Generative AI and Symbolic Reasoning

Generative AI and Symbolic Reasoning

位教师：Ian McCulloh

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3个模块

深入了解一个主题并学习基础知识。

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中级等级

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1 周完成

在 10 小时一周

灵活的计划

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3个模块

深入了解一个主题并学习基础知识。

11 条评论

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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.

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作业

6 项作业

授课语言：英语（English）

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该课程共有3个模块

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.

涵盖的内容

1篇阅读材料1个插件

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.

涵盖的内容

9个视频2篇阅读材料3个作业

9个视频总计106分钟

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

2篇阅读材料总计120分钟

Reading References 60分钟
Reading References 60分钟

3个作业总计90分钟

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

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.