Four Years Remaining

Here's a curious quote from Alan Turing's famous paper from 1950:

Makes you appreciate how seriously one person's wishful thinking, coupled with dedication and publicity skills, may sometimes affect the scientific world.

A long time ago, information was stored and transmitted by people who passed it in the form of poems from mouth to mouth, generation to generation, until, at some moment, writing was invented. Some poems were lucky enough to be carefully written down in letters on the scrolls of papyrus or pergament, yet a considerable number of them was left unwritten and thus lost. Because in the age of writing, an unwritten poem is a non-existent poem. Later on came the printing press and brought a similar revolution: some books from the past were diligently reprinted in thousands of copies and thus preserved for the future. The remaining ones were effectively lost, because in the age of the printing press, an unpublished book is a nonexistent book. And then came the Internet. Once again, although a lot of the past knowledge has migrated here, a large amount hasn't, which means that is has been lost for most practical purposes. Because in the age of the Internet, if it is not in the Internet, it does not exist. The tendency is especially notable in science, because science is essentially about accumulating knowledge.

The effect of such regular "cleanups" (and I am sure these will continue regularly for as long as humankind exists) is twofold. On one hand, the existing knowledge is reviewed and only the worthy pieces get a chance to be converted into the new media format. As a result, a lot of useless crap is thrown away in an act of natural selection. On the other hand, a considerable amount of valuable information gets lost too, simply because it seemed useless at that specific moment. Of course, it will be reinvented sooner or later anyway, but the fact that it was right here and we just lost it seems disturbing, doesn't it.

I'm still browsing through that old textbook from the previous post and enjoying the somewhat unfamiliar way the material is presented. Bayesian learning, boolean logic and context-free-grammars are collected together and related to decision theory. If I did not know the publication date of the book, I could easily mistake this "old" way of presenting the topic, for being something new. Moreover, I guess that, with an addition of a minor twist, some ideas from the book could probably be republished in a low-impact journal and thus recognized as "novel". It would be close to impossible to figure out the copy, because a pre-Internet-era non-English text simply does not exist.

The day before I've accidentally stumbled upon an old textbook on pattern analysis written in Russian (the second edition of a book originally published in 1977, which is more-or-less the time of the classics). A brief review of its contents was enormously enlightening.

It was both fun and sad to see how smallishly incremental the progress in pattern analysis has been for the last 30 years. If I wasn't told that the book was first published in 1977, I wouldn't be able to tell it from any contemporary textbook. I'm not complaining that the general approaches and techniques haven't changed much, these shouldn't have. What disturbs me is that the vision of the future 30 years ago was not significantly different from what we have today.

Pattern recognition systems nowadays are getting more and more widespread and it is difficult to name a scientific field or an area of industry where these are not used or won't be used in the nearest future...

Further on the text briefly describes the application areas for pattern analysis that range from medicine to agriculture to "intellectual fifth-generation computing machines" and robots that were supposed to be here somewhere around nineties already. And although machines did get somewhat more intelligent, we have clearly failed our past expectations. Our current vision of the future is not significantly different from the one we had 30 years ago. It has probably become somewhat more modest, in fact.

Interesing, is this situation specific to pattern analysis or is it like that in most areas of computer science?