From LLMs to Hallucinations: A Simple Guide to Key AI Terms

Artificial Intelligence (AI) is a rapidly evolving field full of specialized jargon. Whether you’re an enthusiast, a budding developer, or a seasoned researcher, these terms pop up everywhere—from research papers to product roadmaps. To help you navigate the conversation, here’s a concise glossary of the most important AI buzzwords.

Artificial General Intelligence (AGI)

Often described as “human‐level AI,” AGI refers to systems that can outperform an average person on most economically valuable tasks. OpenAI’s CEO Sam Altman likens AGI to “the equivalent of an average employee you could hire,” while OpenAI’s charter defines it as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind, by contrast, frames AGI as “AI that is at least as good as humans across the majority of cognitive tasks.” Even experts disagree on the exact line between narrow AI and AGI—so consider it a moving target.

AI Agent

An AI agent is an autonomous tool that uses AI technologies (often multiple models) to carry out multi-step tasks on your behalf—everything from tracking expenses and booking flights to writing and reviewing code. While the infrastructure to support fully capable AI agents is still under development, the core idea is simple: a system that orchestrates various AI services to achieve complex goals without constant human guidance.

Chain of Thought

Humans often break down complex problems into smaller steps—jotting ideas on paper to solve a tricky puzzle, for example. “Chain of thought” in AI mimics this by having language models articulate intermediate reasoning steps before arriving at a final answer. Though it may take longer, the results are usually more accurate, especially for logical or programming challenges. Models trained to follow such reasoning chains are known as “reasoning models.”

Deep Learning

A subset of machine learning, deep learning builds multi-layered artificial neural networks that can automatically discover intricate patterns in data—much like the human brain’s neural connections. These models “learn” important features without explicit engineering, but they demand massive datasets (often millions of examples) and substantial computation, driving up development costs.

Diffusion

At the heart of many modern generative models (for images, music, text) lies diffusion: a process that incrementally adds noise to data until it’s unrecognizable, then learns to reverse that noise to generate new content. Inspired by physical diffusion, the goal is to train AI to “undo” noise and produce coherent, novel outputs starting from random patterns.

Distillation

Also called “teacher-student” learning, distillation uses a large, high-capacity model (the teacher) to generate outputs on a set of inputs. A smaller model (the student) then learns to mimic the teacher’s behavior. The result is a more compact, efficient model with minimal loss in performance—likely how GPT-4 Turbo was created.

Fine-Tuning

Fine-tuning involves additional training of a pre-trained model on a specialized dataset, tailoring it to a narrower domain. Many startups build their products by fine-tuning large language models on industry-specific data, boosting relevance and accuracy for their target users.

Generative Adversarial Network (GAN)

GANs pit two neural networks—the generator and the discriminator—against each other. The generator tries to produce realistic data, while the discriminator learns to detect fakes. Through this adversarial process, GANs can generate impressively lifelike images or videos, though they’re best suited for specialized creative tasks rather than broad AI intelligence.

Hallucination

In AI, a “hallucination” occurs when a model confidently generates false or unsupported information. This remains a critical quality challenge—especially in high-stakes areas like healthcare or finance—so most generative services warn users to verify outputs. Hallucinations often stem from gaps in training data; one answer is to build more specialized models with tighter, domain-specific knowledge.

Inference

Inference is the runtime process of a trained model making predictions or drawing conclusions from new data. It’s the “live” phase—distinct from training—where models can run on anything from mobile CPUs to cloud GPUs. However, big models on a laptop can feel glacial compared to optimized cloud hardware.

Large Language Model (LLM)

LLMs—like ChatGPT, Claude, Gemini, LLaMA, and Microsoft Copilot—are deep neural networks with billions of parameters that learn word relationships across massive text corpora. When you chat with an AI assistant, you’re interacting with an LLM that may also tap external tools (web search, code interpreters) to enrich its responses.

Neural Network

These multi-layered algorithmic structures power deep learning and the recent surge in generative AI. Although the concept dates back to the 1940s, it was GPU advances (driven by gaming) that enabled training networks with many layers, unlocking breakthroughs in speech recognition, autonomous navigation, drug discovery, and beyond.

Training

Training is the process of “feeding” data to a model so it can learn patterns and improve its outputs. Before training, a neural network is just random numbers; afterward, those numbers—the “weights”—encode knowledge. Training is expensive in both data and compute, though hybrid approaches (e.g., rule-based models fine-tuned with data) can reduce costs.

Transfer Learning

Transfer learning reuses a pre-trained model as a starting point for a new, related task. It saves time and resources when labeled data is scarce, though for very specialized goals you often still need additional fine-tuning.

Weights

Weights are the numerical parameters in a neural network that determine the importance of various input features. Initially random, they are adjusted during training so the model’s outputs increasingly align with the desired objectives.

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