

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

13 videos Total 148 minutes

Symbolic Generative AI 9 minutes
Business Applications 12 minutes
Limitations 12 minutes
Formal Methods Part 1 16 minutes
Formal Methods Part 2 5 minutes
Relational Calculus 12 minutes
Relational Calculus vs Algebra 4 minutes
How Relational Calculus Works 7 minutes
Query Optimizer 12 minutes
A Chase Algorithm 18 minutes
Query Optimizer Illustrative Example 7 minutes
Data Integration 23 minutes
GPT Assignment 11 minutes

3 readings Total 160 minutes

Reading References 60 minutes
Reading References 60 minutes
Self-Reflective Reading: Building a Custom GPT to Address Strategic Business Challenges 40 minutes

3 assignments Total 90 minutes

Exploring Symbolic Generative AI and Formal Methods in Business 15 minutes
Relational Calculus, Query Optimization, and Data Integration Techniques 15 minutes
Symbolic Generative AI 60 minutes

Instructor

Ian McCulloh

Johns Hopkins University

17 Courses 25,551 learners

Offered by

Johns Hopkins University

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Reviewed on Mar 23, 2025

The instructor made complex ideas easy to understand through very relevant and modern examples and business applications.

Reviewed on Jan 4, 2025

This course helped me to understand power and good use of Generative AI.

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