Artificial Intelligence terms might seem like an indecipherable jargon to newcomers, similar to how I feel utterly bewildered when my musician friend tried explaining the subtle differences between various guitar pedals, assuming I'd grasp the nuances as easily as they do.

Or when a friend, passionate about vintage watches, launches into a detailed discussion of various movements and styles, leaving you perplexed.

To help demystify the domain of artificial intelligence definitions, I aim to unpack every AI related term, abbreviation, and concept you might encounter.

The guide is extensive, but its purpose is to serve as a detailed resource, not a memorization challenge.

This is why it's available for you to consult at any time.

This artificial intelligence glossary is in alphabetical order.

Here's a quick list of useful beginner terms:

Artificial General Intelligence (AGI), Augmented Reality (AR) and Virtual Reality (VR), Chatbots, ChatGPT, Conversational AI, Generative AI, Hallucinations, Internet of Things (IoT), Large Language Models (LLMs), Machine Learning, Machine Learning Algorithms, Midjourney, Virtual Assistants

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

Activation Functions: ReLU, Sigmoid, Tanh

Technical Definition:

ReLU (Rectified Linear Unit) is a piecewise linear function that outputs the input directly if positive, else, it will output zero. It introduces non-linearity to the model.
Sigmoid function outputs a value between 0 and 1, making it useful for models where we need to predict probabilities.
Tanh (Hyperbolic Tangent) function outputs values between -1 and 1, making it more suited for layers deep in the network architecture.

Simplified Definition: Activation functions decide whether a neuron should be activated or not. It's like deciding if a light should be turned on based on the sunlight in the room:

ReLU is like using a simple switch: if it’s somewhat bright, keep it on; otherwise, off.
Sigmoid is like a dimmer switch that adjusts smoothly.
Tanh is like a switch that can dim down or brighten up, depending on the light.

Adapter

Technical Definition: In AI, an adapter is a component or module that allows for the integration of different systems, models, or applications so they can work together efficiently, despite not being originally designed to do so.

Simplified Definition: An adapter is like using a special plug that lets you charge your phone with any kind of outlet, even if it's different from the one your charger usually fits into.

Adobe Firefly

Technical Definition: Adobe Firefly is Adobe’s family of creative generative AI models designed to assist in generating images, effects, and other creative assets. It's tailored for creative professionals to enhance their workflow with AI-powered tools.

Simplified Definition: Adobe Firefly is like having an artist's magic brush that helps you create amazing pictures or designs by just telling it what you're thinking about.

Artificial General Intelligence (AGI)

Technical Definition: AGI is a level of artificial intelligence where a machine can understand, learn, and apply its intelligence to solve any problem in a way that is indistinguishable from human intelligence, across a wide range of domains.

Simplified Definition: AGI is like a robot that's as smart as a person and can do anything a human can do, from writing stories to solving puzzles, without needing special training for each task.

AI Copilot

Technical Definition: An AI Copilot refers to an artificial intelligence system designed to assist users in completing tasks more efficiently by providing suggestions, automations, or even taking direct action. It's commonly used in software development, writing, and other creative or technical processes.

Simplified Definition: An AI Copilot is like having a smart robot friend that helps you do your homework or work projects faster by giving you ideas or doing some of the easy parts for you.

AI Ethics

Technical Definition: AI Ethics is a branch of ethics concerned with how AI systems are designed, developed, and used. It deals with questions about the morality of actions taken by artificially intelligent agents, as well as the impacts of AI technologies on society and individuals.

Simplified Definition: AI Ethics is thinking about right and wrong in how we make and use robots or AI, like making sure they are fair to everyone and don't hurt people's feelings or privacy.

Algorithm

Technical Definition: An algorithm is a set of step-by-step procedures or rules to be followed in calculations or problem-solving operations, especially by a computer.

Simplified Definition: An algorithm is like a recipe for computers to follow, telling them how to do something step by step, whether it’s solving a math problem or finding the fastest way to get home.

Annotation

Technical Definition: Annotation in AI involves adding explanatory notes or labels to data, like images or text, so that AI models can learn from this information. It's a crucial part of training AI to recognize and understand various elements within the data.

Simplified Definition: Annotation is like putting sticky notes on things to remember what they are or what they're for. It helps AI remember and learn about different things, like telling which photos have cats in them.

Artificial Creativity

Technical Definition: Artificial Creativity, or computational creativity, refers to the ability of AI systems to produce work or come up with ideas that are novel, useful, and surprising, mimicking the creative process of humans.

Simplified Definition: Artificial creativity is when a computer or robot can come up with new, cool ideas or make art all by itself, almost like it has an imagination.

