AI Glossary

Application Programming Interface (API): A set of rules and protocols that enables different software applications to communicate and interact with each other. It defines the methods and data formats that one software component can use to request and exchange information or perform specific tasks with another, facilitating seamless integration and functionality across various platforms and services.

Artificial Intelligence (AI): A field of computer science focused on creating systems and machines that can perform tasks typically requiring human intelligence, such as problem-solving, learning, reasoning, and decision-making. AI encompasses a range of techniques, including machine learning, natural language processing, and computer vision, to enable computers to mimic cognitive functions and adapt to new information.

Artificial Neural Network (ANN): A computational model inspired by the structure and function of the human brain. It consists of interconnected nodes, or neurons, organized in layers that process and analyze data. ANNs are a fundamental component of machine learning and deep learning, allowing computers to learn from data by adjusting the connections between neurons, enabling tasks like image recognition, language translation, and predictive analytics.

Attention: A mechanism used in deep learning models, particularly in natural language processing, to weigh the importance of different parts of an input sequence when making predictions or generating outputs. Attention mechanisms enable the model to focus on relevant information and have been crucial in improving the performance of various sequence-to-sequence tasks, such as machine translation and text summarization.

Burstiness: A measure of the variation (length, complexity, verbal patterns) amongst sentences within a text passage.

Chain-of-Thought Prompting: A variation of chain prompting where the model is given a series of prompts or questions, and each subsequent prompt is generated based on the previous response from the model. This approach encourages the model to maintain a consistent line of thought throughout the conversation, making it useful for more dynamic and interactive text generation tasks.

Chain Prompting: Involves sequentially providing a series of prompts or questions to a language model, allowing it to generate a coherent narrative or answer by building upon its previous responses. This technique is often used to create longer and contextually connected pieces of text, such as stories or essays.

Chat: A form of communication between individuals or entities through written or spoken messages. In the context of technology and artificial intelligence, “chat” often refers to human-computer interactions, where users communicate with computer systems, bots, or virtual assistants via text or voice messages.

Data Set: A collection of structured or unstructured data that is used for training, testing, or validating machine learning models. Datasets can include various types of information, such as text, images, numerical data, and more, and they are crucial for training and evaluating machine learning algorithms.

Deep Learning: A subset of machine learning that involves neural networks with multiple layers (deep neural networks). These networks are designed to automatically learn and extract hierarchical features and representations from data. Deep learning has been particularly successful in various AI tasks, including image recognition, natural language processing, and speech recognition.

Fine-tuning: The process of taking a pre-trained language model, like GPT-3, and further training it on a specific dataset or task. This helps adapt the model’s knowledge and capabilities to perform better in a particular domain or application, making it more contextually relevant and accurate.

GPT: “Generative Pre-trained Transformer” is a type of deep learning model used in natural language processing tasks. GPT models are trained on massive datasets containing text from the internet, and they are designed to generate human-like text or perform various language-related tasks. These models consist of multiple layers of transformers, which enable them to capture complex patterns and relationships in language data.

Generative Artificial Intelligence: AI systems, particularly deep learning models, that have the ability to generate new content, often in the form of text, images, or other media. These systems can produce human-like outputs by learning patterns and representations from large datasets.

Large Language Models: Advanced machine learning models, typically based on deep learning techniques, that are designed to understand and generate human language text on a massive scale. These models are trained on vast datasets and consist of millions or even billions of parameters, enabling them to perform various natural language processing tasks such as text generation, translation, summarization, and more.

Latency: The time delay between the input or request made to an AI system and the system’s response or output. It is a critical measure of performance, impacting the speed and efficiency of AI applications, particularly in real-time or interactive environments. Lower latency means faster responses, which is essential for tasks requiring immediate feedback, such as autonomous driving, financial trading, or conversational AI.

Machine Learning: A subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computer systems to improve their performance on a specific task through the acquisition and utilization of data, without being explicitly programmed. In essence, it involves teaching machines how to learn from experience and make predictions or decisions based on patterns and insights extracted from large datasets. Machine learning algorithms use statistical techniques to recognize complex patterns and relationships in data, allowing them to make predictions, classify data, or automate decision-making processes.

