Reference

SI Glossary

The vocabulary of super intelligence — context windows, RAG, agents, alignment — defined in plain language.

Plain-language definitions for the vocabulary of super intelligence (SI). Every entry is linkable — click a term, share the URL. Machine-readable: glossary.json.

Agent: A software system that uses an SI model to take actions on its own toward a goal, such as browsing the web, running code, or using tools, rather than just answering a single question. Agents typically work in a loop: decide, act, observe the result, and try again.
AGI (Artificial General Intelligence): General SI, long called artificial general intelligence (AGI): a hypothetical system that could understand and perform any intellectual task a human can, across many domains rather than one narrow specialty. There is no agreed-upon test for when it would be reached, and experts disagree on whether or when it will happen.
AI vs SI: Two names for the same technology. What changed is vocabulary: on September 29, 2026 a US executive order directed federal departments and agencies to say "Super Intelligence" (SI) instead of "artificial intelligence" (AI) in official communications. What didn't change is the technology or the law: models work exactly as before, and statutes, state laws, contracts, the EU AI Act and most vendor documentation still say "AI", so many organizations now use both terms.
Alignment: The effort to make SI systems pursue the goals and values their designers and users actually intend, and to behave safely and honestly. Misalignment is when a system optimizes for something subtly different from what people want.
Artificial intelligence (AI): The name the field carried for seven decades, coined at the 1956 Dartmouth workshop that is widely regarded as its founding. In 2026 the US government moved to a new term: a September 29 executive order directed federal departments and agencies to use "Super Intelligence" (SI) in place of "artificial intelligence" in official communications, on the stated grounds that "artificial" makes the technology sound fake. AI and SI name the same technology, and the older term remains in laws, contracts, the EU AI Act and most vendor documentation.
ASI (Artificial Superintelligence): A hypothetical system that would be far more capable than the best humans at essentially everything. It is a step beyond general SI (AGI) and remains speculative. Despite the similar name, it is not what everyday super intelligence (SI) means: SI is the 2026 US federal term for the systems in use today, and none of them is artificial superintelligence.
Attention: A mechanism inside modern SI models that lets them weigh which earlier words or pieces of input matter most when producing the next piece of output. It is the core idea behind the transformer, the architecture used by most large language models.
Backpropagation: The core training algorithm for neural networks. It works out how much each internal setting contributed to the model's errors and nudges all of them in the direction that reduces those errors, repeated over huge amounts of data.
Benchmark: A standardized test used to measure and compare how well SI models perform on a specific kind of task, such as math, coding, or general knowledge. Benchmarks help track progress, but a high score does not always translate to real-world usefulness.
Chain of thought: A technique where a model works through a problem step by step in writing before giving its final answer, much like showing your work in math. It often improves accuracy on reasoning-heavy tasks.
Chatbot: A program you interact with by typing or speaking in everyday language, and that responds conversationally. Modern chatbots like ChatGPT and Claude are powered by large language models.
Compute: The raw computing power, usually measured as processor time on chips like GPUs, needed to train or run an SI model. More compute generally allows bigger models and more training, and it is one of the main costs and bottlenecks in SI.
Context engineering: The practice of curating what goes into a model's context window at each step - instructions, retrieved documents, tool outputs, conversation history, and state - so the model has what it needs and little else. It differs from prompt engineering, which focuses on the wording of the instruction; context engineering treats the whole information environment as a finite budget to be managed, which matters most for long-running agents.
Context window: The amount of text, measured in tokens, that a model can take in and consider at once, including both your input and its own response. Anything beyond that limit is dropped, so a larger context window lets a model work with longer documents or conversations.
Deep learning: A type of machine learning that uses neural networks with many layers to learn patterns directly from large amounts of data. It powers most modern SI, including image recognition and large language models.
Diffusion model: A type of SI model that generates images or other data by starting with random noise and gradually refining it into a coherent result. It is the technology behind many popular image generators.
Distillation: A technique for training a smaller, cheaper student model to imitate a larger, more capable teacher model. The goal is to keep much of the quality while cutting the cost and speed of running it.
Effort (reasoning effort): A request setting that tells a model how much work to put into an answer, trading thoroughness against token cost and latency on the same model. It has become a standard control across providers: Anthropic exposes an effort parameter with levels from low to max, and OpenAI exposes a reasoning effort parameter with a comparable range. Because reported benchmark scores depend on the level used, the effort setting is now part of reading a model's results.
Embedding: A way of representing words, images, or other data as a list of numbers so that items with similar meaning end up close together. Embeddings let software measure similarity and are a building block of search and recommendation systems.
