In the rapidly evolving field of artificial intelligence (AI), understanding key concepts is crucial for grasping the broader landscape and applications, particularly in generative AI. While there are countless terms and nuances within AI, this glossary highlights thirteen fundamental concepts that are essential for anyone engaging with generative AI in an educational setting. These terms have been carefully selected to provide a foundational understanding, aiding students and faculty in navigating the complexities of this technology.
- Artificial Intelligence (AI): The overarching field focused on creating systems that can perform tasks requiring human-like intelligence, such as learning, reasoning, problem-solving, and perception.
- Machine Learning (ML): A subset of AI that involves algorithms that allow computers to learn from data and improve their performance over time without being explicitly programmed.
- Neural Networks: Computational models inspired by the human brain, consisting of layers of interconnected nodes (neurons). Neural networks are the foundation of many ML and deep learning models, enabling complex pattern recognition.
- Deep Learning: A specialized area within ML that uses neural networks with many layers (deep neural networks). It is particularly effective for processing and learning from large datasets, often used in tasks like image and speech recognition.
- Generative AI (sometime seen as GAI or GenAI): A subset of deep learning that focuses on creating new content, such as text, images, or audio. Generative AI models are trained on extensive datasets to generate outputs that resemble real-world examples.
- Natural Language Processing (NLP): A branch of AI and ML that deals with the interaction between computers and human language. NLP involves understanding, interpreting, and generating human language, and is used in applications like language translation, sentiment analysis, and dialogue systems.
- Vector Search: A technique used in AI and NLP to find and compare items based on their features. Each item is represented as a vector, similar to a point in a multi-dimensional space. This method helps identify similar items or content quickly, often using ML models to process the data.
- Chatbots: AI-driven systems that use NLP to simulate human conversation. They can answer questions, provide information, and perform tasks based on user input, commonly used in customer service and educational settings.
- Training Data: The dataset used to train AI models, including those in ML, deep learning, and generative AI. The quality and diversity of training data are critical for developing accurate and unbiased models.
- Prompt Engineering: The practice of designing inputs (prompts) to guide the outputs of generative AI models. This technique is crucial for achieving specific and relevant results, especially in text generation applications.
- Hallucinations: A phenomenon in generative AI and NLP where models produce outputs that are not based on actual data or logical reasoning. These outputs can be incorrect, nonsensical, or entirely fabricated.
- Bias: Refers to systematic errors or prejudices in AI model predictions or outputs, often stemming from the training data or algorithmic design. Addressing bias is essential to ensure fairness and accuracy in AI systems.
- AI Literacy: The knowledge and skills required to understand, use, and critically evaluate AI technologies. AI literacy involves understanding the basics of AI, ML, deep learning, and generative AI, as well as the ethical and societal implications of these technologies.
Please keep in mind that this glossary covers the core terms necessary for a foundational understanding of generative AI. For those interested in delving deeper into AI, machine learning, or related technologies, there are numerous resources available that we will recommend at the end of this course. Exploring these materials can provide more comprehensive insights into the technical, ethical, and societal implications of AI. We encourage you to continue your learning journey beyond this course to fully appreciate the transformative potential of AI technologies.
OpenAI. (2024). ChatGPT (Jul 18 version) [Large language model]. https://chatgpt.com/share/a48cc892-dbab-43d0-bb37-ccdef9f81c71.
