Artificial intelligence (AI) terminology covers a wide range of concepts, from fundamental building blocks like algorithms to advanced models and ethical considerations. A detailed understanding of these terms provides a foundation for grasping the technology’s capabilities and implications.
AI terminology includes core concepts like Artificial Intelligence (AI), which is the simulation of human intelligence in machines, and its subfields such as Machine Learning (ML) and Deep Learning. Key applications and models include Generative AI for content creation, Large Language Models (LLMs) for text-based tasks, and Natural Language Processing (NLP) for understanding human language. Essential terms also cover how AI works, like algorithms and neural networks, and how users interact with it, such as through prompts and prompt engineering.
Core AI concepts
- Artificial Intelligence (AI): The broad field of creating computer systems that can perform tasks requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation.
- Machine Learning (ML): A subset of AI where systems learn from data to improve their performance on a specific task without being explicitly programmed.
- Deep Learning (DL): A subset of machine learning that uses multi-layered artificial neural networks to model complex patterns in data.
- Algorithm: A set of rules or instructions that a machine follows to perform a task or solve a problem.
- Artificial Neural Network (ANN): A computational model inspired by the structure and function of the human brain’s neural networks.
Following AI Terms Everyone Should Know
Large language model (LLM)
A large language model is a type of artificial intelligence that can generate and analyze text or code. You give the model a prompt or question, and it responds with a human-sounding answer. Often, when people refer to AI, they are actually speaking specifically about large language models.
Generative AI
This type of artificial intelligence is trained on a large dataset to identify patterns and create new variations based on those patterns. Generative AI tools create new content ranging from text and images to audio and video. An LLM is just one example of generative AI and uses text.
Machine learning (ML)
This subfield of artificial intelligence deals with a machine’s ability to imitate human behavior. When creating computer models, the goal is to give these models the ability to act intelligently as a human would. Machine learning may involve training machines to understand natural text or recognize visual scenes.
Deep learning
This subset of machine learning aims to simulate how the human brain operates and makes decisions. Deep learning involves passing information through hundreds or thousands of layers of processing. This capability enables computers to take raw, unstructured data and create accurate outputs.
Natural language processing (NLP)
This term represents the ability of digital devices to understand and generate text and speech. Devices gain this capability through a combination of statistical modeling, machine learning, deep learning, and modeling of human language.
Tokens and tokenization
Tokens are small units of text data broken down so an LLM understands them. A token may include characters, words, or phrases. Each LLM developer has a unique system, but typically, 1 token represents several characters. Normally, developers price their products based on tokens.
Tokenization is the process NLP systems use to break text down into smaller units. Once the system divides the text, it can analyze it more effectively to present a human-sounding response.
Application programming interface (API) key
An API key is like a password that lets you access a service. This code identifies and authenticates an application or user.
Within AI models, API keys identify people and grant them access to make requests to the AI system without directly accessing it. For example, you could use an API key to integrate a particular AI model into your application or on your website.
Prompt engineering
While LLMs are meant to supply relevant and human-sounding answers, the quality of their responses vary depending on the quality of the input or prompt.
Through prompt engineering, individuals carefully craft the instructions given to an LLM to enhance the output quality. Well-crafted inputs help create more useful and relevant outputs.
Agent
In the world of AI, agents are like tailored personal assistants built to have particular expertise and help you accomplish tasks more effectively. An AI agent has a specific job and possesses the tools necessary to achieve it.
For example, you could create an agent with knowledge of your particular business and its operations so it can automatically answer customer questions or help you develop content for your website.
