A Glossary of AI Terms Defined in Relatable, Easy-to-Understand Metaphors

Navigating the intricate landscape of data and artificial intelligence can feel like deciphering a new language. To help you keep pace with the explosion of AI terms, we put together this glossary as a foundational lexicon. It breaks down complex AI concepts into digestible definitions, making them easy to understand through relatable metaphors. Use it to build a more comprehensive vocabulary and intuitive understanding of this transformative field.

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Just as learning a new language opens up new possibilities, grasping key AI terms will help you unlock new directions both personally and professionally.

Artificial Intelligence (AI) & Its Subfields

What is artificial intelligence (AI)?

Think of AI as the overarching ambition — building machines that mimic human cognitive abilities. Instead of a singular, conscious robot of science fiction, current AI is more like a collection of specialized tools, each incredibly skilled at particular tasks, from playing chess to recognizing faces.

What is machine learning (ML)?

If AI is the ambition, ML is a core method. It's the art of teaching computers to learn from data without explicit programming. Imagine it as giving a computer a vast library and the ability to find patterns and insights on its own, allowing it to predict the future or make decisions based on what it has learned. Just as there are different ways for a student to learn, ML encompasses various techniques (i.e., learning models) like supervised learning (learning with a teacher), unsupervised learning (exploring independently), and reinforcement learning (learning through trial and error).

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What is deep learning?

Consider deep learning as a sophisticated engine within the machine learning toolkit. Inspired by the structure and organization of biological neurons in the human brain, it uses artificial neural networks with many layers to process complex information. It's like having a highly skilled detective who can sift through mountains of unstructured clues — like images, speech, or text — and uncover hidden connections without needing explicit instructions on what to look for.

What is generative AI (GenAI)?

This is the creative wing of AI. Generative AI models are like digital artists or writers, capable of producing entirely new content — text, images, audio, code, and more — by learning the underlying patterns in vast datasets. Tools like ChatGPT and DALL-E have democratized this capability, allowing many to witness the power of AI to bring new creations into existence.

What is reinforcement learning?

Imagine training a dog with treats and corrections. Reinforcement learning works similarly, where an AI agent learns by interacting with its environment and receiving rewards or penalties for its actions. It's the driving force behind teaching robots new tricks, mastering complex games, and developing autonomous systems that can learn optimal behaviors through trial and error.

What is multimodal AI?

Think of human understanding as a rich tapestry woven from different senses. Multimodal AI strives for a similar holistic understanding by analyzing various data streams — like images, text, sounds, and location data — simultaneously. It's like having a super-perceptive AI that can see the object, read the description, recognize the speaker, know the location, and provide a much richer and more nuanced understanding of a situation.

What is bias (of AI models)?

In the context of AI models, bias refers to systematic errors or tendencies in the model's predictions or outcomes that unfairly favor certain groups, individuals, or concepts over others. It's worth distinguishing this from bias terms which are not inherently "good" or "bad," but often learned (bias often refers to the additive components of a model's parameters. This bias often originates from skewed or unrepresentative data used during training, but can also be introduced through model design or flawed assumptions. It can lead to unfair, discriminatory, or inaccurate results.

Large Language Models (LLMs) & Related Concepts

What is a large language model (LLM)?

An LLM is a powerhouse of language processing. Trained on colossal amounts of text data, it's like a digital polyglot and storyteller combined. With billions of parameters, it can understand and generate human-like text for tasks like writing, translating, text summarization, and answering questions.

What is an AI hallucination?

In the realm of LLMs, hallucination is when the model confidently fabricates information, presenting falsehoods as truth. It's like a vivid dream that feels real but has no basis in reality. These made-up outputs can sound convincing, making it crucial to verify their accuracy.

What is retrieval augmented generation (RAG)?

RAG is like giving an LLM an open-book exam. It enhances the model's ability to answer questions accurately by first retrieving relevant information from external knowledge sources and then using that context to generate its response. This grounding in facts helps to prevent hallucinations and allows the LLM to access information beyond its initial training.

What is a token?

Think of tokens as the fundamental building blocks of language for LLMs. Just as sentences are made of words, LLMs process text by breaking it down into these smaller units, which can be whole words, parts of words, or even punctuation. The number of tokens influences how much information an LLM can process at once and the computational cost involved. Furthermore, the seemingly abrupt or natural cessation of an LLM's output, even when ample generation capacity remains, often signifies the model's learned prediction of a specific "halt" token, indicating a natural and contextually appropriate endpoint to its response. This nuanced interplay of tokens underscores their integral role in orchestrating the intricate dance of language generation and interaction within these sophisticated systems.

