Essential AI Glossary: Key Terms Explained

Understanding artificial intelligence can be challenging. This glossary defines key AI terms in clear, concise language.

AGI (Artificial General Intelligence)

AGI refers to AI that surpasses human capabilities in most tasks. The exact definition varies, with some emphasizing economic value and others focusing on cognitive tasks. Even AI experts are grappling with a precise definition.

AI Agent

An AI agent is a tool that performs tasks beyond basic chatbot capabilities, like scheduling, booking, or coding. It's an evolving field, but the core concept involves autonomous systems using multiple AI technologies for complex tasks.

Chain of Thought

Chain of thought reasoning allows large language models (LLMs) to solve complex problems by breaking them down into smaller, intermediate steps. This improves accuracy, especially in logic and coding, though it can increase processing time.

Deep Learning

Deep learning is a type of machine learning using artificial neural networks (ANNs) with multiple layers. This allows for more complex correlations and self-improvement compared to simpler machine learning models. Deep learning requires large datasets and extensive training.

Diffusion

Diffusion models power many generative AI tools for art, music, and text. They work by adding noise to data until it's unstructured, then learning to reverse the process and reconstruct the data from noise.

Distillation

Distillation is a "teacher-student" model where a smaller "student" model learns from the outputs of a larger "teacher" model. This creates more efficient models but can be misused to replicate competitor models, violating API terms of service.

Fine-tuning

Fine-tuning is the process of further training an existing AI model to specialize in a specific task or area using new, targeted data. Many AI startups use this to adapt LLMs for specific industries.

GAN (Generative Adversarial Network)

GANs use two neural networks in competition: a generator creates data, and a discriminator evaluates its realism. This adversarial process leads to highly realistic outputs, particularly in image and video generation.

Hallucination

Hallucination refers to AI generating incorrect information. This poses significant risks, especially in areas like healthcare. It's a major challenge due to gaps in training data, driving a shift towards specialized AI models.

Inference

Inference is the process of running a trained AI model to make predictions or draw conclusions from data. Different hardware can perform inference, but larger models require more powerful resources.

LLM (Large Language Model)

LLMs power AI assistants like ChatGPT and Google Gemini. They are deep neural networks trained on massive text datasets to understand and generate human language. They predict the next word in a sequence based on the preceding text.

Neural Network

A neural network is a multi-layered algorithmic structure inspired by the human brain. It's the foundation of deep learning and the recent generative AI boom. GPUs have been crucial in unlocking their potential.

Training

Training is the process of feeding data to an AI model so it can learn patterns and generate useful outputs. It's a crucial step in developing machine learning AIs, but it can be computationally expensive.

Transfer Learning

Transfer learning uses a pre-trained AI model as a starting point for a new model for a related task. This saves time and resources, but additional training is often needed for optimal performance.

Weights

Weights are numerical parameters that determine the importance of different features in training data. They shape the AI model's output by assigning different levels of influence to input variables.