I saw this quote in an interview with Teresa Meng (founder of Atheros) in ACM Queue regarding Signal Processing:
RB: How would you advise someone who wants to get involved with digital signal processing and new applications?
TM: You will have to master a few areas. You cannot just learn math and ignore the silicon, especially in signal processing where the algorithms really mean nothing unless you have an interesting, efficient way of implementing them. Then you immerse yourself in the application domain of your interest.
RB: Do you have any ideas on what you might do after the neural area?
TM: Probably something bio-related. I do feel that signal processing is the basic tool that can be applied to many different areas. We have applied it to low-power circuit designs, video processing, and, most recently, wireless communication. I think in the next several decades signal processing will be widely used in the bio field—for example, genome analysis or diagnostics. Signal processing is after all a science for optimal detection. I think there might be some interesting developments in those areas. [emphasis added]
I've never heard anyone describe signal processing this way. I interpret her as saying that signal processing is the science of finding patterns.
Also, given the broad definition of signal ("A signal is an abstract element of information"), signal seems synonymous with datum/data. Thus information processing seems synonymous with signal processing. A pattern is just a relationship, which is what a signal is.
Furthermore, in a later article in the ACM Queue issue on DSP, it is observed that DSP systems must optimize three concerns: speed, precision, and power. I would add a forth: adaptability (aka programmability). The three concerns are equivalent to my three concerns for liquidity: timeliness, value, and efficiency.
Finally, perhaps Signal Processing is the Science of Finding and Generating Patterns. Thus evolution (which finds and generates patterns) is a form of signal processing.
5:00:23 PM 
