Navigating the world of artificial intelligence shouldn’t require a computer science degree. Our comprehensive glossary, AI Words, serves as your trusted companion through the complex terminology that shapes the AI industry.
From fundamental concepts like machine learning and neural networks to cutting-edge terms like prompt engineering, we break down each definition into clear, accessible language. Updated regularly to reflect the latest developments in AI technology, this carefully curated resource helps you build confidence in AI literacy, whether you’re a curious beginner or a seasoned professional.
Enjoy our knowledge packed list of terms below.
The process of ensuring AI systems behave in accordance with human values and intentions. This includes both technical methods and ethical considerations to make AI systems helpful, honest, and safe.
To enhance both safety and reliability, the team developed alignment techniques that enabled their AI to deliver precise, factual information while automatically detecting and refusing requests that could cause harm to humans.
The tendency to attribute human characteristics, behaviors, or emotions to AI systems, potentially leading to misconceptions about their capabilities and nature.
Users began anthropomorphizing the AI assistant, assuming it had feelings and personal experiences, which required careful correction by the development team.
Refers to AI systems that can understand, learn, and apply knowledge across different domains at a human level or beyond, rather than excelling at just specific tasks.
Consider today’s personal assistant AI versus Marvel’s J.A.R.V.I.S. Current AI assistants can schedule meetings, set reminders, and answer predefined questions. J.A.R.V.I.S., representing AGI, can independently learn new skills, engage in complex problem-solving, and apply knowledge across countless situations without additional programming. It can seamlessly shift from managing Tony Stark’s calendar to analyzing scientific data to designing new technology, much like how a human can adapt their knowledge and skills across different challenges.
The simulation of human intelligence by machines, especially computer systems. AI includes tasks like learning, reasoning, problem-solving as well as creative tasks like image, audio and video creation.
Using deep learning algorithms and a comprehensive dataset of labeled bird images, the AI system learned to identify distinguishing features like wing patterns, beak shapes, and plumage colors, achieving 98% accuracy across both common and rare species.
A component of certain neural network architectures, like Transformers, that allows the model to focus on specific parts of the input data when making predictions or generating responses.
When translating “The bank is by the river,” the attention mechanism focused on the context to correctly interpret “bank” as a riverbank rather than a financial institution.
Systematic patterns of error introduced into a model, often due to biases present in the training data. This can affect the fairness and accuracy of the model’s predictions.
A recruitment AI showed bias by consistently ranking male candidates higher than equally qualified female candidates due to historical hiring patterns in its training data.
Techniques and strategies used to reduce or eliminate bias in AI models. This can involve changing the training data, modifying the algorithm, or adjusting the way the model interprets data.
The team implemented data balancing techniques to ensure their facial recognition system performed equally well across all ethnic groups.
A prompting technique that encourages LLMs to break down complex problems into smaller, logical steps, often leading to more accurate results. Similar to how a math teacher asks students to show their work, this approach guides the AI to reveal its reasoning process.
Prompting and LLM: “Please analyze my garden’s health by: 1) evaluating the soil condition, 2) examining leaf discoloration patterns, 3) considering recent weather patterns, and 4) suggesting potential solutions based on these factors.
The surrounding text or information that helps a model understand the meaning of a given word or phrase.
The model used the context of a sports article to understand that “court” referred to a basketball court rather than a legal institution.
A statistical method used in machine learning to assess the performance of a model. It involves dividing the data into training and test sets multiple times to ensure that the model generalizes well to unseen data.
The researchers used 5-fold cross-validation to ensure their prediction model performed consistently across different subsets of their medical data. This rigorous validation approach helped identify and mitigate any potential biases or overfitting issues.
AI systems that learn to create images by gradually removing noise from random patterns. Unlike GANs, they work by slowly refining random noise into clear images through a series of small, incremental improvements.
Imagine a time-lapse video of fog clearing from a landscape. The process starts with a completely foggy scene (pure noise), and step by step, the fog slowly dissipates to reveal a detailed image. This is how models like DALL-E and Stable Diffusion work.
Low-dimensional, dense vector representations of words or phrases that capture their meaning in a way that can be processed by machine learning models.
The embedding space placed the words “happy” and “joyful” close together, while “happy” and “sad” were far apart, reflecting their semantic relationships.
The study and application of ethical principles to the development and use of AI technologies. This includes considerations of fairness, transparency, and accountability.
The company established an AI ethics board to review all new AI implementations for potential impacts on privacy and fairness.
The ability of AI models to provide understandable explanations for their decisions or outputs. This is critical for ensuring trust and transparency in AI systems.
The medical AI system highlighted the specific regions of an X-ray that led to its diagnosis, making its decision process clear to doctors.
Adjusting a pre-trained model on a smaller, specific dataset to make it perform better on particular tasks or in certain domains.
The customer service chatbot was fine-tuned on company-specific documentation to better answer questions about their products and policies.
AI systems that pit two neural networks against each other: one network creates content (the generator), while the other evaluates it (the discriminator). This competitive process helps the generator produce increasingly realistic outputs.
