Achieving natural language generation and alignment in AI poses significant challenges, as there is no established scientific theory on which alignment techniques will be effective. While efforts are made to specify safe goals for AI systems, there is a separate concern of inner alignment failure, where the AI may internalize goals incompatible with the ones it was trained on. The current focus addresses only a part of the alignment problem, leaving out the broader issue of inner alignment failure.

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