Your comprehensive guide to artificial intelligence terminology - from beginner basics to advanced concepts.
A theoretical form of AI that would possess human-level intelligence across all domains, capable of understanding, learning, and applying knowledge in any context. Currently, we only have narrow AI (specialized for specific tasks). AGI remains a future goal, not a present reality.
Technology that enables machines to perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, making decisions, and solving problems. Modern AI is powered by machine learning and large datasets.
An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specific goals. Examples include virtual assistants, chatbots, and automated trading systems.
The study and practice of developing AI systems responsibly, addressing concerns about bias, fairness, privacy, transparency, accountability, and societal impact.
When an AI generates information that sounds plausible but is actually false or nonsensical. Common with language models that confidently produce incorrect facts, fabricated sources, or made-up statistics. Always fact-check important AI outputs.
A set of step-by-step instructions or rules that a computer follows to solve a problem or complete a task. In AI, algorithms process data to learn patterns and make predictions.
The challenge of ensuring AI systems behave in ways that match human values and intentions. A critical concern as AI becomes more powerful and autonomous.
The AI safety company that created Claude. Founded by former OpenAI members, Anthropic focuses on building reliable, interpretable, and steerable AI systems.
A set of tools and protocols that allows different software applications to communicate with each other. AI APIs let developers integrate AI capabilities (like ChatGPT or Claude) into their own applications.
A technique in neural networks (especially transformers) that allows models to focus on relevant parts of input data when making predictions. Critical to how modern language models understand context.
AI systems that can operate and make decisions independently without human intervention. Examples include self-driving cars and automated trading systems.
Systematic errors or unfair outcomes in AI systems, often reflecting biases present in training data. Can lead to discriminatory results based on race, gender, age, or other factors. Addressing bias is a major challenge in AI development.
A language model developed by Google that reads text bidirectionally (considering words before and after a given word) to better understand context and meaning.
Extremely large datasets that are too complex for traditional data processing methods. AI and machine learning thrive on big data, using it to identify patterns and make predictions.
An AI system whose internal decision-making process is not transparent or easily understood by humans. Deep learning models are often considered black boxes because it's difficult to explain exactly how they reach specific conclusions.
An AI program designed to simulate conversation with human users. Can be rule-based (following scripts) or AI-powered (using natural language processing). Examples include customer service bots and virtual assistants.
Anthropic's AI assistant, known for accuracy, nuanced understanding, and strong performance on complex reasoning tasks. Named after Claude Shannon, the father of information theory.
AI technology that enables machines to interpret and understand visual information from images and videos. Applications include facial recognition, medical imaging analysis, and autonomous vehicles.
An AI's ability to comprehend the meaning and nuances of text or speech based on surrounding information. Advanced models like GPT-4 and Claude have strong contextual understanding.
A type of neural network particularly effective for processing visual data. Used extensively in image recognition, video analysis, and computer vision applications.
A personalized version of ChatGPT created for specific tasks or domains. Users can configure instructions, knowledge bases, and capabilities to suit particular use cases.
The process of discovering patterns, correlations, and insights within large datasets using AI and statistical methods.
An interdisciplinary field combining statistics, mathematics, programming, and domain expertise to extract insights from data. AI and machine learning are core tools in data science.
A subset of machine learning using artificial neural networks with multiple layers (hence "deep"). Particularly effective for complex tasks like image recognition, natural language processing, and game playing. Powers most modern AI breakthroughs.
A type of generative AI that creates images by starting with random noise and progressively refining it into coherent outputs. Used by tools like Stable Diffusion and Midjourney.
Mathematical representations of words, sentences, or other data as vectors (arrays of numbers) that capture semantic meaning and relationships. Allow AI to understand that "king" and "queen" are related, for example.
Unexpected capabilities that appear in AI systems as they scale up, not explicitly programmed or predicted. Large language models have shown emergent abilities like reasoning and problem-solving.
An early form of AI that uses a knowledge base and inference rules to solve problems in specific domains. Mimics the decision-making ability of human experts.
AI's ability to learn new tasks from just a few examples. Modern language models can adapt to new tasks with minimal training data by leveraging their general knowledge.
