4 ms·

Talking about things as broad and vague as "community" tends to obscure things more than it helps, in my opinion. A more useful exercise is to consider the fac

Talking about things as broad and vague as "community" tends to obscure things more than it helps, in my opinion. A more useful exercise is to consider the factors that go into people choosing a language in the first place. I don't think people usually think hard about how powerful or elegant the language is, at least when they're starting out, rather they pick it up when they need to solve a specific problem. For example, here's how I ended up using the languages I've learned in the past: QBASIC - Because I wanted to make video games when I was a kid, and things like functions and classes and scopes confused 10 year old me. The idea of executing a sequence of commands was simple enough to grasp. C - Because that's what all the "real" programmers used, and I had reached the limits of what I could do with basic. VB - Because writing a GUI in C sucks. Perl - Because that's just what you used for web pages at the time. Java - Because of college. C# - Because VB was pretty lame. Python - Because I needed something better to write small programs in, and the entirety of perl would regularly fall out of my head because it's too complex. F# - Because C#'s verbosity gets on my nerves and everyone was all up on functional programming; but I found the library support in most other functional languages lacking. Ruby - Because it's kind of the lingua franca of web startups at the moment. And on and on. I'd say about half the languages up there are actually pretty awful, and none of them are as good as LISP, but they did something specifically useful for me at the time I learned them. I can't really say that about LISP, because LISP doesn't really have a niche. The reason I ended up learning LISP (Clojure specifically) was because I wanted to be a better programmer, but I don't think that's really a strong enough argument for it if people want it to "win"

