AI is talking over in every field. Not just in the digital world, but also in the real world.
Top entrepreneurs like Bill Gates & Elon Musk think that AI will replace a lot of jobs in the future. So, learning the basics of AI, how it works, etc. can help you a lot.
It won’t just help you in safeguarding your job, but also help you in various other aspects of life.
Many people know a lot about AI, they also know how to use AI chatbots, but don’t know the basic terms used in the world of AI. Because of this, they can’t understand the news and new updates happening in the AI world properly.
So, in this post, I’ll share some basic terms used in AI, so that you can understand about AI and topics related to it, much more easily.
Even if you want to study AI in more detail in the future, then understanding these terms will help you a lot.
I’ll try to explain each and every of these terms in as simple and understandable language as possible.
37 AI Terms You Should Know!
1. Artificial Intelligence (AI)
This is when computers can do things that usually need human thinking like talking, solving problems, or recognizing faces and more.
2. Machine Learning (ML)
A part of AI where the computer learns from data. It’s like teaching a dog new tricks, but instead, you’re teaching a machine.
3. Deep Learning
A smarter version of ML that uses many layers to learn big tasks like reading handwriting or driving a car.
4. Neural Network
This is how deep learning works. It’s like a mini computer brain made of fake neurons that learn from examples.
5. Transformer
A smart model that understands the meaning of words in the right order. It’s exactly how ChatGPT talks like a human.
Here are 3 Sites That Pay You to Train AI (No Skills or Prior Experience Required)
6. GPT (Generative Pre-trained Transformer)
This is the brain behind ChatGPT. It was trained on a lot of text and can now write stories, answer questions, and chat with you.
7. Prompt
Whatever you type into the AI, like a question or request. Example: “Write a short story about a Rabbit & a Tortoise.”
8. Token
A small piece of text that the AI understands. It could be a word, part of a word or a punctuation.
9. Fine-Tuning
This is when we take an already trained AI and teach it something specific, like training a cook to make only pizza.
10. Training Data
This is the information/data we give AI to help it learn. If the data is bad, the AI learns the wrong stuff.
11. Inference
When AI uses what it already learned to answer a new question or make a guess.
12. Model Parameters
Tiny settings inside the AI model that help it to learn. The more parameters it has, the smarter it can be.
13. Overfitting
When AI remembers the training data too well and can’t handle anything new. Like a student who memorized answers but can’t solve new problems.
14. Underfitting
When AI didn’t learn enough and keeps making mistakes. Like a student who didn’t study at all.
15. Bias
When AI makes incorrect choices because the data it learned from was incorrect. For example, if it mostly saw pictures of men as doctors, it might wrongly assume all doctors are men.
16. Hallucination
When AI makes stuff up and acts like it’s true. It doesn’t lie on purpose, it just guesses wrong.
17. Dataset
A big collection of information like a folder full of labeled pictures, videos, or sentences used to train AI.
18. Natural Language Processing (NLP)
This helps AI understand and work with human language. It’s used in tools like chatbots, auto-correct, or Siri.
19. Computer Vision
This helps AI see pictures and videos. It’s used in face scanners, self-driving cars, and photo apps.
20. Reinforcement Learning
AI learns by trying things, getting rewards for good choices and nothing (or a penalty) for bad ones, just like training a dog with treats.
21. Supervised Learning
The AI learns from examples that are already labeled. For example: showing it lots of cat pictures labeled “cat.”
22. Unsupervised Learning
The AI tries to figure out patterns without any labels. It might learn that people who like coffee also like reading, all by itself.
23. Semi-Supervised Learning
A mix of both: some labeled data, some not. It’s good for such situations when we don’t have time to label everything.
24. Generative AI
AI that can create new stuff, like writing stories, creating images, or composing music.
25. Latent Space
A secret place in the AI’s brain where it thinks and plays with ideas before creating something.
26. Embedding
Turning words or pictures into numbers, so AI can understand what they mean and how they relate to each other.
27. Zero-Shot Learning
AI can do a task without ever learning it before. Like solving a puzzle it has never seen or solved before.
28. Few-Shot Learning
AI learns from just a few examples. Like hearing a new slang word once and knowing how to use it.
29. LLM (Large Language Model)
A really big AI model, trained mainly on text. It’s what powers things like ChatGPT and can write, explain, or answer almost anything.
30. Ethical AI
This means making sure AI is used in a fair and legal way, without hurting people or spreading misinformation.
31. Black Box
An AI system that works, but we don’t really know how. Even the creators might not fully understand what’s going on inside.
32. Turing Test
A test to see if an AI can imitate a human well enough to fool someone or not.
33. Singularity
A possible future time when AI becomes smarter than humans. It’s still just an idea, not real yet.
34. Prompt Engineering
Writing really good prompts (questions/commands) to get the best results from AI.
35. Multimodal AI
AI that can understand different types of stuff like reading, looking at pictures, and listening to sounds, all at once.
36. API
A way for apps and websites to connect with AI tools. It’s like a bridge that lets them work together.
37. Open Source AI
AI that anyone can use, share, or improve because it’s made freely available to the public.
Wrapping it Up
So, these were some basic and really-really common terms used in the AI space.
If you understood all these terms clearly, then you have much better understanding of AI.
Now, you can understand news related to AI much more clearly, understand what changes happened in the new updates of AI tools and more.
