easyMultiple Choice
Generative AI Leader Practice Question: Which Google AI milestone introduced the…
Which Google AI milestone introduced the Transformer architecture that underpins modern LLMs?
⚠ Common exam trap
Google often tests the distinction between the original research paper that introduced a concept (the Transformer paper) and later implementations or applications of that concept (like BERT or GPT), causing candidates to confuse the milestone with its derivative products.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Transformer paper
The Transformer architecture, which is the foundational technology behind modern large language models (LLMs) like GPT and BERT, was introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al. This paper proposed the self-attention mechanism and the encoder-decoder structure that replaced recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, enabling parallelized training and superior handling of long-range dependencies in sequence data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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AlphaGo
Why it's wrong here
AlphaGo mastered the board game Go using deep reinforcement learning and Monte Carlo tree search, not the Transformer. The Transformer was introduced in the 2017 'Attention Is All You Need' paper. AlphaGo is tempting as a famous Google DeepMind milestone, but it predates and does not use the attention-based architecture in question.
- ✓
Transformer paper
Why this is correct
The 2017 paper "Attention Is All You Need" introduced the Transformer, replacing recurrent and convolutional layers with self-attention to process sequences in parallel. This architecture underpins modern LLMs, satisfying the stem's requirement for the Google milestone that originated the Transformer.
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AlphaFold
Why it's wrong here
AlphaFold predicts protein structures from amino acid sequences; it did not introduce the Transformer. The Transformer came from the 2017 paper 'Attention Is All You Need', which underpins modern LLMs. AlphaFold is tempting because it is a landmark Google DeepMind achievement, but its architecture and scientific domain are unrelated to language modelling.
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BERT
Why it's wrong here
BERT applies the Transformer encoder to natural language understanding; it did not introduce the architecture. The Transformer originated in the 2017 'Attention Is All You Need' paper. BERT is tempting because it is a well-known Google LLM built on Transformers, but it is a downstream application, not the originating milestone.
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