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111 points ewf | 2 comments | | HN request time: 0.406s | source
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rishi_rt ◴[] No.45305201[source]
Aravind Narayanan seems to be the only guy qualified enough to be called an expert.
replies(3): >>45305738 #>>45305888 #>>45306515 #
dang ◴[] No.45305738[source]
HN's own https://news.ycombinator.com/user?id=randomwalker!
replies(1): >>45306256 #
randomwalker ◴[] No.45306256[source]
Thanks! HN was part of the origin story of the book in question.

In 2018 or 2019 I saw a comment here that said that most people don't appreciate the distinction between domains with low irreducible error that benefit from fancy models with complex decision boundaries (like computer vision) and domains with high irreducible error where such models don't add much value over something simple like logistic regression.

It's an obvious-in-retrospect observation, but it made me realize that this is the source of a lot of confusion and hype about AI (such as the idea that we can use it to predict crime accurately). I gave a talk elaborating on this point, which went viral, and then led to the book with my coauthor Sayash Kapoor. More surprisingly, despite being seemingly obvious it led to a productive research agenda.

While writing the book I spent a lot of time searching for that comment so that I could credit/thank the author, but never found it.

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dang ◴[] No.45307338[source]
> While writing the book I spent a lot of time searching for that comment so that I could credit/thank the author, but never found it.

Sounds like a job for the community! Maybe someone will track it down...

Edit: I tried something like https://hn.algolia.com/?dateEnd=1577836800&dateRange=custom&... (note the custom date range) but didn't find anything that quite matches your description.

replies(2): >>45308582 #>>45308624 #
1. z2 ◴[] No.45308582[source]
https://news.ycombinator.com/item?id=14944613

This was from 2017, and it made such an impression on me that I could find it on my first search attempt!

replies(1): >>45308654 #
2. ontouchstart ◴[] No.45308654[source]
“… machine learning everything that focuses on dealing with problems with a complex structure and low noise, and statistics everything that focuses on dealing with problems with a large amount of noise.”