Also long-established notation like integrals and differentials were once new and innovative, and paved the way for new discoveries.
[1] https://en.wikipedia.org/wiki/Einstein_notation
(edit: capitalize GR, grammar)
Also long-established notation like integrals and differentials were once new and innovative, and paved the way for new discoveries.
[1] https://en.wikipedia.org/wiki/Einstein_notation
(edit: capitalize GR, grammar)
It's a good notation, but writing a few sigmas in front of every equation is at worst a constant-factor overhead. I know of a text that does it that way in a (misguided?) attempt to be more approachable to nonspecialists.
Leibniz's notation opened up Calculus in ways that Newton's geometric analysis did not (which is why high school students are writing ∫x²dx and not doing a geometric analysis of that expression). This is also why programmers are looking for the bright shiny language that will let them express what they really mean in their code without ambiguity. I think that we might still be looking for the algebraic notation or Leibniz notation for programming that will make everything completely obvious and will make future generations look back at our C, Python, Rust, et al and marvel that we were able to do anything.
https://en.wikipedia.org/wiki/APL_(programming_language)
"A mathematical notation for manipulating arrays was developed by Kenneth E. Iverson, starting in 1957 at Harvard University. In 1960, he began work for IBM where he developed this notation with Adin Falkoff and published it in his book A Programming Language in 1962."
It didn't become an actual (usable) programming language until 1963.
I used to think this too, and spent years looking for that notation, but now I think I was wrong. I think any gains now from notational inventions will be dwarfed by further gains from scaling up neural nets. I think we've 'been living in Symbolia but now are in the age of Weightville.