According to Hofstadter, what is missing in machine translation like Google Translate?

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Question restated: According to Hofstadter, what is missing in machine translation like Google Translate?

Option 1: Nuance, subtlety, and cultural context
- This option points to features Hofstadter emphasizes as challenging for machines: the subtle shades of meaning, implied cultural knowledge, and the nuances that humans infer from context. The argument is that translation isn’t just word-for-word replacement; it requires grasping intent, idioms, humor, and social connotations, which are areas where current MT often falter.

Option 2: Speed and efficiency
- Speed and efficiency concern how fast a translation is produced and how computationally inexpensive the process is. While these are practical considerations, Hofstadter’s critique isn’t that MT is too slow or inefficient; it’s that it misses deeper interpretive elements, so speed isn’t the core missing piece here.

Option 3: Accuracy and precision
- Accuracy and precision relate to how closely a translation matches a source text. Although MT can struggle with accuracy, especially for ambiguous phrases or idioms, the term

01:356:156:02 EAP II Reading Notes: "The Shallowness of Google Translate"

According to Hofstadter, what is missing in machine translation like Google Translate?

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