As artificial intelligence rapidly transforms industries, it is simultaneously generating a complex lexicon to describe its inner workings. From established acronyms like LLMs, RAG, and RLHF to emerging concepts such as “opaque recurrence”—a reasoning method in OpenAI’s latest Astra model that has concerned safety researchers—the vocabulary evolves daily. This speed can leave even seasoned tech professionals feeling overwhelmed. In response, we have compiled a living, regularly updated glossary to provide plain-English definitions for the most relevant AI terms.
Artificial General Intelligence (AGI)
AGI remains a loosely defined concept, generally referring to systems that surpass average human capability across a broad range of tasks. Perspectives vary among industry leaders: OpenAI CEO Sam Altman has likened it to hiring a median human as a co-worker, while OpenAI’s charter defines it as highly autonomous systems outperforming humans in most economically valuable work. Google DeepMind characterizes AGI as AI matching human proficiency in most cognitive tasks. Notably, even leading experts admit the definition remains elusive.
AI Agents
An AI agent is an autonomous tool capable of executing multi-step tasks beyond the scope of basic chatbots, such as filing expenses, booking reservations, or maintaining code. While the concept implies a system that can orchestrate multiple AI tools to achieve goals, the infrastructure is still maturing. The term lacks a single rigid definition, but generally denotes a system that operates independently to complete complex workflows.
API Endpoints
Think of API endpoints as invisible buttons on software that allow other programs to trigger actions. Developers use these interfaces to integrate applications, such as enabling an AI agent to control smart home devices or pull data from third-party services without manual input. As agents become more sophisticated, they can locate and utilize these endpoints autonomously, unlocking new levels of automation.
Chain of Thought
While humans might answer simple questions instantly, complex problems often require intermediate steps—much like solving a math equation on paper. In AI, chain-of-thought reasoning involves breaking down large language model (LLM) queries into smaller, sequential steps. Although this process takes longer, it significantly improves accuracy, particularly in logic and coding. Reasoning models are built upon traditional LLMs but are fine-tuned via reinforcement learning to excel at this step-by-step analysis.
Coding Agents
A specialized subset of AI agents, coding agents assist in software development by autonomously writing, testing, and debugging code. Unlike standard assistants that merely suggest snippets for human review, these agents can navigate entire codebases, identify errors, run tests, and deploy fixes with minimal oversight. They function somewhat like tireless junior developers, though human review remains essential for quality control.
Compute
Compute refers to the computational power necessary to train and deploy AI models. It is often used as shorthand for the underlying hardware infrastructure, including GPUs, CPUs, and TPUs, which form the backbone of the modern AI industry.
Deep Learning
A branch of machine learning, deep learning uses multi-layered artificial neural networks inspired by the human brain. These systems can identify patterns and features in data autonomously, rather than relying on human-defined rules. While they require massive datasets and longer training times, they enable algorithms to learn from errors and improve outputs through repetition.
Diffusion
Diffusion is the foundational technology behind many generative AI models for image, music, and text. Inspired by physical processes where substances spread irreversibly, diffusion systems add noise to data until it is unrecognizable. The AI then learns to reverse this process, reconstructing detailed content from pure noise.
Distillation
Distillation is a technique where a smaller, more efficient “student” model is trained to mimic the behavior of a larger “teacher” model. By analyzing the teacher’s outputs, the student can approximate the original model’s performance with greater speed and lower resource costs. This method is likely how companies developed faster variants like GPT-4 Turbo. However, distilling a competitor’s proprietary model often violates their terms of service.
Fine-Tuning
Fine-tuning involves further training a pre-existing AI model on specialized, domain-specific data to optimize it for particular tasks. Many startups use this approach to enhance the utility of large language models for specific industries or applications.
GANs (Generative Adversarial Networks)
GANs are a machine learning framework comprising two neural networks—a generator and a discriminator—that compete against each other. The generator creates data, such as images, while the discriminator attempts to detect falsifications. This adversarial process drives the generator to produce increasingly realistic outputs, making GANs particularly effective for narrow applications like deepfakes and photo generation.
Hallucination
In the AI industry, hallucination refers to a model generating incorrect or fabricated information. This is a significant quality control issue, as hallucinations can produce misleading outputs with real-world consequences, such as providing dangerous medical advice. The phenomenon is believed to stem from gaps in the model’s training data or understanding.
Compute costs are still the elephant in the room. Definitions are helpful, but economic viability matters more to us.
RAG is getting old news. The real buzz is now shifting toward chain-of-thought reasoning in production environments.
I didn’t know coding agents could deploy fixes autonomously. When do you think we will see true junior developer replacement?
Wait, is AGI really defined as hiring a median human? That feels dangerously reductive given the current capabilities.
Finally, someone explains opaque recurrence in simple terms. This glossary is exactly what I needed for our investor deck.