AI Learning Tools: Definitions

Software tool used to rewrite texts, such as articles or blogs, by retaining the overall message and meaning while changing the language and vocabulary significantly.

The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

A design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making and new experiences.

A computer program that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if they were communicating with a real person.

A large language model chatbot developed by OpenAI based on GPT-3.5. It has a remarkable ability to interact in conversational dialogue form and provide responses that can appear surprisingly human. Large language models perform the task of predicting the next word in a series of words. Reinforcement Learning with Human Feedback (RLHF) is an additional layer of training that uses human feedback to help ChatGPT learn the ability to follow directions and generate responses that are satisfactory to humans.

In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, but they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network.

Refers to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing text, audio and video files, images, and even code to create new possible content. Generative AI allows computers to abstract the underlying patterns related to the input data so that the model can generate or output new content.

In May 2020, Open AI published a groundbreaking paper titled Language Models Are Few-Shot Learners. They presented GPT-3, a language model that holds the record for being the largest neural network ever created with 175 billion parameters. It’s an order of magnitude larger than the largest previous language models. GPT-3 was trained with almost all available data from the Internet, and showed amazing performance in various NLP (natural language processing) tasks, including translation, question-answering, and cloze tasks, even surpassing state-of-the-art models.

Open AI released the fourth version on March 16, 2023. The fourth version is multi-modal - it can recognize and analyze images among the improvements.

A form of Supervised Machine Learning in which the aim is to train the agent by demonstrating the desired behavior.

A type of machine learning model that can perform a variety of natural language processing (NLP) tasks, including generating and classifying text, answering questions in a conversational manner and translating text from one language to another.

Advanced computing that enables a technology (a machine, device, or algorithm) to interact with its environment intelligently, meaning it can take actions to maximize its chance of successfully achieving its goals. The concept of machine intelligence highlights the intersection of machine learning and artificial intelligence, as well as the broad spectrum of opportunities and approaches in the field.

An application of artificial intelligence (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... learn for themselves.

Refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

A method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.

An American artificial intelligence (AI) research laboratory that conducts AI research with the declared intention of promoting and developing a friendly AI.

An AI research assistant with a conversational interface, contextual awareness and personalization to learn your interests and preferences over time. Perplexity achieves this with the help of our advanced answer engine to process your questions and tasks, taking into account the entire conversation history for context. Copilot is an interactive search assistant that asks you clarifying questions to guide the AI search process and find the best answer for you. Quick Search gives you faster and straight-to-the-point answers. They both aim to understand the information or answers you really want to know.

Step-by-step process of creating inputs that determine the output to be generated by an AI language model.

A machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own.

A test proposed (1950) by the English mathematician Alan M. Turing to determine whether a computer can “think.”