Artificial Intelligence (AI): The field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, perception, problem-solving, and language understanding. AI systems can range from simple algorithms that respond to specific prompts to complex machines that engage in human-like decision-making and interactions.
Big Data: Refers to exceptionally large and complex data sets that traditional data processing software is inadequate to deal with. Big data encompasses a wide variety of data types, including structured, unstructured, and semi-structured data, requiring advanced methods for analysis, storage, and processing.
Unstructured Data: Data that lacks a predefined data model or is not organized in a predefined manner. It includes formats such as text, images, videos, and social media posts. Unstructured data is challenging to process and analyse using conventional database techniques but is rich in information valuable for insights.
Entity Annotation: A technique in natural language processing (NLP) where specific pieces of text are identified and classified by a human into predefined categories, such as names of people, organizations, locations, dates, etc. This process helps in structuring text to facilitate information extraction and understanding.
Entity Extraction: A process in NLP that involves identifying and classifying key elements from text into predefined categories by machine learning algorithms. This helps in transforming unstructured data into a structured format, making it easier for machines to understand, analyse, and utilise the data.
Label: In machine learning, a label is the output or answer for a given input data point. During supervised learning, datasets are composed of input-output pairs, where the output part is referred to as the label, guiding the model during training to learn the mapping from inputs to outputs.
Machine Learning (ML): A subset of AI focusing on algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions. Instead, they rely on patterns and inference derived from data.
Supervised Learning: A machine learning approach where models are trained on labelled data, learning to predict outputs from inputs.
Unsupervised Learning: A method where models infer patterns and relationships directly from unlabelled data, without reference to known or labelled outcomes.
Overfitting: Occurs when a machine learning model learns the details and noise in the training data to the extent that it performs poorly on new data. This is due to the model’s inability to generalize from the training data to unseen data.
Generative AI: Refers to AI models that can generate new content or data that resembles the training material. These models can create text, images, music, and more by learning the patterns and distributions in the data they were trained on.
Prompt: In the context of generative AI, a prompt is an input given to the model to generate specific outputs. The nature and structure of the prompt can significantly influence the quality and relevance of the generated content.
GPT (Generative Pre-trained Transformer): A type of large language model (LLM) designed for a wide range of tasks such as text generation, translation, and question answering. GPT models are trained on vast amounts of text data to generate coherent and contextually relevant text based on input prompts.
Hallucination: In AI, refers to instances where a model generates false or misleading information not supported by its training data. This can occur due to various factors, including insufficient data, overfitting, or errors in the model’s reasoning process.
Bias: In AI, bias refers to systemic errors in the data or algorithm that lead to unfair outcomes, such as privileging one arbitrary group of users over others. Bias can stem from the data collection process, the way the algorithm is designed, or the manner in which data is processed and used.
Machine Meta-Learning (MML): A branch of AI focused on creating algorithms that improve their learning efficiency and adaptability by leveraging previous experiences. MML aims to enable AI systems to learn new tasks faster and with fewer data points by applying learned knowledge from past tasks.
Artificial Neural Network: A computing system inspired by the biological neural networks that constitute animal brains. These networks are composed of layers of interconnected nodes (neurons) that process information by responding to external inputs, forming the basis for learning and memory in machines.
Temperature: In the context of AI, particularly generative models, temperature is a hyperparameter that controls the level of randomness in the generation process. Adjusting the temperature can affect the creativity and unpredictability of the outputs, with higher temperatures leading to more varied results.
Large Language Model (LLM): An advanced AI model trained on extensive text corpora to understand and generate human-like text. LLMs can perform a variety of language tasks, including writing, summarizing, translating, and more, by processing and generating text based on learned patterns
Deep Learning: A subset of machine learning that uses neural networks with many layers (deep neural networks) to analyse various factors of data. Deep learning is particularly powerful for tasks such as image and speech recognition due to its ability to learn complex patterns.
Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve some goals. The agent learns from the outcomes of its actions, rather than from being taught explicitly, optimizing its behaviour to maximize some notion of cumulative reward.
Natural Language Processing (NLP): The branch of AI that focuses on the interaction between computers and humans through natural language. The goal is to enable computers to understand, interpret, and generate human language in a valuable way.
Computer Vision: A field of AI that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, computers can accurately identify and classify objects, and then react to what they “see.”
Explainable AI (XAI): Refers to methods and techniques in the application of AI technology such that the results of the solution can be understood by human experts. It contrasts with the “black box” nature of many AI models, providing transparency into the decision-making process of AI systems.
Federated Learning: A machine learning approach where the model is trained across multiple decentralized devices or servers holding local data samples, without exchanging them. This technique is useful for improving privacy and reducing the amount of data needed to be transferred.
Transfer Learning: A research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.
Adversarial Machine Learning: A technique in machine learning and AI research that studies the construction of attacks on and defences for machine learning models. It includes generating inputs that are designed to confuse AI models (adversarial examples) to test or improve their robustness.
Quantum Computing and AI: The intersection of quantum computing and AI explores how quantum computing can advance machine learning algorithms and AI capabilities. Quantum algorithms have the potential to process complex datasets more efficiently than classical computers, potentially revolutionizing AI research and applications.
This page contains a selection of terms I found relevant, it was generated with the help of ChatGPT4 and Gemini, there are several glossaries with more terms online, for example
https://www.cnet.com/tech/computing/chatgpt-glossary-42-ai-terms-that-everyone-should-know/