Imagine you need to find the midpoint of a stick. You can measure its length, using a ruler (or making a ruler, using any available increment) and digitally compute the midpoint. Or, you can use a piece of string as an analog computer, matching the length of the stick to the string, and then finding the middle of the string by doubling it back upon itself. This will correspond, without any loss of accuracy due to rounding off to the nearest increment, to the midpoint of the stick. If you are willing to assume that mass scales linearly with length, you can use the stick itself as an analog computer, finding its midpoint by balancing it against the Earth's gravitational field.
There is no precise distinction between analog and digital computing, but, in general, digital computing deals with integers, binary sequences, and time that is idealized into discrete increments, while analog computing deals with real numbers and continuous variables, including time as it appears to exist in the real world. The past sixty years have brought such advances in digital computing that it may seem anachronistic to view analog computing as an important scientific concept, but, more than ever, it is.
Analog computing, once believed to be as extinct as the differential analyzer, has returned. Not for performing arithmetic — a task at which even a pocket calculator outperforms an analog computer — but for problems at which analog computing can do a better job not only of computing the answer, but of asking the questions and communicating the results. Who is friends with whom? For a small high school, you could construct a database to keep track of this, and update it every night to keep track of changes to the lists. If you want to answer this question, updated in real time, for 500 million people, your only hope is to build an analog computer. Sure, you may use digital components, but at a certain point the analog computing being performed by the system far exceeds the complexity of the digital code with which it is built. That's the genius that powers Facebook and its ilk. Your model of the social graph becomes the social graph, and updates itself.
In the age of all things digital, "Web 2.0" is our code word for the analog increasingly supervening upon the digital — reversing how digital logic was embedded in analog components, sixty years ago. The fastest-growing computers of 2010 — search engines and social networks — are analog computers in a big, new, and important way. Instead of meaningful information being encoded as unambiguous (and fault-intolerant) digital sequences referenced by precise numerical addressing, meaningful information is increasingly being encoded (and operated upon) as continuous (and noise-tolerant) variables such as frequencies (of connection or occurrence) and the topology of what connects where, with location being increasingly defined by fault-tolerant template rather than by unforgiving numerical address.
Complex networks — of molecules, people, or ideas — constitute their own simplest behavioral descriptions. This behavior can be more easily and accurately approximated by continuous, analog networks than it can be defined by digital, algorithmic codes. These analog networks may be composed of digital processors, but it is in the analog domain that the interesting computation is being performed.
Analog is back, and here to stay.