Seq2Seq and Encoder-Decoder: the one-vector bottleneck that led to attention
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Before Transformers, before modern chatbots, there was a beautifully simple idea for turning one sequence into another: read the whole thing, squeeze it into a single vector, then unroll that vector back out into a new sequence. That is sequence-to-sequence learning, and it powered the first wave of neural machine translation. It also had a flaw so obvious in hindsight that fixing it produced the entire modern era of AI. I built an interactive page where you can watch the whole thing happen,…
1Key Takeaways
- Before Transformers, before modern chatbots, there was a beautifully simple idea for turning one sequence into another: read the whole thing, squeeze it into a single vector, then unroll that vector back out into a new sequence.
- That is sequence-to-sequence learning, and it powered the first wave of neural machine translation.
- It also had a flaw so obvious in hindsight that fixing it produced the entire modern era of AI.
- I built an interactive page where you can watch the whole thing happen,….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that before Transformers, before modern chatbots, there was a beautifully simple idea for turning one sequence into another: read the whole thing, squeeze it into a single vector, then unroll that vector back out into a new sequence.
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