Great comment.
Probably also worth speculating on why this is happening NOW. Why is this breaking out of CS departments in 2011 and not 2002?
The datasets are new.
Bandwidth? Storage capacity? Computing power? All of the above?
Probably also worth speculating on why this is happening NOW. Why is this breaking out of CS departments in 2011 and not 2002?
The datasets are new.
Bandwidth? Storage capacity? Computing power? All of the above?
It wasn't until the 1990s that computers started becoming reasonably priced and more accessible to researchers and hobbyists that we began seeing an exponential growth in the amount of research output. In many way, one could argue that the proliferation and development of AI has very much followed Moore's law, since these are extremely complex and costly calculations.
Bandwidth increases have certainly increased the availability of data sets (Google has its entire ngrams data set fully available, and it's multiple terabytes in size), but storage capacity (hard disk, RAM, and CPU cache) and computing power have really formed the bottle neck. It's not just storage capacity, either: I/O read/write times are also immensely important. It's all just a huge balancing act right now.