The process of taking a pre-trained AI model and training it further on specific data to specialize it for particular tasks or domains. Makes general-purpose models more effective for specific applications.
Large-scale AI models trained on vast amounts of data that serve as a base for various applications. GPT-4, Claude, and PaLM are foundation models that can be adapted for many tasks.
AI systems that create new content (text, images, music, code, etc.) rather than just analyzing existing data. ChatGPT, DALL-E, and Midjourney are examples of generative AI.
OpenAI's series of large language models. "Generative" means it creates text, "Pre-trained" means it learned from vast amounts of text data, and "Transformer" refers to the neural network architecture it uses.
Originally designed for rendering graphics, GPUs are now essential for training AI models due to their ability to perform massive parallel computations efficiently.
An optimization algorithm used to train neural networks by iteratively adjusting parameters to minimize errors in predictions.
See "AI Hallucination" - when AI generates false or nonsensical information with confidence.
An approach where humans provide feedback and guidance to AI systems during training or operation, improving accuracy and addressing edge cases.
Settings that control how an AI model learns, set before training begins. Examples include learning rate, batch size, and number of layers in a neural network.
The process of using a trained AI model to make predictions or generate outputs on new data. The "production" phase after training is complete.
An AI's ability to understand and execute commands or requests. Modern language models like ChatGPT and Claude are optimized for instruction following.
Using AI to enhance human capabilities rather than replace them. The philosophy that AI should augment human intelligence, not substitute for it.
Attempts to bypass an AI system's safety guidelines or restrictions to make it produce prohibited content. Responsible AI developers work to prevent jailbreaking.
A structured collection of information that AI systems can reference to answer questions or make decisions. Used in chatbots, expert systems, and retrieval-augmented generation.
A network of interconnected information showing relationships between entities (people, places, concepts). Helps AI systems understand context and connections.
Google's conversational AI technology, designed for natural, open-ended dialogue. Powers Google's Bard chatbot.
AI models trained on massive amounts of text data to understand and generate human-like language. Examples include GPT-4, Claude, PaLM, and LLaMA. The foundation of modern conversational AI.
A compressed mathematical representation of data that captures essential features while reducing dimensionality. Where AI models "understand" patterns and relationships.
The ability for AI systems to remember and reference information from previous interactions over extended periods. Currently limited in most AI assistants.
A subset of AI where systems learn from data and improve performance over time without being explicitly programmed for every scenario. The foundation of modern AI.
A popular AI image generation tool that creates artwork from text descriptions. Known for producing highly artistic and stylized images.
The mathematical representation trained by machine learning algorithms to make predictions or generate outputs. The "brain" of an AI system.
AI systems that can process and generate multiple types of data (text, images, audio, video) rather than just one. GPT-4 with vision is an example of multimodal AI.
The branch of AI focused on enabling computers to understand, interpret, and generate human language. Powers chatbots, translation, sentiment analysis, and more.
A subset of NLP focused specifically on comprehending the meaning and intent behind human language, including context, sentiment, and nuance.
A computing system inspired by biological neural networks in the brain. Consists of interconnected nodes (neurons) organized in layers that process information. The foundation of deep learning.
The sequence of steps used to process natural language, including tokenization, parsing, entity recognition, and semantic analysis.
When an AI model learns the training data too well, including noise and anomalies, causing poor performance on new, unseen data. Like memorizing test answers instead of understanding concepts.
A value that the AI model learns during training. Larger models have more parameters (GPT-4 has trillions), generally enabling more sophisticated capabilities.
The initial phase where an AI model learns from vast amounts of data before being fine-tuned for specific tasks. Foundation models undergo extensive pre-training.
The input text you give to an AI system to generate a response. In ChatGPT or Claude, your prompt is your question or instruction.
The skill of crafting effective prompts to get optimal results from AI systems. Involves specifying context, format, tone, and constraints to guide AI outputs.
The most popular programming language for AI and machine learning development due to its simplicity and extensive AI libraries (TensorFlow, PyTorch, scikit-learn).
