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AI, machine learning, and language models
- AI
- Machine learning
- Language models
- Machine learning
Distinguish a broad field from one kind of model.
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Build Your Own AI in 9 Steps
From an elementary-school level all the way to Python code
- Understand it Beginner
- Look inside Intermediate
- Build it yourself Advanced
All 9 steps at a glance
- Beginner Use AI and check its answers
- Intermediate Look inside a language model
- Advanced Build and evaluate with Python
- Level 1 AI, machine learning, and language models
- Level 2 Write and check a prompt
- Level 3 Text, tokens, and IDs
- Level 4 Vectors and embeddings
- Level 5 Training, inference, and retrieval
- Level 6 Instructions, sampling, and verification
- Level 7 Python: run, inspect, change
- Level 8 Numbers, predictions, and activations
- Level 9 Train and evaluate a small model
AI is a fill-in-the-blanks champion!
Let's see how a chat AI (a language model) makes its answers.
What would you put in the blank? Tap an example.
This morning, I __________.
These choices are prepared examples. Tap your selected example again to bring back the blank.
A plausible continuation can still be wrong. Check important answers.
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Does AI really make replies this way?
Yes, predicting what comes next is the basic idea! A language model uses patterns learned during training, your input, and the text so far to calculate what could come next. It chooses a small piece of text called a token, which can be a word or part of one, then repeats the process to build a reply.
Filling a blank is an analogy. In a chat reply, the model usually keeps adding tokens at the end. It doesn't always choose the most likely one: it can also sample from several candidates according to their probabilities.
Then what does “thinking” or “reasoning” mean?
People use these words when AI works through a problem in steps. Underneath, it runs calculations using learned patterns and its input. Fluent language alone is no reason to treat it as a person with feelings or intentions.
Beginner