AI's New Language: A Glossary for the Confused

AI is rewriting the world and, at the same time, inventing a whole new language to describe how it's doing it. Sit in on any product meeting, pitch, or panel these days, and you'll hear people toss around LLMs, RAG, RLHF — and, as of last week, terms like "opaque recurrence," the reasoning technique in OpenAI's new Astra model that's got AI safety researchers rattled. The vocabulary moves fast enough to make even very smart people in the tech world feel a little insecure.

This glossary is our attempt to fix that: plain-English definitions of the AI terms you're most likely to see, whether you're building with this stuff, investing in it, or just trying to keep up by reading TechCrunch or listening to related podcasts. We update it regularly as the field evolves, so consider it a living document, much like the AI systems it describes.

AGI: The Elusive Goal

Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that's more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman once described AGI as the "equivalent of a median human that you could hire as a co-worker."

Meanwhile, OpenAI's charter defines AGI as "highly autonomous systems that outperform humans at most economically valuable work." Google DeepMind's understanding differs slightly from these two definitions; the lab views AGI as "AI that's at least as capable as humans at most cognitive tasks." Confused? Not to worry — so are experts at the forefront of AI research.

Agents and Endpoints: The Next Wave

An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we've explained before, there are lots of moving pieces in this emergent space, so "AI agent" might mean different things to different people.

Think of API endpoints as "buttons" on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations — for example, allowing one application to pull data from another, or enabling an AI agent to control third-party services directly without a human manually operating each interface. As AI agents grow more capable, they are increasingly able to find and use these endpoints on their own, opening up powerful — and sometimes unexpected — possibilities for automation.

Chain of Thought: Thinking Step by Step

Given a simple question, a human brain can answer without even thinking too much about it — things like "which animal is taller, a giraffe or a cat?" But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows).

In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be correct, especially in a logic or coding context. Reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning.

Coding Agents: The Fast Intern

This is a more specific concept than "AI agent," which means a program that can take actions on its own, step by step, to complete a goal. A coding agent is a specialized version applied to software development. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code autonomously, handling the kind of iterative, trial-and-error work that typically consumes a developer's day.

These agents can operate across entire codebases, spotting bugs, running tests, and pushing fixes with minimal human oversight. Think of it like hiring a very fast intern who never sleeps and never loses focus — though, as with any intern, a human still needs to review the work.

Compute and Deep Learning: The Engine

Compute generally refers to the vital computational power that allows AI models to operate. This type of processing fuels the AI industry, giving it the ability to train and deploy its powerful models. The term is often a shorthand for the kinds of hardware that provides the computational power — things like GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry.

Deep learning is a subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees. Deep learning AI models are able to identify important characteristics in data themselves, rather than requiring human engineers to define these features. However, deep learning systems require a lot of data points to yield good results (millions or more). They also typically take longer to train compared to simpler machine learning algorithms — so development costs tend to be higher.

Diffusion and GANs: Creating Realism

Diffusion is the tech at the heart of many art-, music-, and text-generating AI models. Inspired by physics, diffusion systems slowly "destroy" the structure of data — for example, photos, songs, and so on — by adding noise until there's nothing left. Diffusion systems in AI aim to learn a sort of "reverse diffusion" process to restore the destroyed data, gaining the ability to recover the data from noise.

A GAN, or Generative Adversarial Network, is a type of machine learning framework that underpins some important developments in generative AI when it comes to producing realistic data — including (but not only) deepfake tools. GANs involve the use of a pair of neural networks, one of which draws on its training data to generate an output that is passed to the other model to evaluate. The two models are essentially programmed to try to outdo each other. The generator is trying to get its output past the discriminator, while the discriminator is working to spot artificially generated data. Though GANs work best for narrower applications (such as producing realistic photos or videos), rather than general purpose AI.

Hallucinations: The AI Problem

Hallucination is the AI industry's preferred term for AI models making stuff up — literally generating information that is incorrect. Obviously, it's a huge problem for AI quality. Hallucinations produce GenAI outputs that can be misleading and could even lead to real-life risks — with potentially dangerous consequences (think of a health query that returns harmful medical advice).

The problem of AIs fabricating information is thought to arise as a consequence of gaps in training data. Hallucinations are contributing to a push toward increasingly specialized and/or vertical AI models — i.e. domain-specific AIs that require narrower expertise — as a way to reduce the likelihood of knowledge gaps and shrink disinformation risks.

The Rest of the Vocabulary

Distillation is a technique used to extract knowledge from a large AI model with a 'teacher-student' model. Developers send requests to a teacher model and record the outputs. These outputs are then used to train the student model, which is trained to approximate the teacher's behavior. Distillation can be used to create a smaller, more efficient model based on a larger model with a minimal distillation loss. While all AI companies use distillation internally, it may have also been used by some AI companies to catch up with frontier models, though distillation from a competitor usually violates the terms of service of AI API and chat assistants.

Fine-tuning refers to the further training of an AI model to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new, specialized (i.e., task-oriented) data. Many AI startups are taking large language models as a starting point to build a commercial product but are vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise.

Inference is the process of running an AI model. It's setting a model loose to make predictions or draw conclusions from previously seen data. To be clear, inference can't happen without training; a model must learn patterns in a set of data before it can effectively extrapolate from this training data. Many types of hardware can perform inference, ranging from smartphone processors to beefy GPUs to custom-designed AI accelerators, but not all of them can run models equally well.

Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT, Claude, Google's Gemini, Meta's AI Llama, Microsoft Copilot, or Mistral's Le Chat. When you chat with an AI assistant, you interact with a large language model that processes your request directly or with the help of different available tools. LLMs are deep neural networks made of billions of numerical parameters that learn the relationships between words and phrases and create a representation of language. These models are created from encoding the patterns they find in billions of books, articles, and transcripts.

Memory cache refers to an important process that boosts inference. Caching is an optimization technique, designed to make inference more efficient by saving particular calculations for future user queries and operations. One of the more well-known is KV (or key value) caching, which works in transformer-based models, and increases efficiency, driving faster results by reducing the amount of computation needed.

Why This Vocabulary Matters

As AI's vocabulary expands, the gap between those who understand it and those who don't widens. This glossary is a step toward closing that gap. The terms here are not just jargon; they represent fundamental concepts that shape how AI is built, deployed, and governed. Whether you're a developer, an investor, or simply an interested observer, understanding these terms is essential for making informed decisions about the AI systems that are increasingly affecting our lives. The stakes are high, and being fluent in the language of AI is becoming a critical skill.