Artificial Intelligence basics for Non-techies - 50 Simplified terms

Introduction:

Have you ever wondered how computers can be so smart? They use something called Artificial Intelligence, or AI for short. It's like giving them a brain to think and learn on their own. But don't worry, you don't need to be a tech expert to understand it. Let's break it down together!

1. Artificial Intelligence (Smart Computers) :

Imagine if your computer could think and learn just like you do. That's what AI is all about! It's like giving your computer a brain so it can do cool things, like recognize your voice or play games.

2. Machine Learning (Learning Computers):

Have you ever taught your dog a new trick? Well, machine learning is like teaching a computer new tricks. Instead of telling it exactly what to do, we show it lots of examples, and it learns from them. For example, it can learn to spot your face in a photo after seeing lots of pictures of you.

3. Deep Learning (Super Smart Learning):

Deep learning is like giving your computer super-smart glasses. It helps it see and understand really tricky stuff, like pictures or sounds. For instance, it can learn to tell the difference between a cat and a dog in a photo.

4. Neural Networks (Computer Brain) :

Neural networks are like computer brains inspired by your own brain. They help computers learn and make decisions based on the things they see and hear. It's kind of like how you learn from your experiences.

5. Natural Language Processing (Talking Computers):

Ever talked to Siri or Alexa? That's because of natural language processing! It helps computers understand what you're saying and talk back to you, just like a conversation with a friend.

6. Computer Vision (Seeing Computers):

Computer vision helps computers see and understand the world around them, just like you do with your eyes. It's how they recognize faces in photos or spot objects in a room.

7. Reinforcement Learning (Learning from mistakes):

Reinforcement learning is like teaching a computer through trial and error. It learns by trying different things and getting rewards or punishments, just like you learn not to touch a hot stove.

8. Supervised Learning (Guided Learning):

Supervised learning is when we teach a computer by giving it lots of examples with the answers. It's like practicing math problems with the teacher's help until you get them right.

9. Unsupervised Learning (Self-learning on own):

Unsupervised learning is like letting a computer explore and find patterns on its own. It's like giving it a puzzle to solve without any hints.

10. Semi-supervised Learning (Self-learning with guidance):

Semi-supervised learning is a mix of both. It's like having some answers for a test but figuring out the rest on your own.

11. Transfer Learning (Knowledge sharing):

Transfer learning is like using what you know from one class to help you in another. It's like knowing how to add and subtract from math class and using that knowledge in science class.

12. Data Mining (Treasure hunt through data):

Data mining is like digging for hidden treasure in a big pile of information. It helps find useful stuff, like trends or patterns, in lots of data.

13. Big Data (High volume data):

Big data is like having a giant mountain of information. It's so big that regular computers can't handle it all at once.

14. Algorithm (Step-by-Step instruction):

An algorithm is like a recipe for a computer. It tells it exactly what steps to follow to solve a problem, like finding the shortest route on a map.

15. Feature Engineering (Picking the right tools):

Feature engineering is like picking the right tools for a job. It helps computers focus on the most important stuff when learning from examples.

16. Generative Pre-trained Transformer - GPT (Writing buddy) :

It's a smart program that can generate new text, like having a writing buddy who's already learned a lot from reading. "Generative" means it can create new text. "Pre-trained" means it has already learned from examples before being used for a specific task. "Transformer" refers to a specific type of neural network architecture used in the model.

17. Regression (Making a prediction):

Regression is like predicting the future based on what we know. It helps computers guess what might happen next, like predicting tomorrow's weather.

18. Clustering (Grouping similar stuff):

Clustering is like finding friends who are similar to you. It helps computers group things together based on how much they're alike, like grouping pictures of cats and dogs.

19. Dimensionality Reduction (Make it simple):

Dimensionality reduction is like cleaning up a messy room. It helps computers tidy up by getting rid of extra stuff they don't need.

20. Overfitting (Learning too much):

Overfitting is like memorizing all the answers for a test but not understanding them. It's when a computer learns too much from one set of examples and can't do well with new ones.

21. Underfitting (Not Learning Enough):

Underfitting is like not studying enough for a test. It's when a computer doesn't learn enough from the examples and can't do well on any of them.

22. Ensemble Learning (Teamwork):

Ensemble learning is like having a group of friends study together for a test. It helps computers work together to get better results, like making more accurate predictions.

23. Convolutional Neural Network - CNN (Special spectacles for computers) :

A convolutional neural network is like special glasses for computers to see better. It helps them recognize things in pictures, like telling the difference between a cat and a dog.

24. Recurrent Neural Network - RNN (Remembering vital stuff):

A recurrent neural network is like a computer with a good memory. It helps them remember important stuff from the past, like what happened in a story.

25. Generative Adversarial Network - GAN (Creating new stuff):

A generative adversarial network is like an artist learning to paint by looking at other paintings. It helps computers create new stuff, like making realistic pictures of people who don't exist.

26. Vector Machine (Smart guess worker):

A support vector machine is like a smart guesser. It helps computers make good guesses about things they've never seen before, like guessing if an email is spam or not.

