If you’ve spent any time online lately, you’ve probably heard a lot of “new” AI-related words being thrown around: LLM, generative AI, neural network, and many others. These terms can sound like something straight out of a sci-fi movie. However, they’re actually part of a new language that’s becoming as common as talking about apps and social media.

Artificial intelligence is no longer a topic just for researchers and industry insiders. More and more “normal” users are interested in jumping on the AI bandwagon. Whether you’re trying to understand a news article, you want to try out a new AI tool, or you’re just curious about the future, knowing the basics is a huge help.

We’ll break down 61 of the most important terms, moving from the fundamentals that everyone should know to the more specific language for those who want to dig a little deeper.

The Core Concepts: The Building Blocks of AI

Let’s start with the big picture. These are the terms you’ll see almost everywhere.

Artificial Intelligence (AI): The broadest and most general term. AI is a technology that can “simulate” human intelligence, enabling systems with learning, problem-solving, and decision-making capabilities.

Machine Learning (ML): A key part of AI. ML is the process of training a computer to learn from data without being explicitly programmed for every possible scenario. You give it data, and it figures out the patterns and rules on its own.

Deep Learning (DL): A specialized form of ML. It uses complex structures called neural networks to process data in many layers, much like a human brain does. Deep learning is behind some of the most impressive AI tools we have today.

Neural Network: The underlying structure of a deep learning model. It’s a series of interconnected layers of “nodes” that work together to process information, with each layer refining the data. It is based on the functioning of the human brain.

Generative AI (GenAI): An AI that can create new, original content. This includes everything from essays and stories to images, music, and computer code.

Predictive AI: An AI that uses data to make a prediction about the future. For example, a predictive AI might analyze shopping data to predict which products will be popular next season.

Natural Language Processing (NLP): The field of AI that allows computers to understand, interpret, and generate human language. Whenever you use a chatbot or a voice assistant, you’re experiencing NLP in action. It removes the “robotic language” barrier that requires rigid commands.

Computer Vision: The field of AI that allows computers to “see” and interpret visual information from images and videos. This is used in everything from facial recognition to self-driving cars.

Prompt: This is basically what you say to an AI. It’s the text or command you type into a chatbot to get it to generate a response. A good prompt is often the key to getting a good answer.

Large Language Model (LLM): The technology behind many generative AIs. An LLM is a powerful AI model that has been trained on a massive amount of text data. Tools like ChatGPT and Gemini are examples of LLMs. End users mostly resort to cloud computing to run them, since LLMs require fairly powerful—and expensive—hardware to run locally.

Small Language Model (SML): Similar to LLM, but trained with far fewer parameters and less data. They are designed to run locally on low-power devices. For example, the Gemini Nano is present on multiple Android phones. SMLs enable AI-powered features such as text summarization, writing assistance, and basic generative image editing, among others.

Hallucination: A quirky but important term. When an AI “hallucinates,” it gives a confident-sounding answer that is completely wrong or nonsensical.

Deepfake: A synthetic video, audio, or image of a person that looks or sounds so real it can be difficult to tell it’s fake.

The Training Ground: How AI Models Are Built

These terms explain the process of creating and training an AI model.

Model: The trained algorithm itself. It’s the file or program that contains the AI’s “knowledge” and is ready to make predictions or generate content. There are models for specific tasks, like Google’s Imagen (GenAI for pictures) and Veo (GenAI for videos).

Dataset: The entire collection of training data used to teach an AI. It’s the library of information the model learns from.

Model Training: The entire process of feeding an AI model with data to teach it a specific task.

Supervised Learning: A training method where the AI gets a labeled dataset. It’s like a student with a teacher: the data has answers, and the AI learns by matching its predictions to the correct ones.

Unsupervised Learning: A training method where the AI gets unlabeled data and has to find hidden patterns on its own. It’s like giving a student a stack of photos and asking them to organize them into groups.

Reinforcement Learning: A training method where the AI learns by trial and error. It then receives “rewards” for correct actions and “penalties” for wrong ones. It’s how AI can learn to play a game, like chess or Go, and get better with practice.

Zero-Shot Learning: The ability of an AI model to perform a task it was not explicitly trained on, based purely on its broad understanding from its training data.

Few-Shot Learning: The ability of an AI model to learn a new task from just a small number of examples.

Data distillation: A technique used to transfer knowledge from a large, complex model (the “teacher”) to a smaller, more efficient one (the “student”). It enables smaller models to replicate the performance of larger ones, but with better efficiency and fewer resources.

Synthetic Data: Artificially generated information that mimics the statistical properties of real-world data but does not contain any actual real-life information. It’s useful to train, test, and validate AI models, especially when real data is scarce or expensive/impossible to obtain.

Fine-Tuning: The process of taking a pre-trained model and training it on a smaller, more specific dataset to make it better at a particular task.

