If you can define intelligence in sufficiently precise terms, its implementation becomes easier.
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Failure to move forward is often not due to a lack of problem-solving capacity, but to failure of properly articulating goals, formulating relevant questions, and following appropriate measures of success.
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If the only successes of AI have been in developing narrow task-specific systems, it is because only within a highly specialized and grounded context are we able to *define the goal* sufficiently precisely, and to *measure progress*. "General AI" is far too vague to be actionable
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The solution must be co-evolved with the problem definition.
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End of conversation
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#1 predictor of ML eng success IME: task definition
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