Artificial General Intelligence (AGI)

Simplified Definition: AGI is like a robot that's as smart as a person and can do anything a human can do, from writing stories to solving puzzles, without needing special training for each task.

Artificial Neural Networks (ANNs)

Technical Definition: ANNs are computing systems inspired by the biological neural networks that constitute animal brains. Comprising interconnected nodes or "neurons," these networks can learn to perform tasks by considering examples, generally without being programmed with any task-specific rules.

Simplified Definition: Imagine a fishnet where each knot can think and learn. If you throw a ball at it, the net learns where to catch the ball every time you throw it. That's what artificial neural networks do with information—they learn from examples to make decisions.

Augmented Reality (AR) and Virtual Reality (VR)

Technical Definition: AR and VR are technologies that alter a user's perception of their environment. AR overlays digital content on the real world through devices like smartphones or AR glasses, while VR creates a fully immersive digital environment that users can interact with through VR headsets.

Simplified Definition: AR is like using magic glasses to see cartoon characters dancing on your desk, while VR is like stepping into a video game world where you can look around, move, and play as if you're really there.

Autonomous Systems

Technical Definition: Autonomous Systems are systems capable of performing tasks or making decisions without human intervention. These systems are designed to operate in an unpredictable environment, making decisions based on their programming and the data they gather.

Simplified Definition: Imagine a robot that can do chores, play games, or even drive a car all by itself, without anyone telling it what to do every time. Autonomous systems are like smart robots that can make their own choices and do tasks on their own.

Bayesian Networks

Technical Definition: Bayesian networks are a type of probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph. They are used for modeling knowledge in systems where there is uncertainty.

Simplified Definition: Bayesian networks are like drawing a map of guesses where some guesses depend on others. They help computers make better guesses about things when they're not sure.

Benchmarking

Technical Definition: Benchmarking in AI refers to the process of comparing the performance of various AI models or systems against a set of predefined standards or metrics to evaluate their effectiveness, efficiency, and accuracy.

Simplified Definition: Benchmarking is like having a race where different robots compete to see who's the fastest or strongest, but instead of racing, they're seeing who's the best at solving problems or answering questions.

Big Data

Technical Definition: Big Data refers to extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Simplified Definition: Big Data is like a gigantic library with billions of books. It’s so huge that you need special tools to find and understand the stories and facts hidden inside.

Bias in AI

Technical Definition: In AI, bias refers to systematic and unfair discrepancies in the operation or outcomes of algorithms. These biases often stem from the data used to train AI systems, reflecting existing prejudices or stereotypes.

Simplified Definition: If you only learn about apples from stories that say apples are bad, you might think all apples are bad, even though it's not true. AI bias happens when a computer learns from information that isn't fair or right, making it think in a way that's not fair to everyone.

Blockchain

Technical Definition: Blockchain is a distributed database or ledger that is shared among the nodes of a computer network. As a database, a blockchain stores information electronically in digital format. Its most notable feature is its security, decentralization, and immutability.

Simplified Definition: Imagine a magic notebook that many people can write in at the same time. Once something is written, it can't be erased or changed, and everyone can see what's written, making it a very special and trustworthy notebook.

Chatbots

Technical Definition: Chatbots are artificial intelligence systems that interact with users through text, voice, or both, integrated into messaging platforms, websites, or mobile apps. They simulate human conversation to assist with customer service, information acquisition, or other tasks.

Simplified Definition: Chatbots are like robot helpers that can chat with you. If you ask them questions or tell them to do something, like checking the weather or ordering pizza, they understand and help you out, just by texting or talking.

ChatGPT

Technical Definition: ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) models developed by OpenAI, designed specifically for understanding and generating human-like text. This AI excels in a variety of conversational tasks, capable of generating responses that are contextually relevant and engaging across countless topics. ChatGPT has been trained on a diverse range of internet text, but it is designed to filter out harmful or biased information to ensure safety and fairness in its interactions.

Simplified Definition: ChatGPT is like a chatbot that can talk about almost anything under the sun. It's really good at understanding what you mean and can keep up a conversation just like a human. Whether you need help with homework, want to know something interesting, or just feel like chatting, ChatGPT is like having a friend who is always there to talk.

Claude

Technical Definition: Claude is an AI language model developed by Anthropic. It's designed to be conversational, providing users with responses that are not only informative but also considerate and safe, focusing on understanding and generating human-like text.

Simplified Definition: Claude is like a digital friend who's really good at chatting, giving advice, or explaining things in a nice and helpful way.

