The article doesn't mention "embedding" even once. How can an argument about discontinuities in language space leave out things like word2vec, etc, which are designed to make things continuous?
I don't think anyone deny that 'true' translation would require some kind of general intelligence that somehow understands what is being translated, but it seems to be the case that a 'dumb' translation works well enough for a great many use cases, regardless.
He's really just making the Chinese room argument. We have a computer shuffling symbols around according to some rule set, that doesn't know what they mean. I don't think it really matters, though, if it produces a reasonably accurate translation.
I think the counter to Searle's argument isn't really that it doesn't matter as long as the result is close enough. The counter to that is that we don't understand how human intelligence works either. Searle is simply assuming that it's "magic" (or less condescendingly, some sort of metaphysical process) that can't be simulated by algorithmic machine. I think it's far more likely that intelligence is physical and we just don't understand the machinery than it is that it's mystical and cannot in principle ever be understood.
For this article, all that is seemingly unnecessary. He's just saying they won't work well enough to even fake it convincingly. Which is very nearly falsifiable just by running today's algorithms.
But if you're at 75% per cent and want to get to 100%, you need to understand what the problem is with the rest. And if it's 75% of "perfect translation of single written sentences from newspapers or technical litterature", how far along is that towards something "being part of an everyday conversation"?
I studied linguistics at a university where focus was very much spoken language, sociolinguistics, language in context, before moving into (or through) NLP, and the distance between what a statistical machine translation system is able to handle and the stuff I used to work with is very large.
A lot of NLP work now seems to focus on algorithms, but intuitively it seems to me that a much larger issue is the quality of the data, in the sense that humans don't learn language from piles of isolated text and somehow we're expecting machines to do it.. Rext is a lossy encoding of spoken language, even if you try your best to mimic it, but more seriously it does not include the physical context that children encounter language in. The learning situations aren't the same, I don't know why we're expecting the results to be.
What machine translation isn't going to do is capture emotion or style or understand what someone is saying without them really saying it and so on. I would be very surprised if a machine translated novel ever hits the best seller charts. But I bet translators are going to be working from machine translated glosses if they aren't already.
The argument goes - "when you look inside, it's just things pushing at each other. How could that produce perception and the conscious mind?"
So, a failure of imagination and incredulity based on how they understand the world and the mind makes them reject AI. They feel that the special place of the soul was traded for "information processing" which is dry and mechanical - a form of dualism creeping up in our day and age.
I would have felt the same if I didn't learn and use neural networks such as CNNs, RNNs and MLPs. Now I know how simple mechanical systems can recognize patterns and process information to generate complex behavior and I don't feel that "explanatory gap" any more.
Reinforcement learning is a good base for consciousness research - much more precise and with scientific results, not just p-zombies and bat based armchair experimentation. There's a limit where you can go with just pure thinking and then you need to start direct implementation.