A GAN is like an art student and an art teacher. The student (generator) creates portraits of people who don’t exist, while the teacher (discriminator) critiques each image against real photos.
A type of LLM developed by OpenAI. GPT models are pre-trained on a large dataset and can generate human-like text based on the input they receive.
When you type “The Eiffel Tower is located in…” the model, having learned from millions of documents, can confidently complete the sentence with “Paris, France” and continue with relevant details about its height, construction date, or cultural significance.
Graph Neural Networks (GNNs) are specialized deep learning models designed to work with data structured as graphs, where information is represented as nodes (points) connected by edges (relationships).
Using LinkedIn as an example, a GNN could analyze networks where each person represents a node in the graph, while their connections form the edges between these nodes. Every person’s node contains vital information such as their job title, skills, and location, while the edges between them carry important details about how people are connected.
When an AI model generates information that is false, inconsistent, or not supported by its training data, presenting it as if it were factual.
The LLM exhibited hallucination when it confidently cited a non-existent research paper to support its argument.
The process of using a trained model to make predictions or generate text based on new input data.
The speech recognition system performed inference in real-time, converting the speaker’s words into text as they spoke.
A specific type of fine-tuning where models are trained to follow human instructions and commands, improving their ability to understand and execute user requests.
After instruction tuning, the model became much better at following specific formatting requirements in its responses.
A type of artificial intelligence designed to understand and generate human-like text. These models are trained on vast amounts of text data to predict and produce language.
The LLM demonstrated its versatility by translating text, answering questions, and writing code all within the same conversation.
The compressed, multidimensional space where AI models represent and manipulate concepts and information learned during training.
The AI system organized similar concepts close together in its latent space, allowing it to make meaningful associations between related ideas.
A subset of AI where machines learn from data and improve their performance over time without being explicitly programmed for every task.
When you first Spotify, recommendations are generic. But as you listen, skip, and like different songs, the ML algorithm learns your preferences – noticing you enjoy indie rock in the morning and instrumental music while working. Without being specifically programmed for your taste, it naturally becomes better at predicting what music you’ll enjoy, even suggesting new artists that match your listening patterns.
A field of AI that focuses on the interaction between computers and humans through natural language. NLP includes tasks like understanding, interpreting, and generating human language.
Consider Gmail…As you type “I’m attaching the report,” but forget to add the attachment, the NLP system understands the context of your message and prompts you with “Did you mean to attach a file?” The system isn’t just matching keywords – it’s actually comprehending the meaning of your sentence and recognizing the discrepancy between your stated intention and actions, just as a human reader would.
A computer system modeled on the human brain’s network of neurons. It consists of layers of interconnected nodes (neurons) that process data in a way similar to the human brain.
For self-driving cars. Its neural network processes input from cameras much like your brain processes vision. The first layer might detect basic edges and shadows, the next layer recognizes shapes like circles and rectangles, and deeper layers combine these to identify specific objects – distinguishing between stop signs, pedestrians, and traffic lights. Through millions of examples, the system learns to make increasingly accurate decisions.
A modeling error that occurs when a machine learning model learns not only the underlying pattern in the data but also the noise. As a result, it performs well on training data but poorly on new, unseen data.
The model achieved 99% accuracy on training data but only 70% on new data, indicating it had overfit to the training examples.
A variable in a model that the training process adjusts to improve the model’s performance. LLMs can have billions or even trillions of parameters.
A master chef is making a sauce recipe. Just as the chef adjusts countless variables – the heat level, ingredient ratios, timing, and seasoning – an AI model tunes billions of parameters to get things right. Each parameter is like one of these cooking variables, and just as more expertise (parameters) allows a chef to create more sophisticated dishes, more parameters enable an AI model to better understand nuance, context, and complexity in language.
The estimated probability of artificial intelligence leading to catastrophic outcomes or human extinction, often discussed in AI safety and existential risk contexts.
Consider calculating the risk of a natural disaster like a major earthquake in a specific region. Geologists analyze various factors: fault lines, historical data, tectonic activity, and building codes to estimate the probability of a devastating event. Similarly, AI researchers assess P(doom) by examining multiple variables: the rate of AI advancement, robustness of safety measures, potential failure modes, and human control mechanisms.
The initial phase in training a model on a large dataset, allowing it to learn general features before fine-tuning it for specific tasks.
The model was pre-trained on millions of web pages to develop a broad understanding of language before being specialized for legal document analysis.
The initial input or question given to an LLM to generate a response. The quality and clarity of the prompt can significantly impact the output.
The data scientist refined her prompt from “Tell me about trees” to “Explain the carbon capture process in deciduous trees” to get more specific information.
The skill of crafting effective inputs for AI models to achieve desired outputs, including techniques for clarity, specificity, and context-setting.
Through careful prompt engineering, she was able to get the AI to generate more focused and relevant responses for her research questions.
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.
When a dog first learns to fetch, the dog makes mistakes – running in wrong directions or not returning the ball. Through consistent rewards for correct actions the dog gradually learns the optimal behavior: chasing the right ball, grabbing it, and bringing it back. Similarly, a reinforcement learning AI might learn to play Pac-Man by repeatedly trying different moves, receiving points for eating dots and avoiding ghosts, until it masters the game’s strategies.