Cloud Computing

Technical Definition: Cloud Computing refers to the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale.

Simplified Definition: Cloud computing is like having a huge, invisible computer that you can use anytime, anywhere through the internet. Instead of saving all your photos, documents, and games on your own computer, you keep them in this invisible computer so you can access them from any device, like magic.

Collective Learning

Technical Definition: Collective Learning refers to a process where multiple AI systems or models share insights, data, or learning outcomes with each other to improve their collective knowledge and performance without compromising data privacy or security.

Simplified Definition: Collective learning is when a group of smart robots learn things and then share what they've learned with each other so they all get smarter together, like students helping each other in class.

Computer Vision

Technical Definition: Computer Vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and take actions or make recommendations based on that information.

Simplified Definition: Computer vision is like teaching a computer to see and understand pictures and videos. Just like you know that a photo has a cat in it, computer vision helps computers recognize cats, people, cars, and lots more in the images.

Controllability

Technical Definition: Controllability in AI refers to the ability to guide or direct the actions and outputs of an AI system, ensuring that it behaves in a predictable and desired manner, especially in complex or unpredictable environments.

Simplified Definition: Controllability is like having a remote control for a robot that lets you tell it exactly what to do, making sure it doesn't do something you don't want it to.

Conversational AI

Technical Definition: Conversational AI refers to artificial intelligence systems designed to communicate with humans in a natural, interactive way, using text or voice. This technology powers virtual assistants, chatbots, and other applications requiring natural language understanding and generation.

Simplified Definition: Conversational AI is like a robot you can talk to just like you would with a friend. It can answer questions, chat about the weather, or help you with tasks by understanding and speaking your language.

Convolutional Neural Networks (CNNs)

Technical Definition: CNNs are a class of deep neural networks, most commonly applied to analyzing visual imagery. They use a mathematical operation called convolution and are structured as a series of layers that automatically and adaptively learn spatial hierarchies of features from input images.

Simplified Definition: CNNs are like very advanced automatic photo analyzers. Imagine a robot that can look at a picture and notice all the tiny details, like if it's a picture of a dog, where the dog is, and what the dog is doing, by breaking the picture down into smaller pieces and understanding each piece.

Cost of Large Language Models

Technical Definition: This refers to the financial, computational, and environmental expenses associated with developing, training, and running large language models (LLMs) due to their need for extensive data, high processing power, and energy consumption.

Simplified Definition: The cost of large language models is like the bill you get for making a super-duper big robot that needs lots of electricity and computer parts to think and learn from tons of books and websites.

Cybersecurity

Technical Definition: Cybersecurity refers to the practice of protecting systems, networks, and programs from digital attacks. These cyberattacks are usually aimed at accessing, changing, or destroying sensitive information; extorting money from users; or interrupting normal business processes.

Simplified Definition: Cybersecurity is like being a digital superhero that protects computers and the internet from villains. These heroes fight against viruses and hackers to keep our information safe and secure.

Data Augmentation

Technical Definition: Data augmentation involves increasing the size and diversity of a dataset without actually collecting new data, through techniques such as cropping, rotating images, or synthesizing new data points. This helps improve the robustness and accuracy of AI models.

Simplified Definition: Data augmentation is like using a copy machine to make puzzles harder and more interesting by flipping or twisting the pieces in different ways, so a robot has more ways to practice solving them.

Data Mining

Technical Definition: Data Mining is the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the Internet, and other data repositories or data sets.

Simplified Definition: Data mining is like being a treasure hunter in a huge library. Instead of looking for gold or jewels, you're searching through mountains of information to find hidden secrets or discover new things nobody knew were there.

Deep Learning

Technical Definition: Deep Learning is a subset of machine learning based on artificial neural networks with representation learning. It involves networks with many layers that enable the automatic learning of hierarchical representations and features from data, facilitating tasks like image and speech recognition.

Simplified Definition: Deep learning is when you have a really smart robot that learns like a super student. Imagine it's like building a tower of blocks where each layer learns something more complex than the one below. This way, the robot can understand pictures or what someone is saying by looking at many examples.

Deterministic Model

Technical Definition: A deterministic model in AI is a model that, given a particular input, will always produce the same output, without any randomness in the process. This predictability makes them suitable for tasks requiring a high degree of accuracy and consistency.

Simplified Definition: A deterministic model is like a predictable magic trick where if you do the same steps the same way, you'll always get the same rabbit out of the hat, no surprises.

Digital Twin

Technical Definition: A Digital Twin is a virtual model designed to accurately reflect a physical object. Digital twins are used throughout the lifecycle of an asset to simulate, predict, and optimize the asset using data and analytics.