Overfitting: A problem that occurs when a model learns the training data “too well,” memorizing specific examples instead of understanding the general patterns. This causes the model to perform poorly on new data.

Inference: The process of using a trained AI model to make a prediction or generate new content from new, unseen data.

Bias: A systematic error in an AI system that leads to unfair or inaccurate outcomes. This often happens when the training data is not representative of the real world.

Retrieval-Augmented Generation (RAG): A technique that allows an LLM to access and use external knowledge bases to provide more accurate and up-to-date information, reducing hallucinations.

The Chatbot Lingo: Talking to AI

These terms are especially useful when you’re interacting with a conversational AI.

Prompt Engineering: The art and science of crafting effective prompts to get the best possible response from an AI model.

Prompt Chaining: Ability to link multiple prompts together in a sequence. The output of one prompt is used as the input for the next one.

Context Window: The amount of information an AI model can “remember” or consider at one time when generating a response. If a conversation goes on for too long, the AI might forget what you said at the beginning.

Token: The smallest unit of data an AI model processes. In text, a token can be a word, a part of a word, or even a punctuation mark.

Temperature: A setting that controls how “creative” or random an AI’s response is. A high temperature leads to more varied and unpredictable answers, while a low temperature makes the response more predictable and conservative.

Agents: AI systems that can perform complex, multi-step tasks on their own, often without constant human oversight. For example, an agent could book a flight for you by interacting with multiple websites.

Going Deeper: The Technical and Advanced Terms

If you want to understand what’s happening under the hood, these terms will give you a glimpse into the mechanics of AI models.

Parameters: The internal settings or variables that an AI model adjusts during training. You can usually measure the size of an LLM by its number of parameters. An LLM with fewer than 10 billion parameters is considered an SML.

Vector Embeddings: A way to represent words, images, and other data as numerical vectors. This allows an AI to understand the relationships and similarities between different pieces of information.

Algorithm: The set of rules or instructions a model follows to learn from data.

Backpropagation: A core algorithm used in deep learning to train a neural network. It involves working backward through the network to adjust the weights of the connections, improving accuracy.

Weights: The values that a neural network assigns to its connections. These weights determine the importance of the inputs and are adjusted during training.

Layers: The different levels of a neural network. Information flows from an input layer, through one or more hidden layers, to an output layer.

Attention Mechanism: A technique that allows a model to focus on the most important parts of the input data when generating a response. It’s crucial for the performance of large language models.

Transformer: A specific type of neural network architecture that uses the Attention Mechanism to handle sequential data. It’s incredibly effective for tasks like language translation and generation.

Reinforcement Learning with Human Feedback (RLHF): A training method that uses human preferences to fine-tune an AI model’s behavior. This makes it more helpful and aligned with human values.

Generative Adversarial Networks (GANs): A type of generative AI that uses two competing neural networks—one to create content and another to try and spot if it’s fake—to create incredibly realistic images and videos.

Latency: The time it takes for an AI model to process a request and generate a response. Lower latency means faster results.

Model Drift: A phenomenon where an AI model’s performance slowly degrades over time as the real-world data it encounters changes.

Explainable AI (XAI): A field of AI dedicated to making the decision-making processes of AI models more transparent and understandable to humans.

Image Generation: The process of using AI to create new images from scratch, often from a text prompt.

Text-to-Image: A specific type of generative AI that creates an image based on a text prompt.

Text-to-Speech (TTS): The technology that converts written text into synthesized speech.

Speech Recognition (STT): The technology that converts spoken language into text.

Sentiment Analysis: The process of using AI to determine the emotional tone or opinion expressed in a piece of text, such as in a social media comment or customer review.

Robotics: A field that combines AI with physical machines to create robots that can perform tasks in the real world.

Natural Language Understanding (NLU): A subset of NLP (Natural Language Processing) that focuses specifically on a computer’s ability to understand the meaning behind human language, including context and intent.

Natural Language Generation (NLG): A subset of NLP that focuses on a computer’s ability to generate human-like text from data.

Application Programming Interface (API): A set of rules and protocols that allows two different software programs, like a website and an AI model, to communicate with each other.

GPU (Graphics Processing Unit): A specialized processor that is incredibly good at handling the parallel computations needed for training AI models.

Algorithm: The set of rules or instructions a model follows to learn from data.

Artificial General Intelligence (AGI): A hypothetical form of AI that would possess the ability to understand, learn, and apply knowledge across a wide range of tasks at a human-like level, similar to a human brain.

Tokenization: The process of breaking down a sequence of text into smaller, manageable units called tokens for an AI model to process.

Chatbot: A software application that can hold a conversation with a human using text or voice.

Welcome, and enjoy your AI travel

With these terms, you’re more than ready to navigate the fast-paced world of AI. This technology is a powerful tool, and understanding its language is the first step toward using it effectively and responsibly.
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