Ok so what is this publication? Because apparently they’ve been around since the 90s. I’ve never heard of them though. Their title and its reference suggests a very strong philosophical stance about something and I imagine that because of that they have political leanings, but I can’t tell what their leanings are
The Hedgehog Review? Yes, they've been around since 1999, and publish a few times a year. But I'm not sure where you're leaping to a strong political leaning. They're an academic journal published by the University of Virginia. I don't religiously follow them, but I've been cursorily aware of them for a while. I don't think I've ever considered them to lean one way or another when reading their publications.
An untidy history of AI across four books
21–30 of 42 posts
Re: An untidy history of AI across four books
#22Aravind Narayanan seems to be the only guy qualified enough to be called an expert.
That's one of the things that drives me nuts about all the public discourse about AI and our future. The vast majority of words written/spoken on the subject are by generic "thought leaders" who really have no greater understanding of AI than anyone else who uses it regularly.
> A great misunderstanding accounts for public confusion about thinking machines, a misunderstanding perpetrated by the unrealistic claims researchers in AI have been making, claims that thinking machines are already here, or at any rate, just around the corner.
> Dreyfus' last paper detailed the ongoing history of the "first step fallacy", where AI researchers tend to wildly extrapolate initial success as promising, perhaps even guaranteeing, wild future successes.
https://en.wikipedia.org/wiki/Hubert_Dreyfus's_views_on_arti...
Re: An untidy history of AI across four books
#23Earlier quoted context omitted.
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. I…
It's hard to miss the similarity between your book's title and Cliff Stoll's 1995 Silicon Snake Oil , an indictment of the general concept of the "information superhighway" that was starting to resonate with the public. Stoll is a really smart guy, but that particular book hasn't held up too well: Few aspects of daily life require computers...They're irrelevant to cooking, driving, visiting, negotiating, eating, hiki…
Our more recent essay (and ongoing book project) "AI as Normal Technology" is about our vision of AI impacts over a longer timescale than "AI Snake Oil" looks at https://www.normaltech.ai/p/ai-as-normal-technology
I would categorize our views as techno-optimist, but people understand that term in many different ways, so you be the judge.
Re: An untidy history of AI across four books
#24Earlier quoted context omitted.
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. I…
It's hard to miss the similarity between your book's title and Cliff Stoll's 1995 Silicon Snake Oil , an indictment of the general concept of the "information superhighway" that was starting to resonate with the public. Stoll is a really smart guy, but that particular book hasn't held up too well: Few aspects of daily life require computers...They're irrelevant to cooking, driving, visiting, negotiating, eating, hiki…
Re: An untidy history of AI across four books
#25Earlier quoted context omitted.
HN's own https://news.ycombinator.com/user?id=randomwalker !
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. I…
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.
Re: An untidy history of AI across four books
#26Earlier quoted context omitted.
HN's own https://news.ycombinator.com/user?id=randomwalker !
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. I…
Re: An untidy history of AI across four books
#27Read "brainmakers", even though it completely ignores Europe's and the East's significant contributions to AI history https://www.newquistbooks.com/brainmakers/brainmakers.html
Re: An untidy history of AI across four books
#28Re: An untidy history of AI across four books
#29Earlier quoted context omitted.
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. I…
what does irreducible error mean?
Re: An untidy history of AI across four books
#30Earlier quoted context omitted.
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. I…
> 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.
This was from 2017, and it made such an impression on me that I could find it on my first search attempt!