Failure is an opportunity to learn
Failure is an opportunity to learn" has been strengthened to "No, rather, failure is an opportunity to learn.
I wonder if this difference in nuance can be translated into English.
When AI deliverables work and when they don't.
Learning from what went wrong is hard to share.
Stable operation phase? The data and the work to be entrusted become more contextualized and harder to share once you enter the
Tools of the trade
When you start doing a lot of AI work, you can't talk about AI.
As quality improves, it becomes invisible.
Roughly twice as many failures as successes.
relevance
People who are missing opportunities don't see opportunities.
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