This is the overarching term for any system that mimics human intelligence. This can include anything from speech recognition and decision-making to visual perception and language translation.

This term refers to the field of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human language in a valuable way.

This is a type of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

This is a subset of machine learning that’s based on artificial neural networks with representation learning. Deep learning models can achieve state-of-the-art accuracy, often exceeding human-level performance in certain tasks.

This is an autoregressive language prediction model that uses deep learning to produce human-like text. GPT is the model upon which ChatGPT is based.

An AI program developed by OpenAI. It uses the GPT model to generate human-like text based on the prompts it’s given.

This is a model architecture introduced in “Attention is All You Need” that uses self-attention mechanisms and has been used in models like GPT.

This term refers to a statistical analysis model that uses time-lagged values as input variables. ChatGPT uses this approach to predict the next word in a sentence.

In the context of ChatGPT, a prompt is an input given to the model, to which it responds

A piece of a whole, so a word is a token in a sentence, and a sentence is a token in a paragraph. Tokens are the building blocks of Natural Language Processing.

This is a process that follows the initial training phase, where the model is tuned or adapted to specific tasks, such as question answering or language translation.

In ChatGPT, this is the amount of recent conversation history that the model can utilize to generate a response.

This refers to the model’s ability to understand a task and generate appropriate responses without having seen such examples during training.

This is the model’s ability to comprehend a task from just a single example during training.

This is the model’s ability to understand a task after being provided a small number of examples during training.

This is a technique used in deep learning models, where the model assigns different weights or “attention” to different words or features when processing data.

This is a fine-tuning method used in ChatGPT, where models learn from feedback provided by humans.

This is the first step in fine-tuning, where human AI trainers provide conversations with both the user and AI role to the model.

These are models used to rank different responses from the output of the Large Language Model (LLM) e.g. ChatGPT.

This allows for the interaction between different software programs. OpenAI provides an API for developers to integrate ChatGPT into their applications or services.

Humans who guide the AI model during the fine-tuning process by providing it with feedback, ranking responses, and writing example dialogs.

These are steps taken to ensure that the AI behaves in a way that is safe, ethical, and respects user privacy.

The artificial intelligence lab that developed GPT-3 and ChatGPT. OpenAI aims to ensure that artificial general intelligence (AGI) benefits all of humanity.

In the context of AI, this refers to the observed trend that AI models tend to improve in performance as they’re given more data, more computation, and are made larger in size.

This refers to situations when AI systems may demonstrate bias in their responses due to biases present in their training data. OpenAI is committed to reducing both glaring and subtle biases in how ChatGPT responds to different inputs.

These are tools provided to developers to control the behavior of the model in their applications and services.

This is the point of human-computer interaction and communication in a device, application, or website.

Documentation that provides detailed information about a machine learning model’s performance, limitations, and ideal use cases.

A type of model that uses mathematical and probabilistic framework to predict the next word or sequence of words in a sentence.

These are rules that control the text generation process from a language model.

A factor used in ChatGPT’s decoding process that penalizes the model’s tendency to repeat the same phrase.

This is the initial message displayed to users when they start a conversation with ChatGPT.

This is about ensuring that conversations with ChatGPT are private and not stored beyond 30 days.

The limit on the length of text that ChatGPT can generate in a single response.

A test proposed by Alan Turing to measure a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, human behavior.

An extension of ChatGPT designed to follow instructions given in a prompt and provide detailed explanations.

A conversation involving back-and-forth exchanges between two participants, such as a user and an AI.

A system designed to converse with humans in a human-like manner.

The measure of how well the AI responds to user prompts, including relevance, coherence, and factuality of the response.

Techniques used to increase the amount of training data, such as introducing variations of existing data or creating synthetic data.

A type of search that seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms.

The rules that govern how the AI responds to different types of input.

A method of training AI models using a fixed dataset without real-time interaction with the environment.

An optimization algorithm used in reinforcement learning to improve model training.

A controlled setting where developers can safely experiment and test new code without affecting the live product.

This is the practice of training AI models on multiple machines. This allows the training process to handle more data and complete faster.

An approach in machine learning that makes decisions based on limited information in real-time. It’s about balancing exploration (trying new things) with exploitation (sticking with what works).

A technique used in the fine-tuning process of ChatGPT, where multiple responses are generated and then ranked to select the best one.

A part of the transformer model that predicts the next token in the sequence.

This is a method used to train neural networks by calculating the gradient of the loss function. This is vital for fine-tuning the weights of the network.

This is a technique used in deep learning models, where the model assigns different weights or “attention” to different words or features when processing data.

This is a technique used in deep learning models, where the model assigns different weights or “attention” to different words or features when processing data.

This is a technique used in deep learning models, where the model assigns different weights or “attention” to different words or features when processing data.

This is a technique used in deep learning models, where the model assigns different weights or “attention” to different words or features when processing data.