Here’s what Claude Sonnet 4.5 suggested to take this piece from something that sounds impressive but lacks substance to something that could actually deliver on its promise. I did this thought exercise to explore whether being AI-generated necessarily precludes brilliance. You be the judge - I think Claude succeeded in mapping the gap between the current draft and what a truly excellent version would actually require.
Yeah -- I don't get why this is front-page -- reads like LLM quasi-insight:
"Through activation, lifeless equations became living systems. The neuron was no longer a mere calculator; it was a decider - a locus of transformation where signal met significance." -- wtf
More than that, it uses a lot of words to say so little.
Even a quick scan shows some pretty critical errors. In 76.4
Two parameters govern its perception:
• ( ): neighborhood radius
• ( MinPts ): minimum points per dense region
Or later in 76.7
In Fuzzy C-Means (FCM), each point (x_i) receives membership values (u_{ik}) in (
0,1
), satisfying (k u{ik} = 1). The objective is to minimize:
These are not human mistakes. They are categorically different
Also, the math really smells of AI. It has equations but it is like they have no substance. It has the form, but not the feeling. I know all this math and looking through I don't know how anyone could learn from such text. I'm not sure how it could even serve as a good reference. Where are the derivations? Where are the corollaries? Where are the implications? The extensions? The... depth?
0/10. I think you would be worse off by reading this
Why not just say what you want to say??? Surely these statistics are supposed to suggest some "obvious" conclusion, probably that the article is somehow bad. What do you mean by these numbers???
Then you may enjoy Elements of Statistical Learning[1] or Kevin Murphy's books[2][3][4], which this chapter is heavily indebted to; it is mostly a short gloss of the topics covered in those books.
Here’s what Claude Sonnet 4.5 suggested to take this piece from something that sounds impressive but lacks substance to something that could actually deliver on its promise. I did this thought exercise to explore whether being AI-generated necessarily precludes brilliance. You be the judge - I think Claude succeeded in mapping the gap between the current draft and what a truly excellent version would actually require.
https://claude.ai/share/46dd4b7e-9adf-473d-8372-22cb1ae34249
This book is open source and welcomes contributions. Would you like to join forces to create a truly brilliant version?
https://github.com/little-book-of/maths
"not a X, but a Y" - 8 matches
"is more than a X... it is Y" - 3 matches
"not just X, but Y" - 4 matches
Yeah -- I don't get why this is front-page -- reads like LLM quasi-insight:
"Through activation, lifeless equations became living systems. The neuron was no longer a mere calculator; it was a decider - a locus of transformation where signal met significance." -- wtf
More than that, it uses a lot of words to say so little.
Even a quick scan shows some pretty critical errors. In 76.4
Or later in 76.7 These are not human mistakes. They are categorically differentAlso, the math really smells of AI. It has equations but it is like they have no substance. It has the form, but not the feeling. I know all this math and looking through I don't know how anyone could learn from such text. I'm not sure how it could even serve as a good reference. Where are the derivations? Where are the corollaries? Where are the implications? The extensions? The... depth?
0/10. I think you would be worse off by reading this
Why not just say what you want to say??? Surely these statistics are supposed to suggest some "obvious" conclusion, probably that the article is somehow bad. What do you mean by these numbers???
They're saying it was written by an LLM because of the style of writing.
This is brilliant, thanks.
This is superbly written.
Then you may enjoy Elements of Statistical Learning[1] or Kevin Murphy's books[2][3][4], which this chapter is heavily indebted to; it is mostly a short gloss of the topics covered in those books.
[1]: https://hastie.su.domains/ElemStatLearn/
[2]: https://probml.github.io/pml-book/book0.html
[3]: https://probml.github.io/pml-book/book1.html
[4]: https://probml.github.io/pml-book/book2.html