Jeffrey Shallit has a response - http://recursed.blogspot.com/2018/02/doug-hofstadter-flight-...
I think it is safe to say that Google Translate almost never macthes the skills of human translators except for rare trivial cases. As a rule it's rubbish, and sometimes as an exception it can be OK. Nobody argues against its usefulness, especially considering it's free and that it's pretty much the only tool we all use to understand things written in other languages. Better than nothing for sure.
Further, the whole analogy with flying and birds completely misses the point. Airplanes do satisfactory job at transporting humans and cargo, they are not required to fly like birds or mate with birds. We have invented what seems to be a more efficient method of flying using engines rather than flapping. All in all, airplanes are not birds and were never meant to be.
Similarly, in my view the arguments regarding machine translation come down to two things today: firstly whether it's useful or not, and secondly wether ML improves translation or not. My answers are: (1) weeeell, yeah, it's kinda useful in a better-than-nothing kind of a way and (2) doesn't seem like a significant improvement to me, if at all.
Depends on the language pair. Depends on the human translator. I met translators who wouldn't compete against 1990s Babelfish / SYSTRAN well. They still had some business.
Even though airplanes are different from birds, airplanes still need to follow the laws of physics and aerodynamics. Birds too need follow the laws of physics.
With intelligence, no one knows what the laws are. That is Hofstader's point.
What he means, which seemed quite obvious to me, is that the machine is not reading text, building up a semantic interpretation of the sentence in the way a human does. That’s because a neural net is a simple pattern recognition machine that does not work in the same way as the brain. It doesn’t have the immense life experience to draw upon (probably relatively easy to fix) but more importantly it doesn’t have a concept of what a sentence actually means.
A neural network doesn’t have an understanding of what “double” means. It just pattern matches a translation.
I think there’s some serious symbolic reasoning going on in the brain, which neural nets don’t yet perform. It all feels like shallow syntactic matching right now, rather than semantic reasoning.
Unfortunately I don’t know how the brain works, so you end up in an absurd argument where people say “the brain is just a neural net” and it’s impossible to completely refute their claims, just as if someone said “the brain is a very large lookup table”. Well, from what I see it does seem that the brain is much smarter than that, but I can’t be certain without knowing what that extra kicker is. So whilst such an assertion seems terribly simplistic and self-evidently insufficient, it is difficult to argue with.
It's a case of not having the right analogies. We liken various bodily systems to machines: the heart is a pump, the lungs are funnels, the kidneys are filters, etc. These work up to some point because we understand both sides of the analogy well enough- we understand how the heart works, to the extent that it works like a pump and we understand how pumps work, etc.
But with intelligence we don't have this luxury. We don't understand how intelligence works, yet we draw an analogy not just with computers ("the brain is a computational device") but with specific types of computer programs. However, there are, literally, an infinite number of different computer programs and an unknown number of them could produce results similar, or even identical, to our intelligence.
Of course understanding intelligence is basically coming up with a good model of it. But that must be preceded by a good understanding of how intelligence works, which we currently don't have. Instead, what some researchers do, is that they take their arbitrarily chosen favourite AI model and try to find a way to argue that it's "like" human intelligence.
Neural networks are particularly guilty of this sort of thing. The whole idea of connectionism is to mimic the way the brain does intelligence, however we don't know what that is, so we've just come up with a complex machine that can optimise systems of functions, instead (I mean the set of neural network architectures). Then, when this machine turned out to be good at doing what it was designed for, optimising systems of functions, we claimed this as proof that it's actually doing what the brain does. That's a very circular way of thinking.
When refuting his claims, he also makes some errors. Hofstadter has some very good reasons to justify what he says, whereas Shallit's argument is more or less "we can do slightly better translations now than before, so there is no reason they couldn't be better" - whereas the whole point of Hofstadter is that it's impossible to do exactly using current methods.
I understand both views and I think Shallit might have a point, but he doesn't justify it well. Simply by using statistical methods you can achieve surprisingly good results, although the error ratio is still too high IMO. What we already can do is to produce relatively good translations of texts belonging to well defined domains, such as legal documents. But in order to do it well with all domains, we'd need to find a good method of passing on the necessary context information to the translation engine.
Imagine translating subtitles of a movie. It's absurd to expect the machine will produce better results than a human as it's lacking visual cues. However, if we manage to transmit this information to the translation machine (via Computer Vision, audio profiling etc.), it can get much better results. It's very difficult to expect good results could be obtained just by training the neural networks based on previous movies. Yet, this is what Shallit seems to argue.
He may actually have a point there.
>> If mediocre translations can be done now without the requirements Hofstadter imposes, there is just no good reason to expect that excellent translations can't be eventually be achieved without them, at least in the same degree that Hofstadter claims.
Of course, there's no good reason to expect that excellent translations _can_ eventually be achieved in this manner, either. We just have to wait and see, according to Jeffrey Shallit.
On the other hand, we can already see that GT is not just "mediocre"; it fails in specific ways that suggest fundamental weaknesses of the way it does translation. By analogy, it's like having a flying machine that can only fly as long as it's anchored to the ground. There's no good reason to expect that such a machine will ever be able to do anything else than fly around the same spot.
So Hofstatder's article is not a discussion of requirements for good translation, only. It's also a discussion of limitations of Google Translate, that have to be overcome before it can consistently offer good translations.