There is a lot of terminological confusion in the field. The fundamental difference is about how the "AI" learns. And who, if at all, supervises the learning.
Some systems are fully formed AI. Often based on heuristics. Humans have encoded the rules into the system. Others do limited learning based on pattern recognition. Still others learn based on tagged data. And LLMs don't need even that. In a stream of N tokens, the trainer has access to K+1 when it is training a model that knows 1...K and can check if the prediction was correct.
AI literally refers to any artificial thing that feels intelligent to us: does some thing a simple bash script will not. Your rules based chess engine is AI. The bot you compete against in single player Age of Empires 2 is AI. DeepBlue was AI.
ML is where things become interesting because we teach machines to learn.
NN is where we borrow terminology from neuroscience and create "neurons" that learn.
DL is where we stack neurons deeply in layers.
I am trying to make sense of all this myself.[1] So I am building a curriculum for it as I learn. So much of the teaching material in the wild revolves around torch. Makes it useless if you want to peek inside the actual machinery.
[1] https://bhashika.org.in/logs/2026/an-ml-curriculum
Some systems are fully formed AI. Often based on heuristics. Humans have encoded the rules into the system. Others do limited learning based on pattern recognition. Still others learn based on tagged data. And LLMs don't need even that. In a stream of N tokens, the trainer has access to K+1 when it is training a model that knows 1...K and can check if the prediction was correct.
AI literally refers to any artificial thing that feels intelligent to us: does some thing a simple bash script will not. Your rules based chess engine is AI. The bot you compete against in single player Age of Empires 2 is AI. DeepBlue was AI.
ML is where things become interesting because we teach machines to learn.
NN is where we borrow terminology from neuroscience and create "neurons" that learn.
DL is where we stack neurons deeply in layers.
I am trying to make sense of all this myself.[1] So I am building a curriculum for it as I learn. So much of the teaching material in the wild revolves around torch. Makes it useless if you want to peek inside the actual machinery.
[1] https://bhashika.org.in/logs/2026/an-ml-curriculum