An advanced machine learning technique that combines reinforcement learning with human guidance to train artificial intelligence systems. It aims to align AI behavior more closely with human values and preferences.
Imagine teaching a virtual assistant to write emails. Initially, the AI might write technically correct but overly formal or robotic messages. Through RLHF, human reviewers rate different email responses, preferring those that strike the right tone – professional yet friendly, clear yet concise. The AI learns from these preferences, much like a new employee learning communication style from feedback by experienced colleagues.
A technique that combines an LLM’s generative capabilities with the ability to retrieve and reference specific information from external sources, improving accuracy and reducing hallucinations.
When a librarian answering questions…Instead of relying solely on memory (like a standard LLM), a RAG-enabled AI acts like a librarian who first consults their catalog and relevant books before responding. When asked about a company’s return policy, rather than generating an answer based on general training, it first checks the current policy document, then formulates a response using both its language skills and the specific, verified information.
A search method that understands the intent and contextual meaning of search queries rather than just matching keywords, often using embeddings to find relevant results.
When searching for dinner recipes, a traditional search might only find recipes with the exact phrase “quick healthy dinner,” a semantic search understands that “nutritious meals I can make in 30 minutes” means the same thing. It recognizes that “pasta with vegetables” might be relevant to a search for “carb-rich meatless meals” – even though these phrases share no common words.
The Singularity refers to a hypothetical future point when artificial intelligence surpasses human intelligence, leading to rapid, uncontrollable technological growth and profound changes in civilization.
Imagine teaching a child mathematics. At first, you help them learn basic arithmetic, then algebra, then calculus. Now imagine if suddenly that child could not only learn advanced mathematics instantly but could also teach themselves every other subject simultaneously and create entirely new fields of study faster than humans could understand them. The Singularity represents a similar leap for AI – a point where artificial intelligence becomes capable of improving itself so rapidly that its growth becomes exponential, fundamentally transforming technology and society in ways we can’t predict or comprehend.
A parameter that controls the randomness or creativity in an AI model’s outputs. Higher temperatures lead to more diverse and creative responses, while lower temperatures produce more focused and deterministic outputs.
Think of temperature like a coffee barista’s approach to drink orders. At low temperature (0.2), the barista makes drinks exactly by the recipe book – every latte is consistently the same predictable results. At high temperature (0.8), the barista gets creative – experimenting with different flavors and combinations. While this might produce some unique and delightful drinks, it could also result in some unusual combinations.
Methods for controlling text generation by limiting the pool of possible next tokens either to the k most likely options (top-k) or to the smallest set whose cumulative probability exceeds p (top-p).
Imagine a jazz musician improvising their next note. Top-k is like limiting them to only the 5 most common notes that typically follow in that musical scale. Top-p is more dynamic – it’s like telling the musician “choose from notes that make up 80% of what would sound harmonious here.” While top-k strictly limits choices to the most common options, top-p adapts based on context, allowing for occasional creative flourishes when they make sense.
The large set of text or data used to teach an AI model. For LLMs, this data can include books, articles, websites, and more.
The model’s training data included over 45 million scientific papers, enabling it to understand and discuss complex research topics.
A technique where a model trained on one task is adapted for another task. In the context of LLMs, this often refers to using a pre-trained model for a related but distinct task.
The image recognition model trained on general photographs was successfully adapted to identify specific medical conditions in X-rays through transfer learning.
Introduced by Google in 2017, and now widely used in development of LLMs, a transformer is a powerful neural network architecture designed to process sequential data, such as text, by learning context and relationships between elements in the sequence.
Understanding that ‘bark’ means something different in ‘tree bark’ versus ‘dogs bark,’ transformer models excel at grasping context. When processing the sentence ‘After adding fuel to the rocket, it launched into orbit,’ the transformer architecture recognizes that ‘it’ refers to the rocket by analyzing the relationships between all words in the sequence.
Numerical parameters in neural networks that determine how strongly different inputs influence the output. These are adjusted during training to improve the model’s performance.
Imagine a basketball scout evaluating players. The scout assigns different levels of importance (weights) to various skills: shooting accuracy might get a weight of 0.8, height 0.6, speed 0.7, and teamwork 0.9. As the scout sees more successful players, they adjust these weights – perhaps realizing teamwork correlates more strongly with success than height. Similarly, a neural network adjusts its weights during training, learning which features deserve more attention to make better predictions.
The ability of a model to make predictions on a task without having been explicitly trained on it. This is often achieved by using general knowledge gained from pre-training on a vast dataset.
Consider a well-traveled person who speaks English and has never studied Spanish, but can still understand that “biblioteca” means “library” because they’ve seen the word on library signs and recognize its similarity to words like “bibliographic.” Similarly, an AI model might never have been specifically trained to identify smoothie recipes, but given its broad knowledge of ingredients, food preparation, and the concept of blended drinks, it can still accurately suggest which combinations would make a good smoothie.
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