chegra84 15y ago
For 10 pts, Lisp niche is AI?
- dudeguy999 15y ago
  Lisp's niche is AI in the 1960s.
  - treetrouble 15y ago
    Unfortunately I don't have a source to cite but I believe the LISP/AI connection started in the 1960s and peaked in the 1980s.
- bfrs 15y ago
  In the 60s the dominant ideology was that symbolic manipulation was all there is to AI (Society of Mind)[1]. Lisp (List Processing language) was the obvious choice for this as it is very good for string manipulation (emacs and elisp are proof of this). Also the ability to express code as lists or s-expressions made it possible to write program-writing-programs or macros which turned out to be very effective for text crunching. Today, statistical learning is considered to be a better approach to building an AI. Much of the techniques of ML are expressed easily and efficiently using linear algebra for which Matlab (Matrix Labaratory) or Octave is much better suited. I think Lisp fell out of favor as it is terrible when it comes to number crunching. [1] The dramatic collapse of this ideology is chronicled in this wired article (Two AI Pioneers. Two Bizarre Suicides. What Really Happened?): http://www.wired.com/techbiz/people/magazine/16-02/ff_aimystery?currentPage=all http://www.wired.com/techbiz/people/magazine/16-02/ff_aimyst...
  - wickedchicken 15y ago
    > Today, statistical learning is considered to be a better approach to building an AI. Much of the techniques of ML are expressed easily and efficiently using linear algebra for which Matlab (Matrix Labaratory) or Octave is much better suited. The first statement is true: statistical learning is considered to be the leading edge of current AI research, especially since it attacks combinatorial explosion head-on. However, linear algebra is the baseline of current work, rarely the driving force. While the 'pull eigenvectors out of shitloads of data" method has been very effective and profitable, it is a very shallow approach to AI (especially considering the problems attacked in the 60s). Luckily, more intelligent algorithms are being developed that combine symbolic and probabilistic reasoning in a more direct way: compare LSA to LDA for example; LDA has a more cohesive and explanatory structure. These kind of things demand flexible programming environments (such as Lisp): Matlab is where algorithms go once they've been proven useful for 'normal people;' it is the domain of the analyst who cares about the data instead of the algorithm itself :).
    - bfrs 15y ago
      Matlab is a very flexible and productive environment for doing exploratory programming. In my experience it even beats python (ipython + (emacs+auto-complete))[1]. The ability to execute small blocks of code in sequence using the %% notation for marking blocks was[2] a real joy to work with. I thought C++ (.mex or .oct files) are where algorithms go once they've been proven useful for 'normal people'. [1] http://bfrsblog.blogspot.com/2011/10/making-emacs-first-class-python-ide.html http://bfrsblog.blogspot.com/2011/10/making-emacs-first-clas... [2] How I miss the free Matlab license at the Uni. Last I checked a commercial license costs about $2000. So, now I've been forced to shift to Octave. Heck, if Octave is good enough for Andrew Ng, I guess its good enough for me.
hello_moto 15y ago
I heard that argument many dozen times regarding certain type of programming languages can make me a better programmer. Boy I think I took that advise from the wrong source. Math can make me a better programmer. Statistics can make me a better programmer. Accounting can make me a better programmer. Biology can make me a better programmer. Chemistry can make me a better programmer. Building Macros and DSLs are the new Ivory Tower of software architecture.
- dmpk2k 15y ago
  Sadly, it took me five years of lost time mucking around with every language under the sun to figure this out. Spend a few months to find a reasonable ecosystem (NB: the libraries, tools and community, not just the language), then move on.
- neilk 15y ago
  I must strongly disagree with this. Learning functional programming was one of the best things I ever did. It gave me a huge advantage when tackling certain problems, which helps me to this day. At my current workplace people call me in to help when they have tricky problems because they are aware I have a different skillset. I started out in 'scripting' languages, and I can even say that learning Java, in a group with some very skilled OO gurus, was a mind-expanding experience. I have even seen it happen, en masse, at a company which was composed of various language partisans uniting under one roof. After exposure to each others' ways, I think we all become better programmers for it.
  - bunderbunder 15y ago
    I agree, knowing it can be huge. I'd even go so far as to say that at work, colleagues who know functional programming generally write code that is noticeably more efficient and maintainable. And on some platforms (.NET since 2006 comes to mind), you simply have to be comfortable with FP to be able to work effectively. But on the other hand, the days where one could get away with treating 'LISP' as a proxy for 'Functional Programming' are long gone. Aside from the hoary old beasts from the 20th century such as Java, pretty much all the major languages have first-class procedures and suchlike nowadays. LISP is no longer distinguished by having good support for functional programming; it's distinguished by lacking good support for procedural programming.
einhverfr 15y ago
Let's see: I learned Basic because I had a C64 (show of hands: anyone else?) I learned Fortran because my Running Start advisor didn't think 8 credits of C in a term would fit into high school credits well and I had to match HS and college credits according to an arcane formula. Ok, I still shiver at the thought of Fortran.... I learned some C years later because it was a "serious" programming language. Not very well though. PHP because it seemed like a good idea at the time. Perl because I wanted to apply for jobs using it., Python because I was unemployed for a bit and bored. Ruby because a customer asked me to teach them (yes, I objected saying I didn't know the language. I was told "that's ok!") and so on. However the things that have made me better have not been knowing all these languages but knowing the human side of the problems I am solving. I taught myself accounting to run my own business, never knowing that it would become my life (I now write open source accounting software). So if I were to give a new programmer advice, it would be: 1) Learn a few languages, because then you can learn new ones easily. 2) Learn about databases, not just SQL, but also as much about the low level implementation as you can. 3) Learn about problems and other unrelated disciplines so you can better solve the problems. So yes, math, statistics, accounting, biology, chemistry, permaculture, etc. are all good things to study.

chegra84 15y ago

For 10 pts, Lisp niche is AI?

