Artificial intelligence is a rapidly evolving and complex field, filled with specialized terminology and jargon that can quickly become overwhelming. To help clarify the most frequently encountered terms in AI coverage, here’s a simple, accessible guide.
AGI (Artificial General Intelligence)
AGI is widely considered a somewhat elusive goal—the development of an AI system as capable as an average or median human at the majority of cognitive tasks. Definitions vary slightly: OpenAI’s CEO Sam Altman refers to AGI as similar to hiring a competent human co-worker, while OpenAI’s charter describes it as a system surpassing humans at most economically valuable tasks. Google DeepMind gives another definition, treating AGI as AI that matches or exceeds human capabilities in cognitive tasks. The lack of a universally agreed-upon definition underscores how speculative and uncertain AGI remains even among leading experts.
AI Agent
An AI Agent is an advanced AI-based system designed to autonomously perform multi-step tasks on a user’s behalf, such as making payments, scheduling meetings, booking reservations, and even writing or maintaining code. While promising, the meaning of “AI agent” is still evolving as the underlying infrastructure and concepts gain maturity.
Chain of Thought
Human reasoning often proceeds in multiple logical steps, allowing people to solve more complicated questions incrementally. In AI, this same approach is called chain-of-thought reasoning, where a model handles complex queries by breaking them down into smaller tasks or intermediate steps. Though this can extend processing times, it generally leads to more accurate and reliable answers, especially for problems requiring logic and computational accuracy.
Deep Learning
Deep learning refers to a form of machine learning that leverages artificial neural networks—complex architectures inspired by the biological neuron connections in human brains. These systems can recognize patterns and relationships within exceptionally large and complex datasets, and adapt from feedback over time. Regardless, deep learning demands huge data sets and significant computational resources to achieve effective, reliable results.
Diffusion
In AI, diffusion describes a technique central to many generative models used to create images, text, or audio content. Inspired by physical processes, diffusion algorithms systematically introduce noise into data sets to degrade them completely, then “reverse” the degradation, effectively teaching AI systems to reconstruct original patterns from seemingly random noise.
Distillation
In this AI optimization process, knowledge from a complex “teacher” model is transferred to a simpler, compact “student” model. By replicating the outputs of larger models, researchers create more efficient systems performing nearly as well. Popular models like GPT-4 Turbo likely emerged via such distillation techniques. While widespread internally, distillation techniques can become problematic or breach terms of service if developers clandestinely leverage competitor APIs.
Fine-Tuning
Fine-tuning involves taking a general AI model (often a large language model) and training it further with specialized, domain-specific datasets to tailor it for particular applications or industries. This targeted approach significantly enhances performance and usability for defined tasks or market needs.
GAN (Generative Adversarial Network)
GANs are specialized machine-learning frameworks consisting of two competing neural networks: one (the generator) produces outputs—such as images—that mimic authentic data, and the other (the discriminator) tries to determine their validity. Through iterative competition, the generator continually improves, eventually producing highly realistic data like convincing images and deepfake videos.
Hallucination
In AI terminology, hallucination describes the phenomenon by which a generative AI system provides entirely inaccurate responses or invented information, creating serious concerns about reliability, misinformation, and potential harm. This inherent limitation drives current efforts toward more specialized or domain-specific models that reduce risks by narrowing focus and relying on accurate and comprehensive training datasets.
Inference
Inference is the operational running stage of an AI system, where the already-trained model makes predictions or conclusions based on data it hasn’t necessarily encountered before. Effective inference heavily depends on sufficient prior training and appropriately robust hardware; larger models require significantly more processing power than simpler ones.
Large Language Model (LLM)
Large Language Models are powerful AI systems built to understand and generate human-like text. Systems like OpenAI’s GPT series, Google’s Gemini, and Meta’s Llama process immense volumes of text data, recognizing patterns, and understanding context. They use this knowledge to generate coherent, plausible text responses by predicting words and phrases based on context and patterns learned.
Neural Network
Neural networks lie at the heart of modern AI breakthroughs, forming a multi-layered digital architecture loosely inspired by the neural connections in human brains. Although the concept traces back decades, modern graphics processing units (GPUs) dramatically enhanced computational power, transforming theoretical neural networks into practical, powerful tools that drive voice recognition, autonomous cars, medical discoveries, and other advancements.
Training
Training involves providing a machine-learning system with extensive data from which it identifies relationships and patterns, eventually shaping its parameters or internal structures accordingly. This training process enables an AI model to make accurate predictions or to generate useful output. Such learning-based approaches differ significantly from rule-based systems, which operate strictly according to pre-defined instructions without evolving through data interactions.
Transfer Learning
This AI approach utilizes knowledge already embedded within an existing trained model, repurposing it to accelerate the development process for related but distinct tasks. Transfer learning helps enhance efficiency and reduce development costs, particularly valuable in contexts where available relevant data might be limited. However, refinement using task-specific datasets often remains necessary to fully realize model efficacy.
Weights
Weights are numerical parameters within AI models used during training, determining the relative significance of inputs. Initially randomized, they adapt over training cycles to highlight patterns most critical for achieving accurate outcomes. In a practical example like real estate AI, weights would give varying importance to factors such as location, property size, or features like garages in predicting home value assessments.
This straightforward guide will continue to evolve alongside the field, incorporating emerging terms and concepts as AI researchers explore and extend our collective understanding of artificial intelligence and its implications.