Natural Language Processing (NLP): A subfield of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human language. It involves the development of algorithms and models that enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

Parameters: The internal variables that the model uses to make predictions or perform tasks. These parameters include the weights and biases of the model’s neurons or units. In the case of deep learning models like GPT, there can be millions or even billions of parameters, and their values are adjusted during training to minimize prediction errors. The quality of the learned parameters directly impacts the model’s performance, with well-tuned parameters leading to more accurate and effective machine learning models.

Perplexity: a quantitative measure of how well a language model can predict the next word in a sequence of words within a given text or dataset. A lower perplexity indicates that the model can make more accurate predictions and has a better understanding of the language, while a higher perplexity suggests less effective prediction and greater uncertainty. It is a useful metric for evaluating the performance and fluency of language models, with lower perplexity values indicating better language understanding and generation capabilities.

Plug-in: A software component or module that adds specific features or functionality to an existing application. Plug-ins are designed to extend the capabilities of software by allowing third-party developers to create and integrate custom code that can enhance or modify the core functionality of an application. They are commonly used in various software environments, such as web browsers, content management systems, and multimedia editing tools.

Prompt: A specific input or instruction provided to a machine learning model to perform a desired task. In the context of natural language processing models, a prompt is a textual or verbal input given to the model to generate a response or perform some language-related task.

Prompt Engineering: Designing and formulating effective prompts or queries to interact with language models or search engines. It aims to create inputs that produce desired outputs from these systems, often involving the careful selection of words, phrasing, and structure to achieve specific results.

Supervised Data Set: A collection of labeled examples used to train machine learning models. In the context of natural language processing, it typically consists of input text paired with corresponding target outputs or labels. These datasets are crucial for teaching models like GPT-3 to perform specific tasks or generate accurate responses by learning from human-provided examples.

Temperature: The level of uncertainty in the model’s predictions. When temperature is high, the model’s output probabilities become more uniform, indicating greater uncertainty and less confident predictions. Conversely, lower temperatures make the model more deterministic, resulting in more confident and sharp predictions. Adjusting the temperature is a technique used to balance exploration and exploitation in reinforcement learning and fine-tune the behavior of machine learning models to suit specific application requirements.

Tokenization: The process of breaking down a piece of text or data into smaller units, typically words or phrases, known as tokens. Tokens are useful for various natural language processing tasks, such as text analysis and language modeling, as they enable software systems to understand and process language more efficiently. Tokenization involves segmenting text based on specific criteria, like spaces or punctuation, and is a fundamental step in many text-related applications, including search engines, machine learning algorithms, and language translation services.

Tokens: Units of text used in natural language processing, typically consisting of words or subword units (e.g., characters or subword pieces in languages like English or Chinese). In the context of language models, text is often broken down into tokens for processing, and the total number of tokens in an input can affect the computational cost and limitations of the model.

Training: The process of fine-tuning a model’s parameters to make it proficient at a specific task. During training, the model learns from a labeled dataset, adjusting its internal parameters (weights and biases) through optimization techniques like gradient descent. This process involves minimizing a loss function that quantifies the difference between the model’s predictions and the ground truth labels, effectively teaching the model to make accurate predictions.

Upskilling: The process of acquiring new skills or improving existing ones to meet the evolving demands of the job market. It involves continuous learning and training to stay relevant in an increasingly technological and dynamic workforce. Upskilling is crucial in the face of automation and digital transformation, as it empowers individuals to adapt to new roles and responsibilities, enhances employability, and helps organizations maintain a skilled workforce that can effectively navigate industry changes.

Zero-shot Prompting: The technique of using a language model, like GPT-3, to generate text or responses for tasks it hasn’t been explicitly trained on. In zero-shot prompting, the model is given a prompt or question and is expected to generate a coherent response without any specific training data for that task.