* Define Artificial Intelligence (AI) and Machine Learning (ML) and distinguish their roles in technology.
* Distinguish between discriminative and generative models in AI, identifying their respective functions and applications.
* Describe the training process for generative AI models, including data collection, preparation, and evaluation of the model.
* Discuss real-world applications of generative AI, including its role in painting, music composition, and content creation.
* Outline the development process and working principles of LLMs, comparing them to traditional machine learning models.
* Explain the foundations of Large Language Models (LLMs) and their place within the broader landscape of deep learning.
* Evaluate the capabilities of LLMs in text generation and question answering, emphasising prompt design and prompt engineering.
* Discuss the importance of ethical considerations when utilizing LLMs, including bias mitigation, transparency, and responsible development practices.
Course Content
Introduction to Generative AI and Large Language Models
In this module, you have learned the following points:
Artificial Intelligence (AI): Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think, learn, and problem-solve like humans.
Machine Learning (ML): Machine Learning is a subset of artificial intelligence that focuses on enabling machines to learn from data and improve their performance over time without being explicitly programmed. ML algorithms allow computers to recognise patterns, make predictions, and generate insights from data by identifying underlying structures and relationships.
Discriminative models are like detectives. They analyse existing data to classify or categorise things. Imagine an email spam filter examining your inbox, trying to discern real emails from unwanted spam.
Generative models, on the other hand, are like artists. They don't just analyse; they create entirely new things! Picture a painter using their knowledge of art styles and techniques to produce a brand-new artwork.
GauGAN2 is a powerful generative model capable of creating stunningly realistic and diverse landscapes.
MuseNet is a deep learning model trained on a massive dataset of musical pieces.
GPT-4 is a large language model capable of generating human-quality text in various styles and formats.
Dreamcatcher is an AI-powered design platform that allows users to sketch and refine product ideas in real time.
