Glosssary

Definition: A field of study that focuses on imitating human intelligence in machines, so that they can perform tasks like decision-making, learning, and problem-solving.
Why it's important: This is the field that encompasses all other terms related to intelligent behavior in machines.

Definition: In AI, a model is a specific program that has been trained to recognize patterns and make predictions or decisions based on data. It is a mathematical representation of a real-life process, which runs in a computer and is programmed to perform tasks like learning from data, classifying images, interpreting language, or making recommendations.
Why it's important: The model is the core of an AI system, determining how it processes input and generates output. A well-trained model can make accurate predictions and improve with more data.

Definition: Training is the process of teaching a model to recognize patterns or make decisions by feeding it data and adjusting its parameters based on its performance. Through training, the model “learns” from examples in the data, improving its accuracy for specific tasks.
Why it's important: Training is essential because it determines how well an AI model performs. The quality and quantity of training data directly affect the model’s ability to make accurate predictions or complete tasks effectively.

Definition: The data or information that is fed into an AI model for processing. This could be in various forms like text, images, audio, or numerical data.
Why it's important: Input data is crucial as it influences how the AI system learns, makes decisions, or generates results. High-quality and relevant input leads to more accurate and reliable AI outputs.

Definition: A prompt is a specific type of input that is not part of the training data but is rather an instruction from the user to generate the desired output. In LLMs, a prompt is typically a written question or instruction that helps the AI system understand what type of response is expected.
Why it's important: The quality and clarity of a prompt significantly impact the AI’s output. Well-structured prompts lead to more accurate, relevant, and coherent responses from AI systems.

Definition: The result generated by an AI model after processing the input data. Outputs can range from predictions, classifications, generated text, images, or actions taken by an AI system.
Why it's important: The output is the final product of an AI system’s operation. It determines the success and utility of the AI in solving specific problems or completing tasks.

Definition: A type of AI that can create new content, such as text, images, or music, based on input data upon a given prompt.
Why it's important: Generative AI can create human-like responses, art, and more. Some examples are GPT-4, Gemini, Midjourney and DALL·E.

Definition: A Large Language Model is an AI model designed to understand and generate human-like text based on vast amounts of language data. LLMs are trained on billions of words from books, websites, and other texts, allowing them to perform tasks such as answering questions, writing, and translating languages.
Why it's important: LLMs enable technologies like chatbots, language translation, and voice assistants like Siri or Alexa. Their ability to process and generate natural language makes them valuable in various fields, from customer service to research.

Definition: A field of AI that studies how models learn from data to improve their performance on tasks, without being explicitly programmed for every outcome.
Why it's important: ML is the driving force behind many modern AI applications like recommendation systems, search engines, and voice assistants.

Definition: Neural networks are a series of algorithms inspired by the structure of the human brain. They consist of layers of interconnected nodes, or “neurons,” that process data in ways that allow an AI model to recognize patterns and make decisions. Each layer processes the data it receives, passing it to the next layer until a final output is produced.
Why it's important: Neural networks are the foundation of deep learning and are essential for complex AI tasks, such as image recognition, natural language understanding, and decision-making. Their ability to learn from large datasets makes them powerful tools for modern AI applications.

Definition: A type of machine learning that uses neural networks with many layers to analyze complex patterns in large datasets.
Why it's important: It powers some of the most advanced AI systems, such as image recognition and large language models (LLM).

Definition: A technique of machine learning where the model is trained on labeled data (i.e., data which is previously categorized so the model has guidance on how to genarate the desired outputs).
Why it's important: This is one of the most common learning paradigms in machine learning, used for tasks like spam detection and image classification.

Definition: A technique of machine learning where the model learns from unlabeled data (data which has not been previously categorized), finding hidden patterns without explicit guidance. See also: supervised learning.
Why it's important: This is used for applications where patterns aren’t easily known beforehand. Examples of these applications are: anomaly detection, clustering of large amounts of data, and pattern recognition.

Definition: The study and application of ethical principles to ensure AI models and systems are fair, transparent, and do not cause harm.
Why it's important: With AI's growing influence, addressing concerns around bias, privacy, and job displacement is crucial to responsible AI development.