List of Top 10 Must-Known AI Terminologies

AI Terminologies: Artificial Intelligence (AI) is not a buzzword anymore, it is the driving force behind the next technological revolution. From the moment you unlock your phone with your face to when a chatbot answers a customer service query, AI is quietly shaping your daily life.

It may sound a little bit complex, full of technical jargon and all, but understanding the core concepts is really easy! Knowing the basic terms of AI, is not only a need for a tech expert, but for students, youngsters, and middle age simple people as well. It will help you in many day-to-day life activities including, drafting a mail, understanding concept, using technical voca

So, we have come-up with some basic AI terminologies, and their explanations, which will help you to have better understanding.

Check out: What is a Large Language Model? Check Its Definition, History, and Examples

AI Terminologies

Here are the essential terms you need to know to navigate the world of Artificial Intelligence:

1. Artificial Intelligence (AI)

The broad field of computer science focuses on creating machines that can simulate human intelligence. This means the ability to perform tasks like learning, reasoning, solving problems, perception, and language understanding. Ai, in simple words means, it is the general idea of making computers think and act smart, like a human, to do a job.

2. Machine Learning (ML)

A subset of AI where systems learn from data to identify patterns and make decisions or predictions without being explicitly programmed for that task. In simple language, this is the method of teaching a computer by example. Instead of giving it a million rules, you feed it lots of data (like pictures of cats and dogs), and it learns the difference on its own.6 Your email spam filter is a classic example.

3. Deep Learning (DL)

A subfield of Machine Learning that uses Artificial Neural Networks (ANNs) with multiple layers to analyze complex data like images, sound, and text. This means, it is an advanced type of Machine Learning that uses a complex structure inspired by the human brain to learn extremely complicated patterns.

4. Neural Network

A computational model inspired by the structure and function of the human brain. It consists of interconnected nodes, or neurons, organized in layers that process and pass information.Think of it as a team of tiny data-processors working together in layers. Each processor takes a small piece of the data, processes it, and passes it on, allowing the system to find complex connections and patterns.

5. Large Language Model (LLM) 

A type of Deep Learning model trained on massive amounts of text data, enabling it to understand, summarize, generate, and predict human language. LLM in basic simple language means, it is a giant, digital bookworm that has read most of the internet. Because it is absorbed so much text, it can write emails, answer questions, and even write code in a way that sounds incredibly human. ChatGPT and Google's Gemini are well-known examples.

6. Generative AI

A category of AI models focused on generating new content, such as text, images, music, or video, rather than simply analyzing or classifying existing data. This is basically a creative artist of AI. It learns the style and patterns of content it is trained on (like thousands of Van Gogh paintings) and then creates something completely new and original in that style.

7. Prompt

Prompt means the input or instruction given to an AI model, especially a Large Language Model (LLM) or Generative AI, to guide its output. Means, the question or command you type into the AI. It is the instruction that tells the model what to do, like ‘Write a poem about a robot learning to cook’ or ‘Generate an image of a cat wearing a tiny hat.’

8. Token

A small unit of text that an AI model, particularly an LLM, processes. A token can be a word, a part of a word, or even a single punctuation mark. It is the smallest building block of language that the AI actually ‘reads’ and ‘writes.’ So, when you send a prompt, the AI first breaks it down into these tokens to understand it.

9. Dataset

A structured collection of data ,e.g., images, text, numbers used to train, test, and validate an AI or Machine Learning model. It is basically the digital food, you give the computer to learn from. The quality and variety of the dataset directly determine how good the AI model will be.

Check out: 5 Common AI Myths You Should Stop Believing

10. Hallucination

A phenomenon where an AI model, particularly an LLM, generates outputs that are factually incorrect, nonsensical, or unfaithful to the source material, but are presented with high confidence.