We’ve created a glossary that explains the most important AI terms in plain English. If you ever come across a term you’re not sure about, you can quickly look it up there.
We’ve created a glossary that explains the most important AI terms in plain English. If you ever come across a term you’re not sure about, you can quickly look it up there.
An agent is AI software that can work independently to complete tasks without constant human supervision, using tools when it needs to, and taking actions on its own. Unlike chatbots that just respond to questions, agents can actively pursue goals and adapt their approach based on what they encounter.
An algorithm is simply a set of step-by-step instructions for completing a specific task. In AI, algorithms tell computers how to process data, recognize patterns, or make predictions. Data analysts use algorithms to organize information, while data scientists use them to build models that can learn from experience.
Alignment refers to making sure AI systems pursue goals that match human values and intentions. This is one of the biggest challenges in AI development. A system might technically accomplish what you asked for while causing unintended harm or missing the point entirely.
An API is a set of rules that lets different software programs talk to each other and share information. When you use an app that pulls in data from another service, an API is making that connection happen behind the scenes. In AI, APIs allow developers to integrate powerful AI capabilities into their applications without building everything from scratch. APIs are typically written in programming languages like C++ or JavaScript.
AGI refers to AI that can perform any intellectual task a human can do. Microsoft researchers have defined it as artificial intelligence that matches human capability in all intellectual tasks. We don't have AGI yet, and experts debate whether we're decades or centuries away from achieving it.
ASI describes AI that would surpass human intelligence across all domains. While AGI matches human capability, ASI would exceed it. This remains a theoretical concept, and experts debate whether it's even possible. If ASI were ever created, it would be able to solve problems and generate insights far beyond what any human could achieve.
Attention is a mechanism in neural networks that helps AI models focus on the most relevant parts of their input. When you read a long sentence, you naturally pay more attention to certain words based on context. AI models use attention mechanisms the same way. This technology is crucial for understanding language and has made modern AI much more effective at processing complex information.
Bias in AI refers to systematic errors or unfair outcomes caused by flawed training data. If an AI model learns from data that reflects historical prejudices or incomplete information, it will reproduce those problems in its predictions.
Big data refers to extremely large datasets that reveal patterns and trends when analyzed. Organizations can now collect massive amounts of complex information using modern data collection tools. Big data powers many AI applications because machine learning models need huge amounts of information to learn effectively.
Chain-of-thought prompting is a technique where you ask an AI model to show its reasoning step-by-step before giving a final answer. This approach improves accuracy for complex problems like math questions or logical puzzles.
A chatbot is software designed to simulate human conversation through text or voice. Overchat AI, ChatGPT, and Claude are examples of chatbots. Chatbots use natural language processing to understand what you're asking and provide relevant responses.
CLIP is an AI model developed by OpenAI that connects images and text. It can understand visual content and generate descriptions, or do the reverse by finding images that match text descriptions. This technology bridges the gap between language and vision, allowing AI systems to work across both mediums.
Cognitive computing is essentially another term for AI, though it's often used in marketing contexts. It refers to computer systems that mimic human thought processes like understanding natural language, recognizing patterns, and learning from experience.
Compute refers to the computational resources needed to train or run AI models. This includes processing power from CPUs and GPUs, memory, and time. Training large AI models requires enormous amounts of compute, which is why major AI breakthroughs often come from organizations with access to massive computing infrastructure.
The context window is the maximum amount of text an AI model can process and remember at once. You can think of it as the model's working memory during a conversation. Models with larger context windows can handle longer documents, maintain coherence over extended conversations, and work with more complex inputs. This is measured in tokens, which are roughly equivalent to words or parts of words.
Data augmentation is the process of creating variations of existing training data to give AI models more examples to learn from. Instead of collecting entirely new data, you make slight modifications to what you already have. For image recognition, this might mean rotating pictures, adjusting brightness, or flipping images horizontally.
Data mining is the process of examining large datasets to discover patterns and extract useful insights. It's a core part of data analytics that helps businesses make informed decisions. Data mining techniques can reveal customer behavior trends, identify market opportunities, or detect fraud patterns.
Data science is an interdisciplinary field that combines statistics, programming, and domain expertise to extract knowledge from data. Data scientists use algorithms and analytical methods to find patterns in large datasets and make predictions. This field sits at the intersection of mathematics, computer science, and business intelligence.
Deep learning is a machine learning technique that uses artificial neural networks with multiple layers. These networks are inspired by the structure of the human brain and can learn complex patterns from data. Unlike simpler machine learning approaches, deep learning models can improve their accuracy through repetition without human intervention.
Diffusion is a technique for generating new content by learning to reverse a noise-adding process. A diffusion model starts with real data, adds random noise to it, then trains a neural network to predict how to remove that noise. Once trained, the model can generate entirely new images, audio, or other content that resembles the training data. This approach has become popular for creating high-quality AI-generated images.