What does temperature mean?

In text generation, temperature is the knob that controls the LLM's creativity. A low temperature makes the output focused and predictable, like a careful scientist sticking to well-established facts. A high temperature introduces more randomness and surprise, potentially leading to novel and imaginative text, but also increasing the risk of nonsensical outputs.

What is reasoning (as it relates to LLMs)?

When a large language model "reasons," it's essentially drawing connections and solving problems based on the patterns it has learned from its vast training data. While it can achieve impressive feats that appear logical, its reasoning is more akin to recognizing and applying learned patterns rather than possessing genuine understanding or common sense in the human way.

What are transformers?

Standing for the "T" in "GPT" (generative pre-trained transformer), transformers are the architectural marvels behind many modern LLMs. Think of them as highly efficient information processors designed for sequential data like text. Their "attention mechanisms" allow them to focus on the most relevant parts of the input, enabling them to understand context and relationships effectively.

Data Representation & Model Optimization

What do embedding, vectorizing, and vectors mean?

Imagine taking words, images, or even entire documents and turning them into meaningful numerical codes (numeric vectors where their position in space partly encodes their meaning) — that's what embedding or vectorizing does. These numerical representations, called vectors, capture the essence and relationships of the original data in a way that AI models can understand and manipulate mathematically. They are the secret language that allows AI to find similarities, differences, and connections between different pieces of information.

What is a mixture of experts (MoE)?

Think of an MoE model as a team of specialized professionals, each an "expert" in a particular area. When faced with a problem, a "gating network" acts like a manager, directing the task to the most suitable expert(s) to handle it. This allows for greater efficiency and specialization within a single AI model.

What are fine-tuning and LoRA fine-tuning?

Fine-tuning is like taking a well-trained athlete and giving them specific coaching for a particular sport. It involves further training a pre-existing AI model on a smaller, task-specific dataset to adapt its skills. LoRA fine-tuning is a more efficient way to do this, like making subtle adjustments to the athlete's technique without completely retraining them.

What do quantization and pruning mean?

These are techniques for making AI models leaner and faster. Quantification is like using smaller units of measurement to represent the same information, making the model more efficient. Pruning is like removing unnecessary branches from a tree, simplifying the model without losing its core functionality.

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What is knowledge distillation?

Imagine a wise teacher sharing their knowledge with a younger student. Knowledge distillation involves transferring the insights and capabilities of a large, complex "teacher" AI model to a smaller, more efficient "student" model, allowing the student to perform well with fewer computational resources.

AI Agents & Environmental Interaction

What are AI agents?

Think of AI agents as autonomous problem-solvers. They are AI entities designed to perform tasks on behalf of a user or system without constant human intervention. From chatbots answering your questions to autonomous drones navigating the skies, they act and make decisions to achieve specific goals.

What is a world model?

A world model is like an AI's internal sandbox or mental map of its environment. It's a simulated representation that allows the AI to predict the outcomes of its actions and make more informed decisions. This is crucial for robots learning to navigate the real world or AI agents strategizing in complex environments.

What is embodied AI?

Embodied AI is about giving AI a physical presence and the ability to interact with the real world through sensors and actuators. Think of robots that can see, move, and manipulate objects. It's about grounding intelligence in physical experience, allowing AI to learn and understand the world in a more human-like way.

Emerging Artificial Intelligence Concepts

What is intent?

In conversational AI, understanding intent is like grasping the true meaning behind someone's words. When a user says, "Turn down the volume," the AI needs to recognize the underlying intent: to decrease the sound level. Identifying this purpose is fundamental for building intelligent and responsive language-based applications.

What are JEPA and V-JEPA?

These are innovative approaches to how AI learns by predicting relationships between different pieces of data. V-JEPA extends this to the complex world of video, aiming to teach machines to understand sequences of visual information, much like how humans learn by watching and interpreting events over time.

What are diffusion models?

Imagine creating a picture by starting with pure noise and gradually removing the static until a clear image emerges. Diffusion models work similarly to generate data, learning to reverse a process of gradual degradation. They've become a powerful tool for creating realistic and high-quality images.

Deepen Your Mastery of AI Terminology

Take what you've learned in this glossary and connect it to real-world examples by exploring our Data & AI Hub. There, you'll see these concepts applied to real-life solutions, from AI driven-software development slashing time and cost to agentic AI automating enterprise workflows.

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