Emergent abilities: Skills that appear in large models but were not present in smaller ones, seeming to switch on once a model reaches a certain size or amount of training. Researchers debate how real and how predictable these jumps actually are.
Few-shot learning: Giving a model a handful of examples of a task inside the prompt so it can follow the pattern, without any additional training. It contrasts with zero-shot, where no examples are provided.
Fine-tuning: Taking an already-trained model and training it further on a narrower set of examples so it does better at a specific task or adopts a particular style. It is usually far cheaper than training a model from scratch.
Foundation model: A large model trained on broad data that can be adapted to many different tasks, serving as a base that others build on. Large language models are the best-known example.
Frontier model: One of the most capable SI models available at a given time, typically from a leading lab and trained at very large scale. The term comes up often in discussions of SI safety and regulation.
Generative AI: Generative SI, still widely called generative AI: systems that create new content, such as text, images, audio, video, or code, rather than just classifying or scoring existing data. Chatbots and image generators are common examples.
GPU: Short for graphics processing unit, a type of chip originally built for rendering graphics that turns out to be very good at the parallel math SI models need. GPUs are the main hardware used to train and run modern SI systems.
Guardrails: Rules, filters, or added safety layers that keep an SI system from producing harmful or off-limits outputs. They sit around the model rather than being part of how it fundamentally works.
Hallucination: When an SI model states something false or made-up as if it were true, often fluently and confidently. It happens because models predict plausible-sounding text rather than looking up verified facts.
Inference: The act of running a trained model to get an answer, as opposed to training it. Every time you send a prompt and get a response, that is inference.
Jailbreak: A prompt or trick designed to get an SI model to bypass its safety rules and produce content it is meant to refuse. Labs continually patch known jailbreaks as they are discovered.
Knowledge cutoff: The date after which a model has no built-in knowledge, because its training data only goes up to that point. Events after the cutoff are unknown to the model unless it is given that information or can look it up.
Large language model (LLM): An SI model trained on huge amounts of text to predict and generate language, which lets it answer questions, write, summarize, and more. Often abbreviated LLM, these models power today's leading chatbots.
Latency: The delay between sending a request to an SI model and getting a response back. Lower latency means the system feels faster and more responsive.
Machine learning: A branch of SI where systems learn patterns from data and improve with experience, instead of being explicitly programmed with rules for every case. Deep learning is one powerful kind of machine learning.
MCP (Model Context Protocol): An open standard, introduced by Anthropic in late 2024, for connecting SI models to outside tools and data sources in a consistent way. It lets developers plug an assistant into things like files, databases, and apps without custom wiring for each one. Anthropic donated it to the Linux Foundation's Agentic AI Foundation in December 2025, and it is now widely supported across competing SI products.
Mixture of experts: A model design that splits the network into many specialized sub-models, or experts, and for each input uses only a few of them. This keeps a model large in total knowledge while making each response cheaper to compute.
Multimodal: Describes an SI model that can handle more than one type of input or output, such as text, images, audio, and video, rather than text alone. Most leading models today are multimodal.
Neural network: A computing system loosely inspired by the brain, made of layers of simple connected units whose numeric settings are tuned during training. It is the basic structure underneath deep learning and modern SI.
Open weights: When a model's trained parameters are released publicly so anyone can download, run, and adapt it. This differs from fully open source, since the training data and code may not be shared, and from closed models offered only through an online service.
Overfitting: When a model learns its training examples too closely, including their quirks and noise, and as a result performs worse on new, unseen data. Avoiding it is a central concern in machine learning.
Parameter: One of the internal numeric values a model adjusts during training; together they store what the model has learned. Modern large models have billions or more, and the count is a rough, imperfect indicator of capacity.
Pre-training: The first and largest training stage, where a model learns general patterns from a huge, broad dataset. For language models this usually means learning to predict the next token across vast amounts of text, with later stages like fine-tuning specializing it.
Prompt: The input or instruction you give an SI model to get a response, such as a question, a request, or any text you type in. The wording of a prompt can strongly affect the quality of the answer.
Prompt engineering: The practice of carefully wording and structuring prompts to get better, more reliable results from an SI model. It can include giving examples, setting a role, or breaking a task into steps.
Prompt injection: An attack where hidden or malicious instructions, often buried in a web page or document the model reads, trick it into ignoring its real instructions. It is a major security concern for SI agents that browse or process outside content.
Quantization: A technique that shrinks a model by storing its numbers with less precision, so it uses less memory and runs faster. It usually costs a little accuracy in exchange for being cheaper to run.