dudeguy999 15y ago
Lisp's niche is AI in the 1960s.
- treetrouble 15y ago
  Unfortunately I don't have a source to cite but I believe the LISP/AI connection started in the 1960s and peaked in the 1980s.
bfrs 15y ago
In the 60s the dominant ideology was that symbolic manipulation was all there is to AI (Society of Mind)[1]. Lisp (List Processing language) was the obvious choice for this as it is very good for string manipulation (emacs and elisp are proof of this). Also the ability to express code as lists or s-expressions made it possible to write program-writing-programs or macros which turned out to be very effective for text crunching. Today, statistical learning is considered to be a better approach to building an AI. Much of the techniques of ML are expressed easily and efficiently using linear algebra for which Matlab (Matrix Labaratory) or Octave is much better suited. I think Lisp fell out of favor as it is terrible when it comes to number crunching. [1] The dramatic collapse of this ideology is chronicled in this wired article (Two AI Pioneers. Two Bizarre Suicides. What Really Happened?): http://www.wired.com/techbiz/people/magazine/16-02/ff_aimystery?currentPage=all http://www.wired.com/techbiz/people/magazine/16-02/ff_aimyst...
- wickedchicken 15y ago
  > Today, statistical learning is considered to be a better approach to building an AI. Much of the techniques of ML are expressed easily and efficiently using linear algebra for which Matlab (Matrix Labaratory) or Octave is much better suited. The first statement is true: statistical learning is considered to be the leading edge of current AI research, especially since it attacks combinatorial explosion head-on. However, linear algebra is the baseline of current work, rarely the driving force. While the 'pull eigenvectors out of shitloads of data" method has been very effective and profitable, it is a very shallow approach to AI (especially considering the problems attacked in the 60s). Luckily, more intelligent algorithms are being developed that combine symbolic and probabilistic reasoning in a more direct way: compare LSA to LDA for example; LDA has a more cohesive and explanatory structure. These kind of things demand flexible programming environments (such as Lisp): Matlab is where algorithms go once they've been proven useful for 'normal people;' it is the domain of the analyst who cares about the data instead of the algorithm itself :).
  - bfrs 15y ago
    Matlab is a very flexible and productive environment for doing exploratory programming. In my experience it even beats python (ipython + (emacs+auto-complete))[1]. The ability to execute small blocks of code in sequence using the %% notation for marking blocks was[2] a real joy to work with. I thought C++ (.mex or .oct files) are where algorithms go once they've been proven useful for 'normal people'. [1] http://bfrsblog.blogspot.com/2011/10/making-emacs-first-class-python-ide.html http://bfrsblog.blogspot.com/2011/10/making-emacs-first-clas... [2] How I miss the free Matlab license at the Uni. Last I checked a commercial license costs about $2000. So, now I've been forced to shift to Octave. Heck, if Octave is good enough for Andrew Ng, I guess its good enough for me.

hello_moto 15y ago

I heard that argument many dozen times regarding certain type of programming languages can make me a better programmer. Boy I think I took that advise from the wrong source. Math can make me a better programmer. Statistics can make me a better programmer. Accounting can make me a better programmer. Biology can make me a better programmer. Chemistry can make me a better programmer. Building Macros and DSLs are the new Ivory Tower of software architecture.

dmpk2k 15y ago
Sadly, it took me five years of lost time mucking around with every language under the sun to figure this out. Spend a few months to find a reasonable ecosystem (NB: the libraries, tools and community, not just the language), then move on.
neilk 15y ago
I must strongly disagree with this. Learning functional programming was one of the best things I ever did. It gave me a huge advantage when tackling certain problems, which helps me to this day. At my current workplace people call me in to help when they have tricky problems because they are aware I have a different skillset. I started out in 'scripting' languages, and I can even say that learning Java, in a group with some very skilled OO gurus, was a mind-expanding experience. I have even seen it happen, en masse, at a company which was composed of various language partisans uniting under one roof. After exposure to each others' ways, I think we all become better programmers for it.
- bunderbunder 15y ago
  I agree, knowing it can be huge. I'd even go so far as to say that at work, colleagues who know functional programming generally write code that is noticeably more efficient and maintainable. And on some platforms (.NET since 2006 comes to mind), you simply have to be comfortable with FP to be able to work effectively. But on the other hand, the days where one could get away with treating 'LISP' as a proxy for 'Functional Programming' are long gone. Aside from the hoary old beasts from the 20th century such as Java, pretty much all the major languages have first-class procedures and suchlike nowadays. LISP is no longer distinguished by having good support for functional programming; it's distinguished by lacking good support for procedural programming.

einhverfr 15y ago

Let's see: I learned Basic because I had a C64 (show of hands: anyone else?) I learned Fortran because my Running Start advisor didn't think 8 credits of C in a term would fit into high school credits well and I had to match HS and college credits according to an arcane formula. Ok, I still shiver at the thought of Fortran.... I learned some C years later because it was a "serious" programming language. Not very well though. PHP because it seemed like a good idea at the time. Perl because I wanted to apply for jobs using it., Python because I was unemployed for a bit and bored. Ruby because a customer asked me to teach them (yes, I objected saying I didn't know the language. I was told "that's ok!") and so on. However the things that have made me better have not been knowing all these languages but knowing the human side of the problems I am solving. I taught myself accounting to run my own business, never knowing that it would become my life (I now write open source accounting software). So if I were to give a new programmer advice, it would be: 1) Learn a few languages, because then you can learn new ones easily. 2) Learn about databases, not just SQL, but also as much about the low level implementation as you can. 3) Learn about problems and other unrelated disciplines so you can better solve the problems. So yes, math, statistics, accounting, biology, chemistry, permaculture, etc. are all good things to study.