Because people have been informally studying linguistics for all of human history (by trying to learn new languages in adulthood), it's surprising to me that we ever thought that natural language processing would be easily solved by computers. To me, it seems quite obvious that this is a fundamentally very challenging problem for computers, like theorem proving or painting, rather than accounting or printing.

I was born with the benefit of hindsight here, but I find it strange that computability theory was formalised in the 1930s (before we could actually build powerful computers), and yet decades later, leading experts still failed to realise that language processing was fundamentally difficult, and intractable on the hardware they had at the time. This seems like an obvious consequence of a number of facts which they did know, like "describing an algorithm is very different to having a normal conversation," and "texts are open to interpretation and cannot be perfectly translated into other languages."

What's curious about this statement (and forgive me if I am in error here; I am not extremely familiar with the machine translation field) is that the community chose to go down the route of "traditional grammars" and their rules, for the purposes of translation, rather than down the route of statistics to begin with.

Because as it was noted, this happened after WW2 and the advances in cryptography, which were by and large also driven by statistical analysis and other similar mathematically based advances; one would think that such insights would have carried over into the machine translation realm.

But they didn't - which is simply a curious historical footnote. It's also something we see often in history - particularly as it relates to technological advances; that there are certain paths that are taken that lead to virtual dead-ends decades later, but which the path that should have been taken was presaged by earlier work, yet for one reason or another wasn't pursued.

If we only had a way to avoid such "wrong turns", we could be much further along technologically - but of course, that would also be tantamount to "telling the future"...almost.

"Symbolic" techniques are the usual first choice because in order to get them to work reasonably well (i.e. beyond the 60%-80% threshold), you have to understand what they're doing.

The same thing happened in speech recognition. IBM started throwing statistical modeling and horsepower at the speech recognition problem, and folks like Kurzweil thought it was inelegant and not "true" speech recognition. However, it serves the purpose, and we know that humans use similar tricks when listening to augment pure recognition. It's harder to hear someone in a loud room if you can't see their mouth, or don't know the context of what they're saying.

People who think it's easy usually don't speak a 2nd language (amongst other problems in communicating with non-natives for example) so I'm going to bet on that.