An embedding is a way of representing data as numbers in a multi-dimensional space. Words, images, or other complex data get converted into vectors where similar items end up close together. For example, the words "king" and "queen" would have similar embeddings because they're related concepts. This mathematical representation allows AI models to understand relationships and meaning in data they process.
Emergent behavior refers to unexpected capabilities that AI systems develop, which weren't explicitly programmed. These abilities only appear when the system reaches a certain scale or complexity. Large language models have shown emergent behaviors like writing poetry, composing music, or solving logic puzzles, even though they weren't specifically trained for these tasks. This phenomenon both excites and concerns AI researchers.
End-to-end learning is a machine learning approach where models learn directly from raw input data without requiring hand-crafted features. Instead of humans telling the system which aspects of the data matter, the model figures this out on its own. You feed in raw data at one end, and the model produces results at the other end, learning everything in between automatically.
An expert system is AI software designed to solve complex problems within a specific domain by mimicking human expertise. These systems use knowledge bases and logical rules to make decisions or provide recommendations. Expert systems were popular in early AI and are still used in fields like medical diagnosis and financial planning, where they can encode decades of specialized knowledge.
Fine-tuning takes a pre-trained AI model and adapts it for a specific task or domain. The model has already learned general patterns from a large dataset. You then train it further on a smaller, specialized dataset to improve its performance on your particular use case.
Forward propagation is how neural networks process information from input to output. Data enters the network and passes through each layer, where it gets transformed by weights and activation functions. This forward flow produces the network's prediction or response.
Foundation models are large AI systems trained on broad, diverse datasets that can be adapted for many different tasks. Instead of building specialized models from scratch, developers can fine-tune foundation models for specific applications. Examples include GPT, BERT, and CLIP.
GANs consist of two neural networks competing against each other to generate realistic content. One network creates fake data while the other tries to detect it. Through this adversarial process, the generator improves until it can create convincing images, videos, or other content.
Generative AI creates new content like text, images, music, or code based on patterns learned from training data. Unlike AI that simply analyzes or classifies information, generative AI produces original output. ChatGPT, DALL-E, and similar tools are examples of generative AI systems.
GPUs are specialized processors originally designed for rendering graphics but now essential for AI. They excel at performing many calculations simultaneously, making them much faster than regular CPUs for training neural networks. Most modern AI breakthroughs depend on GPU computing power.
Hallucination occurs when AI models generate false or nonsensical information while presenting it as fact. Large language models sometimes make up sources, create fake statistics, or describe events that never happened. This happens because models predict plausible-sounding text rather than retrieving verified information.
Hidden layers are the layers in a neural network between the input and output layers. They're hidden because they're not directly visible to users. These layers transform and process information, allowing the network to learn complex patterns and relationships in data.
Hyperparameter tuning is the process of selecting the best settings for a machine learning model before training begins. Unlike parameters that the model learns, hyperparameters are set by humans and control how the learning process works. Finding optimal hyperparameters significantly impacts model performance.
Image recognition is the ability of AI systems to identify objects, people, places, or text in images and videos. This technology powers applications from facial recognition on smartphones to automated quality control in manufacturing. Modern image recognition uses deep learning models trained on millions of labeled images.
Inference is the process of using a trained AI model to make predictions on new data. After a model has learned from training data, inference is when it actually does its job. Every time you ask ChatGPT a question or use face unlock on your phone, that's inference in action.
Instruction tuning is a fine-tuning technique where models learn to follow specific instructions given in prompts. Instead of just predicting text, instruction-tuned models understand commands like "summarize this," "translate that," or "explain this concept." This makes AI assistants more helpful and controllable.
Large language models are AI systems trained on massive amounts of text to understand and generate human-like language. They work by predicting what words should come next in a sequence. LLMs like GPT-4 and Claude can write essays, answer questions, and perform various language tasks.
Latent space is a compressed mathematical representation of data that AI models create internally. Similar concepts or images are positioned close together in this space. Understanding latent space helps explain how AI models organize and understand the patterns they've learned.
A loss function measures how wrong a model's predictions are during training. The model tries to minimize this loss by adjusting its internal parameters. Think of it as a score that tells the model how much it needs to improve.
Machine learning is a subset of AI where systems learn from data rather than following explicit programming. These systems identify patterns and improve their performance over time through experience. Machine learning powers everything from spam filters to recommendation systems.
Mixture of experts is a technique where multiple specialized AI models work together. Different "expert" models handle different types of inputs or tasks, and a gating mechanism decides which experts to use. This approach can be more efficient than using one massive model for everything.
Multimodal AI can process and generate multiple types of data like text, images, audio, and video. Instead of being limited to one format, multimodal models understand relationships across different media types. GPT-4 with vision is an example that can analyze both text and images.
