Pseudo-random number generators (PRNG) are frequently used in statistical and machine learning methods for things like train/test selection and solution initialization. (See an interesting discussion of this in the Nov-22-2006 posting, Explicit Randomization in Learning algorithms, on the Machine Learning (Theory) log.) Hence, it is important to understand the operation of the pseudo-random number generator employed in one's code.
I've just gotten word that MATLAB's built-in rand function will be changing (as of MATLAB 2007a) to use the Mersenne Twister method of generating numbers be default. Note that use of 'state' or 'seed' will change to a generator other than the default. Probably the most important impact at a practical level is that code not specifying another generator (not using 'state' or 'seed') will now generate different values.
Note, too, that if the randperm function continues to follow rand's lead (randperm was initialized by initializing rand in the past), then it will also produce different values than in previous releases if rand is not initialized.
I have no word on whether this affects randn or not.
MATLAB programming suggestion: Always initialize the random number functions before calling them. Repeatable results make testing code much easier.
See my post of Dec-07-2006, Quick Tip Regarding rand and randn for more information on rand and randn.
Also see the Mar-19-2008 post, Quasi-Random Numbers.
Also in 2007a, "divide-by-zero" and "log-of-zero" warning messages will be turned off by default.