Simplified Definition: A digital twin is like having a video game version of something real, like a car or a building. You can play around with it in the computer to see what happens in different situations, helping you make better decisions for the real thing.

Dimensionality Reduction: PCA, t-SNE

Technical Definition:

Principal Component Analysis (PCA) reduces the dimensionality of data by transforming it into a new set of variables, the principal components, which are uncorrelated and ordered by how much of the data's variation they capture.
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization that reduces high-dimensional data to two or three dimensions so that it can be plotted.

Simplified Definition: Dimensionality reduction is like turning a very detailed and complex coloring book into a simpler one with fewer pages, so it's easier to see the big picture without losing too much detail.

Discriminative Model

Technical Definition: Discriminative models in AI are designed to distinguish between different types of data inputs, predicting a specific output label given an input. They focus on the relationship between the input data and the output label.

Simplified Definition: Discriminative models are like playing a guessing game where you have to figure out if an animal is a cat or a dog based on clues. These models get really good at telling the difference between things.

Edge Computing

Technical Definition: Edge Computing refers to the practice of processing data near the edge of your network, where the data is being generated, instead of in a centralized data-processing warehouse. This reduces latency, bandwidth use, and allows for real-time data processing.

Simplified Definition: Imagine if your smart toy could think and make decisions quickly on its own without having to ask a computer far away for help every time. Edge computing is like giving devices their own little brains to make them faster and smarter, right where they are.

Ensemble Methods: Bagging, Boosting, Stacking

Technical Definition:

Bagging (Bootstrap Aggregating) reduces variance by training multiple models independently and then averaging their predictions.
Boosting improves the prediction strength of weak models by sequentially training models to correct the mistakes of prior models.
Stacking involves training a new model to combine the predictions of several other models to improve accuracy.

Simplified Definition: Ensemble methods are like team projects where each team member (or model) does a part of the job:

Bagging is everyone works separately, and then you average all their work.
Boosting is like each person fixing the mistakes of the person before them.
Stacking is when you get a team leader to decide how to best combine everyone's work.

Evolutionary AI

Technical Definition: Evolutionary AI is a subset of artificial intelligence that uses evolutionary algorithms inspired by biological evolution to generate solutions to problems, iterating over generations to optimize results.

Simplified Definition: Evolutionary AI is like a robot that gets better by pretending it's evolving like animals. It tries lots of small changes over time, keeping the good changes and getting rid of the bad ones, to get really good at something.

Explainable AI (XAI)

Technical Definition: XAI refers to methods and techniques in the application and development of artificial intelligence technology that make the results of the solution understandable by humans. It contrasts with the "black box" nature of many AI models, where the decision-making process is not transparent.

Simplified Definition: Imagine if your toy could talk and explain how it works, why it moves the way it does, or why it makes certain sounds. Explainable AI is like giving the computer a voice so it can tell us how it thinks and makes decisions, making it easier for us to understand and trust it.

Feature Engineering

Technical Definition: Feature Engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms.

Simplified Definition: Feature engineering is like being a detective looking at clues to solve a mystery. You pick out important bits of information that help solve the case faster and better.

Federated Learning

Technical Definition: Federated Learning is a machine learning approach where the model is trained across multiple decentralized devices or servers holding local data samples, without exchanging them. This process allows for privacy-preserving data analysis and model training.

Simplified Definition: Imagine if all the smartphones in the world could learn together to get smarter, but without needing to share or send their photos and messages anywhere else. Federated learning is a way for devices to learn from each other while keeping all your stuff private.

Frequency Penalty

Technical Definition: Frequency Penalty is a setting that decreases the likelihood of a token being selected by the model again based on how many times it has already appeared in the prompt or response. This helps in reducing repetition and promoting more diverse vocabulary in the output.

Simplified Definition: If you're coloring and you keep using the same color too much, your picture might get boring. Frequency Penalty is like your friend suggesting you use other colors more often to make your picture more interesting.

Generative Adversarial Networks (GANs)

Technical Definition: GANs are a class of artificial intelligence algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a zero-sum game framework. This setup enables the generation of new data with the same statistics as the training set.

Simplified Definition: Imagine two artists: one tries to create new paintings, and the other judges if they're good or not. The first artist keeps trying to make paintings that can fool the judge into thinking they're real. This is how GANs work, with one part making stuff and the other part checking it, both getting better over time.

Generative AI

Technical Definition: Generative AI refers to algorithms that can create content, such as text, images, videos, or music, by learning from a dataset. These AI systems understand how to produce new pieces of content that resemble the original data.