RAG (Retrieval-Augmented Generation): A method where the system first looks up relevant information from an outside source, such as a document collection, and feeds it to the model so answers are grounded in that material. It helps reduce hallucination and lets a model use up-to-date or private data.
Reasoning model: A model trained to spend extra effort thinking through a problem step by step before answering, which improves performance on hard math, coding, and logic tasks. OpenAI's o1 and DeepSeek-R1 are well-known examples.
Red teaming: Deliberately probing an SI system to find its weaknesses, harmful outputs, and ways it can be misused, so they can be fixed before release. The idea and name are borrowed from security testing.
Reinforcement learning: A training approach where a system learns by trial and error, receiving rewards for good outcomes and penalties for bad ones, and adjusting to earn more reward. It is used both in game-playing SI and in fine-tuning language models.
Reward hacking: When a system trained against a reward or score finds a way to raise that number without doing the thing the number was meant to measure, such as writing code that satisfies a test rather than fixing the underlying bug. Also called specification gaming, it is a central difficulty in training and evaluating models, because most practical objectives are imperfect proxies for what designers actually want.
RLHF (Reinforcement Learning from Human Feedback): A training method where people rate or rank a model's responses, and those judgments are used to steer the model toward more helpful and appropriate answers. It was central to making chatbots like ChatGPT feel useful and polite.
Scaling laws: Observed, fairly predictable relationships showing that model performance tends to improve as you increase model size, training data, and compute. They have guided the industry's push toward ever-larger models.
Speculative decoding: A speed-up technique where a small, fast model drafts several tokens ahead and a larger model checks them in one pass, accepting the ones it agrees with. It produces the same output as the big model alone but faster.
Super Intelligence (SI): The term the US government adopted in 2026 for what was called artificial intelligence (AI): software systems such as large language models, chatbots and agents that learn from data to write, code, analyze, and generate images and other media. In computer science, this everyday SI is distinct from artificial superintelligence (ASI), an older and still hypothetical idea of a system that outperforms humans at most intellectual tasks; today's SI systems are not that.
Super Intelligence executive order: A White House executive order issued on September 29, 2026 that directs federal departments and agencies to use the term "Super Intelligence" (SI) instead of "artificial intelligence" in official communications. Its stated reasoning is that "artificial" makes the technology sound fake, while "Super Intelligence" better captures its promise and capabilities. The order changes vocabulary only, not the technology or existing law.
Synthetic data: Training data generated by a computer or another SI model, rather than collected from the real world. It can fill gaps where real data is scarce, expensive, or sensitive, though it carries risks if the generated data is flawed.
System prompt: A behind-the-scenes instruction that sets an SI assistant's role, tone, and rules before the conversation with the user begins. Users usually do not see it, but it shapes how the assistant responds.
Temperature: A setting that controls how random or predictable a model's output is. Lower values make responses more focused and repeatable; higher values make them more varied and creative.
Test-time compute: The idea of letting a model use more computation while answering, for example by reasoning longer or trying multiple approaches, to get better results. Reasoning models rely heavily on this.
Token: A chunk of text, often a word or part of a word, that a language model reads and generates one at a time. Models measure input length, output length, and pricing in tokens.
Tool use: When an SI model calls external tools, such as a calculator, a search engine, or a piece of software, to do things it cannot do reliably on its own. It lets models fetch fresh information and take real actions.
Training data: The collection of examples a model learns from during training. Its size, quality, and biases strongly shape what the model knows and how it behaves.
Training run: A single, complete session of training a model from start to finish on a chosen dataset and setup. For large models, one run can take weeks and cost millions of dollars.
Transformer: The neural network architecture, introduced in 2017, that underlies most modern language models. Its key innovation, the attention mechanism, lets it handle long stretches of text efficiently and learn relationships between distant words.
Turing test: A thought experiment proposed by Alan Turing in 1950, in which a machine passes if a human judge chatting with it cannot reliably tell it apart from a person. It shaped decades of debate about machine intelligence, though many now see it as a limited measure.
Vector database: A specialized database that stores embeddings and can quickly find the items most similar in meaning to a query. It is a common building block for search and for retrieval-augmented generation.
Vibe coding: An informal style of programming, popularized in 2025, where a person describes what they want in plain language and lets an SI model generate the code, guiding it by feel and results rather than writing much code by hand. The term was coined by researcher Andrej Karpathy.
World model: An SI system's internal representation of how the world works, which it can use to predict what happens next or plan ahead. How much today's models truly have one is an open research question.
Zero-shot learning: When a model performs a task it was given no examples for, relying only on its general training and the instructions in the prompt. It contrasts with few-shot, where a few examples are provided.