one-more-re-nightmare VS paip-lisp

Depends on your regex engine, and your non-regex solution. My engine (shameless self-plug https://github.com/telekons/one-more-re-nightmare) rivals hand-written automata, having to load each character more-or-less* only once, and throws in vectorisation for simple search loops too. I would not want to write or maintain the generated code.

one-more-re-nightmare used to let you write Σ, but I then tried to search Greek stuff with it and it went wrong. So now there's...$ for all characters (since that's not used for end-of-line assertions).

One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.

> One of my favorite examples is the regex library cl-ppcre. Thanks to the nature of Lisp, the recognizer for each regex you create can be compiled to native code on compiler implementations of CL.

That is not true - cl-ppcre generates a chain of closures. Experimental performance is in the same ballpark as typical "bytecode" interpreting regex implementations.

(Disclosure: I wrote another regex library at <https://github.com/telekons/one-more-re-nightmare>, which does do native code compilation.)

Someone else didn't tell me that before, so it can't be true. But I don't publish papers on toys, nor do I think toy projects are awfully fast. Though the x86-64 backend I wrote was in someone else's repository and thus was several PRs :(

Regular expressions

Also try this https://github.com/telekons/one-more-re-nightmare

A Common Lisp system has the compiler around at runtime, so if you can figure out how to profitably stage/specialise a computation, then you can roll your own cheap JIT of sorts. This can be useful for array munging and regular expressions at the least. You can do this in C, of course but you would need to use another compiler as a library (e.g. LLVM, TCC, libgccjit) or write your own (e.g. PCRE2's sljit).

Agree to disagree - I don't have the energy to remember operator precedence. One file from the regular expression compiler has most of the rewrite rules I read from the papers, except in S-expression syntax. There were a few bugs due to misreading precedence. Also c.f. Gerald Sussman talking about physics notation being a pain in the butt.

It's all part of the library. Everything about regular expression types is in this file.

PAIP by Peter Norvig, Chapter 23, Compiling Lisp

https://github.com/norvig/paip-lisp/blob/main/docs/chapter23...

Emacs is so much more than a text editor! But I need to stay on topic...

I believe your assessment of LISP (and therefore of MacArthy)'s impact on AI to be unfair. Just a few days ago https://github.com/norvig/paip-lisp was discussed on this site, for example.

Sounds like a great project idea to make a toy demo of this direction you'd like to see. Maybe comparable to https://github.com/norvig/paip-lisp/blob/main/docs/chapter15... and https://github.com/norvig/paip-lisp/blob/main/docs/chapter8.... which are a few hundred lines of Lisp each, but do enough to be interesting.

You could look into Paradigms of AI Programming by Peter Norvig which might interest you regardless of Lisp content.

Lisp has great power, examine PAIP, part II chapters 7 and 8.

A computer running Prolog reasons, and that only requires a couple of pages of code. So it seems feasible that the network could have learned some ability to reason within its network.

IMHO it's because lisp shines to manipulate symbols whereas the current AI trend is crunching matrices.

When AI was about building grammars, trees, developing expert systems builds rules etc. symbol manipulation was king. Look at PAIP for some examples: https://github.com/norvig/paip-lisp

This paradigm has changed.

Origen: Conversación con Bing, 4/4/2023(1) gigamonkey/monkeylib-binary-data - GitHub. https://github.com/gigamonkey/monkeylib-binary-data Con acceso 4/4/2023. (2) paip-lisp/chapter4.md at main · norvig/paip-lisp · GitHub. https://github.com/norvig/paip-lisp/blob/main/docs/chapter4.md Con acceso 4/4/2023. (3) bibliography.md · GitHub. https://gist.github.com/gigamonkey/6151820 Con acceso 4/4/2023.

If anyone is interested PAIP is downloadable at https://github.com/norvig/paip-lisp