Simplified Definition: Generative AI is like an artist robot that can make its own paintings or songs after looking at lots of other art. It learns what makes them good and tries to create something new that's just as cool.

Google Gemini

Technical Definition: Google Gemini is a state-of-the-art artificial intelligence model developed by Google, characterized by its multimodal capabilities. It can understand, interpret, and generate responses across different types of information, including text, images, audio, and video. Gemini is versatile, with applications in various domains such as math, physics, and coding, available in three versions: Nano, Pro, and Ultra, each designed for specific performance and scalability needs.

Simplified Definition: Google Gemini is like a super smart robot that can understand not just words but pictures, sounds, and videos too. It can answer tough questions, explain complex ideas, and even write code in different languages. Whether it’s on a phone or in a big computer server, Gemini can do a lot of smart things really well.

Gradient Descent

Technical Definition: Gradient Descent is a first-order iterative optimization algorithm used to find the minimum of a function. It's commonly used in machine learning to minimize error by iteratively moving towards the steepest descent as defined by the negative of the gradient.

Simplified Definition: Gradient descent is like playing a game of "hot and cold" where you’re trying to find a hidden treasure on a hill. Each step you take is based on a guess of whether going forward, left, or right will get you closer to the lowest point where the treasure is buried.

Hallucinations

Technical Definition: In the context of AI, hallucinations refer to instances where a model generates false or misleading information, often as a result of overfitting, lack of sufficient data, or biases in the training data.

Simplified Definition: Hallucinations in AI are like when a robot or computer makes up stuff that isn't true or doesn't make sense because it got confused by what it learned.

Hyperparameter Tuning

Technical Definition: Hyperparameter Tuning involves adjusting the parameters that govern the training process of a machine learning model to improve its performance. Unlike model parameters that are learned during training, hyperparameters are set before training and control aspects like learning rate or model complexity.

Simplified Definition: Hyperparameter tuning is like adjusting the settings on a video game before you start playing to make it just right for you, so you have the best chance of winning.

Internet of Things (IoT)

Technical Definition: The Internet of Things describes the network of physical objects—"things"—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.

Simplified Definition: IoT is like giving everyday things, like lamps, watches, or refrigerators, the ability to talk to each other over the internet. For example, your alarm clock can tell your coffee maker to start brewing coffee right when you wake up.

Jasper.ai

Technical Definition: Jasper.ai is an AI-powered content creation platform that helps users generate high-quality written content. It utilizes advanced AI to assist with various writing tasks, from marketing copy to blog posts, by understanding context and generating relevant text.

Simplified Definition: Jasper.ai is like a writing wizard that helps you come up with words for stories, ads, or articles, making it easier to write really well without getting stuck.

Kernel Methods: Support Vector Machines (SVM)

Technical Definition: SVMs are a set of supervised learning methods used for classification, regression, and outliers detection. Kernel methods allow SVMs to solve linear and non-linear problems by transforming the original data into a higher-dimensional space where it becomes easier to separate.

Simplified Definition: SVMs are like smart decision-makers that can draw lines or curves to separate different types of things, like separating apples from oranges, even when they're mixed up in complex ways.

Knowledge Graph

Technical Definition: A Knowledge Graph is a large network of entities (objects, events, situations, etc.) that are interconnected by edges representing their relationships. It's used for storing data in a way that emphasizes the connections between information.

Simplified Definition: A knowledge graph is like a huge spider web where each part of the web is a piece of information, and the strands that connect them show how they are related, like who owns what or what happened when.

LLM (Large Language Models)

Technical Definition: Large Language Models (LLMs) are advanced AI models trained on vast amounts of text data. They are capable of understanding and generating human-like text, performing tasks like translation, summarization, question-answering, and more, based on the patterns they've learned.

Simplified Definition: LLMs are like super-smart robots that have read a lot of books and articles, so they can write and talk about almost anything you can think of.

Loss Functions: MSE, Cross-Entropy

Technical Definition:

Mean Squared Error (MSE) measures the average squared difference between the estimated values and the actual value, used mainly for regression tasks.
Cross-Entropy measures the difference between two probability distributions, often used in classification tasks.

Simplified Definition: Loss functions are like scoring how well you did in a game, but in reverse: lower scores are better.

MSE is like measuring how far a dart is from the bullseye and aiming to get as close as possible.
Cross-Entropy is like guessing the outcome of multiple-choice questions and trying to match the correct answers as closely as possible.

Machine Learning

Technical Definition: Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions, relying instead on patterns and inference.