27. Decision Tree (Make decisions using a flowchart):

A decision tree is like a flowchart for making decisions. It helps computers decide what to do next based on the information they have, like deciding if a fruit is an apple or an orange.

28. Random Forest (A large jury):

A random forest is like a big group of decision trees working together. It helps computers make better decisions by asking lots of trees for their opinions.

29. K-means Clustering (Finding hidden groups):

K-means clustering is like finding hidden clubs in a school. It helps computers find groups of things that belong together, like grouping students by their favorite subjects.

30. Reinforcement learning agent (Learning by Playing):

A reinforcement learning agent is like a kid learning to ride a bike. It learns by trying different things and getting rewards for doing well, like getting better at a game by practicing.

31. Large Language Model - LLM (Word Wizard)

LLM is a type of computer program that's really good at understanding and generating human-like text. It's like having a super-smart assistant that can write essays, answer questions, and have conversations with people, all based on the information it has learned from lots of text data..

32. Q-learning (Learning from experience):

Q-learning is like learning from your mistakes. It helps computers figure out the best actions to take in different situations by remembering what worked well before.

33. Expert System (Advise from a subject matter expert):

An expert system is like having a wise teacher in your computer. It helps computers make decisions by using knowledge from experts in a certain field, like diagnosing illnesses based on symptoms.

34. Fuzzy logic (Thinking in shades of gray):

Fuzzy logic is like thinking in shades of gray instead of black and white. It helps computers deal with uncertainty by understanding that things aren't always just true or false, like deciding how fast a fan should spin based on room temperature.

35. Knowledge Representation (Putting knowledge to work):

Knowledge representation is like organizing your thoughts in a notebook. It helps computers store and use information so they can make smart decisions, like remembering what you learned in school to solve a problem.

36. Pattern Recognition (Spotting patterns):

Pattern recognition is like finding hidden messages in a puzzle. It helps computers see similarities in things, like recognizing faces in a crowd or identifying songs by their tunes.

37. Artificial General Intelligence - AGI (Jack of all trades):

Artificial General Intelligence is like being good at lots of things, not just one. It helps computers understand and learn from different situations, like knowing how to cook, play sports, and solve puzzles.

38. Artificial Narrow Intelligence - ANI (The Specialist):

Artificial Narrow Intelligence is like being really good at just one thing. It helps computers do specific tasks really well, like playing chess or recommending movies you might like.

39. Artificial Superintelligence (Super Smart Computers):

Artificial Superintelligence is like having a computer that's smarter than any human. It helps computers do things we can't even imagine, like solving big problems or exploring space.

40. Hyperparameter (Settings for learning):

A hyperparameter is like adjusting the settings on a game to make it easier or harder. It helps computers learn better by changing how they look at data, like deciding how quickly they should learn from examples. Something like a smart microwave heat settings for a type food.

41. Activation function (Making brain cells active):

An activation function is like turning on a light bulb in a computer's brain. It helps neurons decide whether to be active or not, like deciding if a neuron should fire up and pass on information.

42. Loss Function (Evaluating mistakes):

A loss function is like checking your answers after a test. It helps computers see how well they did compared to the right answers, like figuring out how close their guesses are to the real thing.

43. Gradient Descent (Climbing downhill) :

Gradient descent is like walking down a hill to find the lowest point. It helps computers find the best answers by gradually adjusting their guesses until they're as close as possible to the right ones.

44. Backpropagation (Learning from errors):

Backpropagation is like fixing mistakes as you go. It helps computers learn from their errors by seeing where they went wrong and making adjustments to get it right next time.

45. Epoch (Going around):

An epoch is like going around a race track once. It helps computers learn from all the examples in a dataset by looking at them one by one.

46. Batch Size (Process in bulk):

Batch size is like making batches of cookies instead of baking them one at a time. It helps computers learn faster by looking at groups of examples together instead of one by one.

47. Regularization (Pro-actively avoid mistakes):

Regularization is like practicing a skill over and over to get better at it. It helps computers avoid making mistakes by adding rules to keep them from getting too good at one thing and missing out on others.

48. Dropout (Tea-break):

Dropout is like taking a break during a study session. It helps computers avoid getting too focused on one thing by randomly ignoring some of the information they see.

49. Tensor (Number crunching):

A tensor is like a supercharged spreadsheet. It helps computers crunch lots of numbers at once, like storing all the data they need for making decisions.

50. Edge Computing (Delegating by thinking):

Edge computing is like having a mini-brain in your phone or smartwatch. It helps computers think fast by doing some of the work right where the data is, instead of sending it all to a big computer far away.

Conclusion:

Congratulations! You've just unlocked the secrets of how smart computers work. From recognizing faces in photos to predicting the weather, AI is all around us, making our lives easier and more fun. So next time you ask Siri a question or play a game on your phone, remember the amazing things happening behind the scenes thanks to Artificial Intelligence.

Keep exploring, keep learning, and who knows? Maybe you'll even invent the next big AI breakthrough!