Introduction to Generative AI

Introduction to AI & Deep Learning

Generative AI does not exist in isolation and has evolved from decades of work on AI & the machines that power modern systems. This part of the course introduces the student to the field of artificial intelligence & machine learning, the systems that power AI like GPUs, and discusses how these can be used to power and be empowered by generative AI technologies.

Natural Language Processing (NLP), Large Language Models (LLMs), Large Multimodal Models (LMMs), and Image Generators

Understand the foundations of large language models, large multimodal models, and image generators. Learn about the principles guiding their development and the alignment techniques designed to ensure their responsible use.

Deploying LLMs

Fine-tuning, Prompting, Chain-of-Thought, and Multi-agent Techniques: Explore how to deploy Large Language Models effectively. Through fine-tuning, creating precise prompts, employing chain-of-thought reasoning, and constructing teams of models that work together, learn to harness the full potential of LLMs for various tasks and to evaluate their performance.

Applications of Generative AI Across Sectors

Examine how generative AI is revolutionizing industries, from chatbots and reporting to design and reasoning in legal, healthcare, education, engineering, and business endeavors, as well as fueling creativity in art and literature.

Evaluating Trustworthiness in AI Outputs

As the world increasingly relies on AI, assessing the trustworthiness of its outputs becomes crucial. This section focuses on methodologies and considerations for evaluating the reliability, robustness, and explainability, of AI-generated content.

Bias and Fairness in AI Systems

Analyze the concepts of bias and fairness in AI, learn about their challenges and the techniques that can be used to mitigate their effects. Understand how AI can be designed to serve all users equitably.

Privacy, Copyright, and Accountability

Learn how to balance innovation with the need to protect individuals' privacy and data creator’s intellectual property and copyrights. Understand the extent to which AI developers and deployers are accountable for their systems and the ethical implications of autonomous systems making decisions that affect human lives.