Simplified Definition: Machine learning is like teaching a robot to learn from lots of examples. If you show it many pictures of cats and dogs, eventually, it can learn to tell the difference between them, even if it has never seen those particular pictures before.

Machine Learning Algorithms

Technical Definition: Machine Learning Algorithms are a set of rules and statistical techniques used by computers to learn from data. They enable computers to improve their performance on a specific task with data, without being explicitly programmed for that task.

Simplified Definition: Machine learning algorithms are like recipes that a computer follows to learn from examples. Just like following a recipe can teach you how to bake a cake, these algorithms help the computer learn how to do things like recognize faces or recommend movies.

Max Length

Technical Definition: Max Length is a parameter that limits the number of tokens (words or pieces of words) the language model can generate in response to a prompt. This helps in managing the length of the output to prevent overly long responses and control computational resources.

Simplified Definition: If you're telling a robot to write you a story and you don't want it to write a book, you tell it to only write a page. Max Length is like telling the robot how long the story should be before it has to stop writing.

Midjourney

Technical Definition: Midjourney is a generative AI developed by Midjourney, Inc. It specializes in creating complex visual images from textual descriptions, leveraging advanced machine learning algorithms to interpret and visualize prompts into detailed artworks. This technology facilitates a broad spectrum of creative and exploratory applications by enabling users to generate art from text.

Simplified Definition: Midjourney is like having a magic art box where you tell it a story or describe a picture with words, and it draws that picture for you. It's like if you could describe a dream and then see the dream come to life as a piece of art.

Model Evaluation

Technical Definition: Model Evaluation involves assessing the performance of a machine learning model on a specific task, using metrics like accuracy, precision, recall, and others to understand how well the model is performing.

Simplified Definition: Model evaluation is like giving a report card to a robot, showing how well it did on its test. Did it get an A for being really good at its job, or does it need more study?

Model Fine Tuning

Technical Definition: Model Fine-Tuning is a process in deep learning where a model trained on one task is adjusted or "fine-tuned" to perform better on a similar but different task. This usually involves continuing the training process with a smaller, task-specific dataset.

Simplified Definition: Fine-tuning a model is like tuning a guitar to make it sound just right for a new song. You already know how to play the guitar, but you make small adjustments so it sounds perfect for what you're going to play next.

Natural Language Processing (NLP)

Technical Definition: NLP is a field of artificial intelligence that gives machines the ability to read, understand, and derive meaning from human languages. It involves the development of algorithms that allow computers to process and analyze large amounts of natural language data.

Simplified Definition: NLP is like teaching a robot to understand and talk like a human. It learns to read what we write and listen to what we say, then figures out what it means so it can respond in a way we understand.

Natural Language Understanding (NLU)

Technical Definition: NLU is a subfield of NLP focused on enabling machines to understand and interpret human language in a way that is both meaningful and contextually relevant.

Simplified Definition: NLU is like teaching a computer to listen and really understand what people mean, not just the words they say but also the hidden meanings, like when someone is joking or being sarcastic.

Neural Networks

Technical Definition: Neural Networks are computing systems vaguely inspired by the biological neural networks that constitute animal brains. They are composed of layers of nodes or "neurons," each layer processing an aspect of the data, allowing the system to learn complex patterns through training.

Simplified Definition: Think of a neural network like a team of detectives trying to solve a mystery. Each detective looks at different clues (pieces of information), and they pass notes to each other to put together the big picture and solve the case. The more they work on cases, the better they get at solving them.

Ontology

Technical Definition: In the context of AI and data science, an ontology is a structured framework that categorizes and describes the properties and relationships between concepts within a particular domain, enabling a shared understanding.

Simplified Definition: An ontology is like making a big family tree for ideas and things, showing how every topic is related to others, like which animals are mammals and which mammals are pets.

OpenAI

Technical Definition: OpenAI is an artificial intelligence research organization that aims to promote and develop friendly AI in a way that benefits humanity as a whole. It is known for its cutting-edge research in AI and its development of highly advanced AI models, including the renowned GPT (Generative Pre-trained Transformer) series. OpenAI conducts research across a wide range of AI applications, from natural language processing to computer vision, with a focus on developing transparent and ethical AI technologies.

Simplified Definition: OpenAI is like a big team of scientists and engineers who work together to create smart robots and computer programs. Their goal is to make sure these AI systems are safe and helpful for everyone. They're the ones behind some of the smartest AI helpers that can write stories, answer questions, and even create art.

