Quiz: Embeddings & Vector Spaces: How AI Understands Meaning

Question 1 of 8

Why do computers need to convert words into numbers when processing text?

Words are too complex for modern processors

Numbers take up less storage space than words

Numbers are easier for humans to understand

Computers work with numbers and can add, multiply, and compare, but they can't read

Question 2 of 8

What is the main problem with one-hot encoding?

It requires too much computational power

It makes similar words appear too close together

It can only work with a vocabulary of 4 words

It treats every word as equally different from every other word and captures no meaning

Question 3 of 8

How many numbers typically make up a word embedding?

50 to 100

256 to 1536

4 to 10

10,000 to 50,000

Question 4 of 8

What does the equation 'king - man + woman ≈ queen' demonstrate about embeddings?

Subtracting embeddings removes all meaning from the vectors

Mathematical operations on embeddings always produce gender-related results

The embeddings captured that 'king' is to 'man' what 'queen' is to 'woman', and relationships are encoded in the numbers

Embeddings can only work with royal terminology

Question 5 of 8

Which of these is given as a working example of embedding arithmetic?

Paris - France + Italy ≈ Rome

Big - Small + Tall ≈ Short

Cat - Dog + Bird ≈ Animal

Red - Color + Shape ≈ Circle

Question 6 of 8

How are embeddings created?

They're calculated using a simple mathematical formula

They're learned by training a model on tons of text to predict words from context

They're hand-coded by programmers based on dictionary definitions

They're randomly generated and then adjusted manually

Question 7 of 8

Which models are mentioned as examples that started the approach of learning embeddings?

ChatGPT and Claude

BERT and GPT-3

Word2Vec and GloVe

RNN and LSTM

Question 8 of 8

How does Google return results about 'debugging tips' when you search for 'how to fix a bug'?

It uses exact word matching and synonym dictionaries

It relies on user click history to determine relevance

It translates all queries into a standard programming language

The query gets embedded, documents get embedded, and the search engine finds documents with similar embeddings where meaning matches

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