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Artificial Intelligence

Artificial Intelligence (AI) glossary: Must-know AI terms

Mailbutler put together a handy AI glossary to help you navigate through the key terms and concepts in the artificial intelligence world.

First published

06.11.2023

Last edited

10.05.2024

Read time

6 minutes


    By James

    James has seven years' experience as a Content Marketer, bylines on Left Foot Forward, Submittable, and INOMICS, and a Master's in History. In his free time he likes to read, play guitar, and write for his personal blog.

    Welcome to the world of AI, where technology meets innovation to transform the way we live and work. Whether you're a tech expert or just curious about artificial intelligence, this blog post is your beginner's guide to various AI terms. We've put together a handy AI glossary to help you navigate the key terms and concepts in the artificial intelligence world.

    A-C:

    AI Copilot

    An AI copilot is a conversational interface that uses large language models to support users in various tasks and decision-making processes across multiple domains within an enterprise environment.

    AI Email Assistants

    These are AI-powered tools designed to help manage, organize, and optimize email-related tasks. They can automate responses, schedule emails, sort incoming messages, and even draft emails based on context. AI email assistants aim to increase productivity and efficiency in email communication. Read more about AI Email Assistants. 

    Artificial Intelligence (AI)

    AI is about making machines and computers intelligent, in the sense that they can adapt to their environment and learn from past experiences in the form of data. Inspiration for this comes from how humans learn, but it is not the final objective to achieve a human-like learning algorithm - it's more of an inspiration.

    AI Email Response

    Refers to the automated replies or messages generated by AI models when interacting with email content. These responses can be tailored based on the context of the incoming email, ensuring relevant and timely communication. Learn more about AI email responses.

    Algorithm

    A set of step-by-step instructions that a computer follows to solve a problem or do a task.

    Bias

    When computers make unfair decisions because they learn from biased information or instructions.

    Big Data

    Extremely large and complex datasets that are difficult to process and analyze with traditional methods.

    Chatbot

    A user-friendly interface that allows the user to ask questions and receive answers. Depending on the backend system that fuels the chatbot, it can be as basic as pre-written responses to a fully conversational AI that automates issue resolution.

    ChatGPT

    ChatGPT is a large language model developed by OpenAI that is trained on a massive amount of internet text data and fine-tuned to perform a wide range of natural language tasks.

    ChatGPT and AI Statistics

    This refers to the data and trends related to the usage, performance, and impact of ChatGPT and other AI models. Statistics can provide insights into how AI is evolving, its effectiveness in various tasks, and its adoption rate among users. Discover the latest statistics for ChatGPT and AI. 

    ChatGPT Email Prompts

    These are specific instructions or questions given to ChatGPT to generate email-related content. By using the right prompts, users can get the AI to draft emails, generate summaries, or even answer email queries. Explore various ChatGPT email prompts and their applications. 

    Related posts:

    ChatGPT for Outlook

    ChatGPT Gmail extension

    ChatGPT for Apple Mail

    Conversational AI

    A sub-field of AI that focuses on developing systems that can understand and generate human-like language and conduct a back-and-forth conversation.

    D-F:

    Data Science

    People using computers to find important information and patterns in big sets of data.

    Deep Learning

    Special computer programs that can learn complicated things from lots of examples.

    Discriminative Models

    Models that classify a data example and predict a label. For example, a model that identifies whether a picture is a dog or a cat.

    Heuristic

    A problem-solving technique or approach that uses a practical approach and shortcuts to produce solutions that may not be optimal but are sufficient for reaching immediate goals. 

    Foundation Model

    Foundation models are a broad category of AI models which include large language models and other types of models such as computer vision and reinforcement learning models.

    G-I

    GPT-3

    GPT-3 is the 3rd version of the GPT-n series of models. It has 175 billion parameters — knobs that can be tuned — with weights to make predictions. Chat-GPT uses GPT-3.5, which is another iteration of this model.