Optimization Algorithms: Adam, SGD, RMSprop

Technical Definition:

Adam (Adaptive Moment Estimation) combines ideas from RMSprop and Stochastic Gradient Descent (SGD) with momentum, updating weights in a way that is particularly effective for large datasets and high-dimensional spaces.
SGD updates parameters in the direction of the negative gradient, often with a small step size called the learning rate.
RMSprop (Root Mean Square Propagation) adapts the learning rate for each parameter, dividing the learning rate for a weight by a running average of the magnitudes of recent gradients.

Simplified Definition: These optimization algorithms are like different strategies for finding the best route through a maze:

Adam is like having a smart guide who remembers the best paths and adapts quickly.
SGD is like trying one path at a time and slightly adjusting based on what you learn.
RMSprop is like adjusting your speed based on how confident you are about the path.

Predictive Analytics

Technical Definition: Predictive Analytics involves the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide a best assessment of what will happen in the future.

Simplified Definition: Predictive analytics is like a fortune teller for data. It looks at all the things that have happened in the past to make smart guesses about what might happen next, like predicting what you might want for your birthday based on what you liked before.

Presence Penalty

Technical Definition: Presence Penalty applies a deterrent to the selection of tokens that have already appeared in the output, regardless of their frequency. This encourages the model to introduce new concepts and vocabulary, enhancing creativity and diversity in the response.

Simplified Definition: Imagine you're telling a story but you keep mentioning the same character over and over again. Presence Penalty is like a rule that nudges you to talk about other characters too, making your story richer and more varied.

Prompt Engineering

Technical Definition: Prompt Engineering is the process of designing and refining the inputs (prompts) given to AI models, especially LLMs, to elicit the most accurate, relevant, or creative outputs. It involves understanding how an AI model interprets instructions to optimize the interaction.

Simplified Definition: Prompt Engineering is like figuring out the best way to ask a genie for a wish. You have to be really clear and specific so the genie understands exactly what you want.

Quantum Computing

Technical Definition: Quantum Computing is a type of computing that takes advantage of quantum phenomena like superposition and entanglement to perform operations on data. This can potentially process complex problems much faster than traditional computers.

Simplified Definition: If regular computers are like super-fast calculators, quantum computers are like magical books that can flip to the answer of really, really hard puzzles instantly. They use the weird and wonderful rules of quantum physics to solve problems that would take normal computers a very long time.

Reinforcement Learning

Technical Definition: Reinforcement Learning is an area of machine learning concerned with how software agents ought to take actions in an environment to maximize some notion of cumulative reward. The agent learns to achieve a goal in an uncertain, potentially complex environment.

Simplified Definition: Reinforcement learning is teaching a robot by rewarding it for good actions, like training a dog with treats. If the robot makes a good move or decision, it gets a 'treat' (points), helping it learn the best way to do things to get more treats.

Robotics

Technical Definition: Robotics is a branch of engineering and science that involves the design, construction, operation, and use of robots. Robotics integrates fields of mechanical engineering, electrical engineering, computer science, and others to create machines capable of performing a variety of tasks, often replicating human actions.

Simplified Definition: Robotics is all about making robots, which are like electronic helpers that can do tasks on their own, such as cleaning, building things, or even exploring planets!

Semantic Analysis

Technical Definition: Semantic Analysis is the process of understanding the meaning and interpretation of words, phrases, and sentences in the context of languages. In AI, it refers to the ability of algorithms to understand human language in a way that is meaningful.

Simplified Definition: Semantic analysis is like figuring out puzzles in what people say or write. It's about understanding not just the words but what they really mean together, like telling if someone is happy, sad, or joking, just by reading what they say.

Semantic Web

Technical Definition: The Semantic Web is an extension of the World Wide Web that enables people to create data stores on the web, build vocabularies, and write rules for handling data. Linked data are empowered by technologies that allow data to be shared and reused across application, enterprise, and community boundaries.

Simplified Definition: The Semantic Web is like a super-smart web that understands and connects information in a way that both computers and people can use. It's like if the internet could understand the meaning of what we're looking for, making it easier to find and use information.

Stable Diffusion

Technical Definition: Stable Diffusion is a generative AI model capable of creating detailed images from textual descriptions. It allows users to generate visual content based on prompts, enabling a wide range of creative applications.

Simplified Definition: Stable Diffusion is like a robot that can draw anything you describe with words, turning your imagination into pictures.

Stop Sequences

Technical Definition: Stop Sequences are specified strings that signal the language model to stop generating further tokens. This feature allows for more precise control over the structure and endpoint of the model's output.

Simplified Definition: Imagine you're playing a music box that plays notes as long as you're winding it. If you put a special mark on the music strip, when it reaches that mark, the music stops. Stop Sequences are like those marks, telling the story (or whatever the model is generating) to end at a certain point.

