[wickedchicken]

> 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]

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-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.