Artificial Intelligence (AI)

AI is the field of computer science focused on creating machines and systems that can perform tasks typically requiring human intelligence. These tasks can range from problem-solving and decision-making to recognizing patterns and processing natural language. AI relies algorithms and computational models to simulate intelligent behavior.

Machine Learning (ML)

Machine Learning (ML) is a subset of AI that enables computers to learn from data without being explicitly programmed. ML algorithms analyze vast amounts of data to identify patterns and improve their performance over time. Examples of ML in everyday life include systems that detect spam in emails or recommend videos based on your previous viewing history. The more data these systems process, the better they become at making predictions and decisions.

Large Language Models (LLMs)

Large Language Models (LLMs) are a specialized type of AI designed to understand and generate human language. Using deep learning techniques, LLMs are trained on massive datasets containing billions or trillions of words. These models can generate coherent text, perform tasks such as translation and summarization, and engage in conversation on a wide range of topics, making them adaptbale in language-related applications.

Generative Pre-trained Transformers (GPTs)

Generative Pre-trained Transformers (GPTs) are a type of LLM designed specifically to create human-like text and engage in conversational tasks. GPT models can produce responses to questions, generate creative content such as stories or music, and assist with various tasks like answering queries in a natural language format. These models are trained on extensive datasets, allowing them to predict and generate language that appears highly sophisticated and human-like.

Generative AI, short for Generative Artificial Intelligence, refers to a subset of AI models that are capable of creating new content, such as text, images, music, or even code, based on patterns learned from existing data. These models, like GPT-4, use deep learning techniques to analyze vast amounts of data and generate outputs that are coherent and contextually relevant. For instance, generative AI can be used in educational settings to draft essays, create visual content for presentations, or simulate complex scenarios for research purposes. However, it is crucial to approach generative AI with an understanding of its limitations, such as the potential for bias in the generated content and the importance of ethical considerations in its application.

Retrieval-Augmented Generation (RAG) is a more advanced application of AI that combines the strengths of generative AI with the precision of information retrieval systems. RAG models work by first retrieving relevant information from a large dataset or knowledge base and then using that information to generate more accurate and contextually appropriate responses. This approach is particularly valuable in academic settings where precision and factual accuracy are paramount. For example, a RAG model can be used to assist in literature reviews by not only retrieving relevant academic papers but also summarizing key findings or generating insightful commentary based on the retrieved data. This hybrid model enhances the reliability of AI-generated content, making it a powerful tool for research and educational purposes.

For a research consultation, please contact Kevin Gunn or Charles Gallagher.

LinkedIn Learning has a number of courses devoted to various topics in Generative AI. Start with What is Generative AI?