I'd respectfully disagree with this characterization of LLMs. While they certainly excel at pattern recognition, calling them mere "autocomplete machines" vastly undersells their capabilities. LLMs demonstrate complex reasoning, multi-modal understanding, and emergent behaviors that go well beyond simple pattern continuation.
They've succeeded in areas like mathematical problem-solving, creative tasks, and various real-world applications outside of GitHub Copilot. Sure, LLMs have limitations, but reducing them to autocomplete overlooks their nuanced language understanding and generation abilities. I also think the "lossy translation" argument doesn't give enough credit to the sophisticated ways problems can be reformulated for LLMs.
They've succeeded in areas like mathematical problem-solving, creative tasks, and various real-world applications outside of GitHub Copilot. Sure, LLMs have limitations, but reducing them to autocomplete overlooks their nuanced language understanding and generation abilities. I also think the "lossy translation" argument doesn't give enough credit to the sophisticated ways problems can be reformulated for LLMs.