Supervised Learning

Technical Definition: Supervised Learning is a type of machine learning where the model is trained on a labeled dataset, which means that each training example is paired with the output label it should predict. This enables the model to learn a function that can be used to predict the output associated with new, unseen data.

Simplified Definition: Supervised learning is like when you're learning to paint by numbers. The numbers tell you what color goes where. After practicing with these guides, you can paint a new picture on your own by applying what you've learned.

Swarm Intelligence

Technical Definition: Swarm Intelligence is the collective behavior of decentralized, self-organized systems, natural or artificial. It’s observed in colonies of insects like bees or ants and is applied in AI to solve complex problems through agents working together without central control.

Simplified Definition: Swarm intelligence is like watching a group of bees or ants work together perfectly without anyone telling them what to do. Each one does its small part, but together they can solve big problems, like finding the best path to food.

Temperature

Technical Definition: Temperature is a parameter in language models like LLMs that influences the randomness or diversity in the responses generated by the model. A low temperature setting results in more deterministic, predictable outputs by favoring the most likely next token. Conversely, a higher temperature encourages more varied and creative responses by increasing the probability of less likely tokens being chosen.

Simplified Definition: Imagine if you're playing a guessing game where you're trying to predict the next word in a sentence. If the game is set to 'cold' (low temperature), it's like playing it safe and choosing the most obvious word every time. But if the game is set to 'hot' (high temperature), it's like taking a chance and sometimes picking surprising words, making the game more fun and unpredictable.

Top P

Technical Definition: Top P, also known as nucleus sampling, is a parameter that controls the randomness of responses from a language model by only considering a subset of the most probable next tokens. A low Top P value makes the model's outputs more deterministic and factual, while a higher value allows for more diversity in the responses by including less probable tokens in the selection pool.

Simplified Definition: Think of choosing a snack from a big box full of different snacks. If you only reach for the snacks right at the top (low Top P), you'll probably end up with the same few kinds every time. But if you dig deeper and pick from anywhere in the box (high Top P), you'll end up trying all kinds of snacks, some of which you might not expect to like but do!

Transfer Learning

Technical Definition: Transfer Learning is 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. This approach allows a model trained on one task to be repurposed on a second task with minimal additional training.

Simplified Definition: Transfer learning is like being good at soccer and using some of what you've learned to get better at basketball. You use the skills from one game (like running fast) to help you play another game better, without starting from scratch.

Underfitting

Technical Definition: Underfitting occurs in machine learning when a model is too simple to capture the underlying structure of the data, leading to poor performance on both training and unseen data.

Simplified Definition: Underfitting is like using a tiny net to catch fish in a big pond. Because the net is too simple and small, it misses a lot of fish, just like a too-simple model misses out on understanding all the information in the data.

Unsupervised Learning

Technical Definition: Unsupervised Learning involves training models on data without labeled responses. The goal is to discover hidden patterns or intrinsic structures within the input data. Common tasks include clustering, dimensionality reduction, and association.

Simplified Definition: Unsupervised learning is like giving a child a box of Lego blocks without instructions. They start grouping blocks by color or size on their own, finding their own ways to organize and build things without being told what to do.

Virtual Assistants

Technical Definition: Virtual Assistants are AI-powered software that can perform tasks or services for an individual based on commands or questions. These tasks can range from answering questions with synthesized voices to setting alarms, playing music, or controlling smart home devices.

Simplified Definition: Virtual assistants are like invisible robot friends that live in your phone or in your home's speaker. You can ask them to do things like play your favorite song, turn on the lights, or remind you to call grandma, and they help make your life easier.

Voice Recognition

Technical Definition: Voice Recognition is a technology that recognizes spoken words, converting them into text. It's a component of speech recognition systems that enable computers to understand and respond to voice commands.

Simplified Definition: Voice recognition is like teaching a computer to understand when people talk. When you say something, it can figure out what words you're using and help you do things like search the internet or send a message just by using your voice.

Weak AI vs. Strong AI

Technical Definition:

Weak AI, also known as Narrow AI, refers to artificial intelligence systems that are designed and trained for a specific task. Virtual assistants like Siri are examples of Weak AI.
Strong AI, also referred to as Artificial General Intelligence (AGI), describes a machine with the ability to apply intelligence to any problem, rather than just one specific problem, equivalent to the broad and flexible intelligence of humans.

Simplified Definition: Weak AI is like a kitchen gadget that's really good at one thing, like a toaster, while Strong AI is like a super chef who can cook anything you can think of.