    GPT-4

    GPT-4 is the latest model addition to OpenAI's deep learning efforts and is a significant milestone in scaling deep learning. GPT-4 is also the first of the GPT models that is a large multi-modal model, meaning it accepts both image and text inputs and emits text outputs.

    Generative AI

    Generative AI models create new data by discovering patterns in data inputs or training data. For example, creating an original short story based on analyzing existing, published short stories.

    Joint Probability

    The probability of two events happening at the same time. In AI and statistics, it's used to measure the likelihood of multiple events occurring together. 

    IoT (Internet of Things)

    Everyday things like thermostats, lights, and cars that can talk to each other and share information over the internet.

    Instruction-tuning

    Instruction-tuning is an approach where a trained model is adapted to perform specific tasks by providing a set of guidelines or directives that outline the desired operation.

    K-means Clustering

    An unsupervised machine learning algorithm used to partition a dataset into a set of distinct, non-overlapping subgroups (or clusters) based on their similarities. 

    L-N

    Large Language Model (or “LLM”)

    A type of deep learning model trained on a large dataset to perform natural language understanding and generation tasks

    Machine Learning (ML)

    Computers learning from examples and getting better at tasks over time, kind of like how you get better at a game.

    Model

    A computer's way of thinking about a problem or making predictions.

    Meta-learning

    A process where AI systems learn how to learn more efficiently. It involves training models on a variety of tasks and using that experience to quickly adapt to new tasks. 

    Natural Language Processing (NLP)

    Computers understanding and talking in human languages, like English or Spanish.

    O-R

    OpenAI

    The organization that developed ChatGPT. OpenAI is a research company that aims to develop and promote friendly AI responsibly.

    Overfitting

    When a computer is too good at a specific task but not so good at similar tasks it hasn't seen before.

    Prompt

    Any form of text, question, information, or coding that communicates to AI what response you're looking for.

    Plugins

    Software components that add specific features or functionalities to an existing computer program. By using plugins, a program can be customized and extended in its capabilities. For instance, web browsers use plugins to play videos, run games, or display specific types of content. 

    Quantum Computing

     A type of computing that uses quantum bits or qubits. It has the potential to solve complex problems much faster than traditional computers and could revolutionize fields like cryptography and optimization. 

    Reasoning

    AI reasoning is the process by which artificial intelligence systems solve problems, think critically, and create new knowledge by analyzing and processing available information.

    Responsible AI

    Responsible AI refers to the approach of creating, implementing, and utilizing AI systems with a focus on positively impacting stakeholders, ensuring ethical intentions, and fostering trust.

    Transfer Learning

    A machine learning method where a model developed for one task is reused as the starting point for a model on a second task. It's beneficial when the tasks are related. 

    S-U

    Sentiment Analysis

    A subfield of Natural Language Processing (NLP) that focuses on determining the emotional tone or sentiment behind a series of words. It's used to gain an understanding of the attitudes, opinions, and emotions expressed within an online mention, such as reviews or social media posts. Sentiment analysis can categorize content as positive, negative, or neutral. 

    Speech-to-text

    The process of converting spoken words into written text.

    Supervised Learning

    Teaching computers by showing them examples and letting them practice.

    Text-to-speech

    Text-to-speech (TTS) is a technology that converts written text into spoken voice output.

    Text Summarization

    The process of shortening a text document or dataset in a way that retains the most important information while discarding redundant or less important details. This can be done using various techniques, including extractive (selecting whole sentences or phrases from the source text) or abstractive (generating new sentences that convey the main points). It's useful for quickly understanding the main points of long articles, reports, or documents. 

    Unsupervised Learning

    Computers finding patterns and interesting things in data all by themselves, without being told what to look for.

    V-Z

    Word Embedding

    A representation of text in which words or phrases from the vocabulary are mapped to vectors of real numbers. It's used in many NLP tasks to capture the semantic meanings of words. 

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