Natural Language Processing enables computers to understand, interpret, and generate human language. NLP powers features like autocorrect, translation apps, and voice assistants. It's the technology that bridges the gap between how humans communicate and how computers process information.
A neural network is an AI model inspired by how the human brain works. It consists of interconnected nodes called neurons organized in layers. Each neuron processes information and passes signals to other neurons, allowing the network to learn complex patterns from data.
NeRF is a technique for creating 3D scenes from 2D images using neural networks. It can generate photorealistic views of objects or environments from angles not in the original photos. This technology enables applications in virtual reality, film production, and architectural visualization.
An objective function is what a machine learning model tries to optimize during training. It could be something the model wants to maximize (like accuracy) or minimize (like error). The objective function guides the entire learning process and determines what "success" means for the model.
Overfitting happens when a model learns the training data too well, including its noise and quirks. The model performs great on training examples but fails on new data it hasn't seen before. It's like memorizing test answers instead of understanding the underlying concepts.
Parameters are the internal variables that a machine learning model learns during training. In neural networks, parameters include the weights and biases that determine how the network processes information. Large language models can have hundreds of billions of parameters that capture their learned knowledge.
Pre-training is the initial phase where an AI model learns general patterns from large datasets before being adapted for specific tasks. During pre-training, the model develops foundational knowledge about language, images, or other data types. This base knowledge can then be fine-tuned for specialized applications.
A prompt is the input or instruction you give to an AI model to get a desired response. In text-based AI, prompts can be questions, commands, or context that guides the model's output. Well-crafted prompts significantly improve the quality and relevance of AI responses.
Prompt engineering is the practice of designing effective prompts to get better results from AI models. This involves choosing the right words, providing examples, setting constraints, and structuring requests strategically. Good prompt engineering can dramatically improve AI output quality without changing the underlying model.
RAG combines language models with external information sources like documents or databases. When you ask a question, the system retrieves relevant information first, then uses that content to generate an accurate answer. This helps AI models provide more current and factual responses.
Regularization techniques prevent models from overfitting by adding constraints during training. These methods discourage the model from becoming too complex or relying too heavily on specific patterns in the training data. Regularization helps models generalize better to new situations.
Reinforcement learning trains AI by having it learn from trial and error. The model takes actions in an environment and receives rewards for good choices and penalties for bad ones. Over time, it learns strategies that maximize rewards, similar to how humans learn from consequences.
RLHF improves AI models by incorporating human preferences into the training process. Humans rate different model outputs, and the system learns to generate responses that align with those preferences. This technique has been crucial for making chatbots more helpful and safer.
Supervised learning trains models using labeled data where the correct answers are provided. The model learns to map inputs to outputs by studying these examples. This approach works well for tasks like image classification or spam detection where you have lots of labeled training data.
Symbolic AI uses logic and rules to represent knowledge and solve problems. Unlike neural networks that learn patterns statistically, symbolic AI works with explicit symbols and relationships. Early AI systems relied heavily on this approach before machine learning became dominant.
Temperature controls how creative or deterministic an AI model's outputs are. Lower temperatures make responses more focused and predictable. Higher temperatures produce more varied and imaginative results but with less consistency.
A token is the basic unit of text that language models process. In English, a token is roughly three-quarters of a word or about four characters. Models break text into tokens to analyze and generate language efficiently.
Transformers are neural network architectures designed for processing sequential data like text. They use attention mechanisms to understand relationships between words regardless of their position in a sentence. Transformers power most modern language models including GPT and
BERT.
Transfer learning applies knowledge from one task to another related task. A model trained on general image recognition can be adapted for medical imaging with less additional training. This approach saves time and resources while achieving strong performance.
Underfitting occurs when a model is too simple to capture the underlying patterns in data. The model performs poorly on both training and new data because it hasn't learned enough. It's like trying to fit a straight line to data that curves.
Unsupervised learning finds patterns in data without labeled examples or predefined categories. The model explores the data structure on its own, discovering groupings or relationships. This approach is useful for customer segmentation, anomaly detection, and exploratory analysis.
Validation data is a separate dataset used to tune a model's settings during development. It's different from both training data (used to teach the model) and test data (used for final evaluation). Validation data helps you make decisions about model architecture and hyperparameters without overfitting.
Voice recognition enables computers to understand and interpret spoken language. Also called speech recognition, this technology converts audio of human speech into text or commands. Voice assistants like Siri and Alexa rely on voice recognition to understand what you're saying.
Zero-shot learning allows models to handle tasks they weren't explicitly trained on. The model uses its general knowledge to make predictions about completely new categories or situations. For example, a language model might translate between languages it never saw during training by understanding language patterns generally.
That covers the key AI terms! This collection gives you a solid foundation for understanding AI. It's alphabetically organized, but the concepts are interconnected, so you'll often see these terms used together as you explore AI further.
