1/ For every idea I have that actually works when researching and developing DeOldify, many, many more fail. One of the most important skills I've had to develop over the past year which is very different from software engineering is getting used to being wrong as the norm.
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2/ It's one thing to be wrong occasionally (as in software engineering) and to swallow your pride, correct course and move on. But having that be the norm can really be demoralizing.
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3/ It's tempting to fall in love with ideas and to start looking for evidence that you're right (confirmation bias) instead of trying to uncover the very likely scenario that you're wrong. This is a constant battle for me. I'd imagine this is true for others as well.
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Replying to @citnaj
Lana Sinapayen Retweeted
Tweet (1) is explained by the fact that you're not doing engineering anymore... It's one of the biggest signs that you've stepped into the room just next to it: science. In fact, tweet (2) is the only thing I need to know to say you're doing science
https://twitter.com/sina_lana/status/948940496268804096?s=19 …Lana Sinapayen added,
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Replying to @sina_lana
I've seen this pinned tweet before- really really like it. I do feel like this is good advice that extends outside of strictly science because really- things generally go better when you follow the evidence. "Good decisions end when...." etc
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Replying to @citnaj
Lana Sinapayen Retweeted Lana Sinapayen
This discussion reminds me of the "good vs bad test" discussion. Once you're optimizing for a measure (eg "being right") rather than for the actual goal (eg "knowledge"), you'll lose a lot of time as soon as measure and goal inevitably start diverging.https://twitter.com/sina_lana/status/1205677284591460352?s=19 …
Lana Sinapayen added,
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That's a great connection there. I've actually said before in a tweet that I think overly simplistic measures are causing a lot more problems than we're recognizing. This subject is near and dear to my heart. Adding ego as a category of "simplistic measures"- that's insightful!
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