Demystifying AI: A Simple Guide to Common Terms from LLMs to Hallucinations

Artificial intelligence (AI) is a complex and evolving field, often filled with technical jargon that can be confusing. To help clarify some of these terms and concepts, a glossary has been developed, covering key words and phrases often encountered in AI discussions and articles.

Key AI Terms and Definitions

AGI (Artificial General Intelligence)

AGI refers to AI systems that can perform tasks as well as or better than humans. Definitions vary, with Sam Altman of OpenAI suggesting it’s akin to having a median human as a co-worker. Google DeepMind’s interpretation also highlights the capability to match human cognitive tasks.

AI Agent

An AI agent is a more advanced tool that can perform complex tasks autonomously—like managing bookings or writing code—beyond simple chat interactions. This evolving space may have varied meanings depending on the context.

Chain of Thought

This concept in AI refers to the breakdown of problems into smaller, more manageable steps, improving the quality of outcomes in tasks like coding and logic problems. While it may take longer to reach a solution, the accuracy tends to be higher.

Deep Learning

A subset of machine learning, deep learning involves multi-layered neural networks that enable more intricate data correlations. These algorithms learn independently and improve their outputs over time, though they require vast amounts of data and longer training periods.

Diffusion

Diffusion is a process used in AI models for data generation, where data structures are gradually corrupted and then learned to be reconstructed, enabling models to produce realistic images, music, and text.

Distillation

This technique allows knowledge transfer from a larger AI model (the teacher) to a smaller one (the student), creating more efficient models while maintaining performance quality.

Fine-tuning

Fine-tuning is a method used to enhance the performance of an AI model for a specific task by exposing it to new, targeted data following its initial training.

GAN (Generative Adversarial Network)

GANs consist of two neural networks contesting with each other: one generates data while the other evaluates it. This adversarial setup helps improve the realism of outputs without additional human input.

Hallucination

In AI parlance, hallucination refers to instances when AI systems produce incorrect or misleading information. This phenomenon stems from gaps in training data and poses significant risks, particularly in applications where accuracy is vital.

Inference

Inference involves applying a trained AI model to make predictions or decisions based on new data. The efficiency of inference can vary based on the hardware used.

Large Language Model (LLM)

LLMs are advanced AI systems, like ChatGPT, that process and generate human-like text by understanding relationships within massive datasets of language examples. They use probabilities to curate responses based on previous training.

Neural Network

This term describes the architecture behind deep learning models, inspired by the human brain’s interconnected neurons. The development of graphical processing units has accelerated the performance of these networks in tasks such as speech recognition and image processing.

Training

Training is the process where AI models learn patterns from data to generate insights or outputs. It can be resource-intensive, depending on the complexity and scope of the data involved.

Transfer Learning

This approach leverages knowledge from pre-existing trained models to expedite the development of new models for related tasks, but it may be limited by the specificity of the previous training.

Weights

Weights are numeric values assigned during training that determine how much influence specific features of the training data will have on the model’s output.

This glossary will be updated regularly to reflect the ongoing advancements and terms emerging in the rapidly evolving realm of artificial intelligence.

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