Have been given overly complicated definitions about AI.
In this doc, you'll find definitions to every AI keyword that you've probably heard, but might not understand, that should help with the problems outlined above.
Below, you'll find definitions for:
AI
Machine Learning
Deep Learning
Models
Parameters
Training
Neural networks
Tokens
Transformers
LLMs
Generative AI
GPT
RAG
Anything missing? Let me know.
AI
An approach to automation for tasks that are hard to write down the step by step instructions for (e.g. how to see, how to understand natural language, etc)
Machine Learning
An approach to AI where software learns to do tasks from examples rather than rules
Deep Learning
An approach to machine learning using neural networks
Model
Input/output functions (aka mathematical equations - numbers in -> numbers out) that represent some relationship between the inputs and outputs (e.g. your text request -> an appropriate response, image pixels -> how should my self driving car turn/accelerate)
Parameters
The numbers inside models that control what input/output relationship they represent
Training
The process of changing the parameters of a model to adjust what input/output relationship they represent
Neural Networks
Models that can learn to represent any input/output relationship
Tokens
Pieces of words (not necessarily whole words) that text inputs to AI systems get broken into before processing (these tokens then get turned into numbers (vectors) so they can be processed by the mathematical AI models
Transformers
The type of neural network powering state of the art AI models (also the T in GPT)
LLMs
Large Language Models - models with lots of parameters (hence the “large”) trained on examples of text (typically to predict the next word) which amazingly, seems to give them some form of intelligence!
Generative AI
AI that generates realistic looking data (e.g. realistic images, realistic text responses, realistic forecasts)
GPT
Generative Pre-trained Transformer (not General Purpose Transformer) transformer neural networks that have already seen lots of example data (have been pre-trained) and can generate similar looking data