Reducing the precision of numerical representations in AI models to make them smaller and faster, with minimal impact on performance. Important for deploying AI on devices with limited resources.
A technique where AI systems retrieve relevant information from external sources before generating responses, improving accuracy and reducing hallucinations.
The ability of AI to draw logical conclusions, solve problems, and make inferences. Advanced models like GPT-4 and Claude demonstrate improved reasoning capabilities.
A type of neural network designed to process sequential data by maintaining memory of previous inputs. Used for time series, speech recognition, and language tasks before transformers became dominant.
A machine learning approach where AI learns by trial and error, receiving rewards for desired behaviors and penalties for undesired ones. Used to train game-playing AI and robotics.
A training method where human evaluators rank AI outputs, and the model learns to prefer responses humans rate more highly. Used to align AI behavior with human preferences.
Search technology that understands the meaning and intent behind queries, not just keyword matching. AI-powered semantic search provides more relevant results.
Using AI to determine the emotional tone (positive, negative, neutral) of text. Used for analyzing customer feedback, social media monitoring, and brand perception.
An open-source AI image generation model that creates images from text descriptions. Notable for being freely available and runnable on consumer hardware.
Machine learning where the AI is trained on labeled data (inputs with known correct outputs). The model learns to map inputs to outputs based on examples.
Artificially generated data used to train AI models when real-world data is limited, expensive, or privacy-sensitive.
A parameter controlling randomness in AI text generation. Lower temperature (0.1-0.5) makes outputs more focused and deterministic; higher temperature (0.8-1.0) makes them more creative and varied.
Google's open-source machine learning framework, widely used for building and training AI models.
The basic unit of text that language models process. Can be a word, part of a word, or punctuation. "Tokenization" breaks text into these units. Models have token limits (e.g., GPT-4 can process ~8,000-32,000 tokens).
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to minimize errors. Can take weeks or months for large models.
Using knowledge gained from training on one task to improve performance on a related task. Allows pre-trained models to be adapted for new applications efficiently.
A neural network architecture introduced in 2017 that revolutionized NLP. Uses attention mechanisms to process text efficiently. Powers GPT, Claude, BERT, and most modern language models.
A test proposed by Alan Turing to determine if a machine can exhibit intelligent behavior indistinguishable from a human. If a person can't tell whether they're conversing with a human or machine, the machine passes.
When an AI model is too simple to capture patterns in the data, resulting in poor performance on both training and new data. The opposite of overfitting.
Machine learning where AI finds patterns in data without labeled examples or explicit instructions. Used for clustering, anomaly detection, and discovering hidden structures.
A specialized database that stores and retrieves high-dimensional vectors (embeddings) efficiently. Essential for semantic search and retrieval-augmented generation.
Applying transformer architecture (originally designed for text) to image processing. Demonstrates that transformers are versatile beyond language tasks.
Numerical values in a neural network that determine the strength of connections between neurons. Adjusted during training to improve model performance.
AI systems designed to provide understandable explanations for their decisions and outputs, addressing the "black box" problem. Important for trust, accountability, and regulatory compliance.
A real-time object detection system that can identify and locate multiple objects in images or video in a single pass. Used in autonomous vehicles, surveillance, and robotics.
An AI's ability to perform tasks it wasn't explicitly trained for, without any examples. Modern language models can often accomplish new tasks through clever prompting alone.
OpenAI's conversational AI, available in free and Plus ($20/month) versions. GPT-4 (paid) offers significantly better reasoning and accuracy than GPT-3.5 (free).
Anthropic's AI assistant, known for accuracy and sophisticated reasoning. Available in free and Pro versions.
AI image generation accessible via Discord. Subscription starts at $10/month. Known for artistic, stylized outputs.
OpenAI's image generator, integrated into ChatGPT Plus and available via API.
Open-source image generation, free to use and can run on your own hardware.
AI pair programmer that suggests code as you type. $10/month for individuals.
AI writing assistant focused on marketing copy and content creation. Starting at $49/month.
AI copywriting tool for marketing, social media, and business content. Free tier available.
AI features integrated into Notion workspace. $10/month.
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