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The Shallowness of Douglas Hofstadter.
If (or more likely, "When") computers translate better than human, respect for the human mind should increase. It's quite a tough problem to solve, way harder than manual translation which thousands of people have been doing for centuries.
You could've engaged with the substance of the article, instead of just calling Doug Hofstadter shallow and saying "no, you're wrong!".
I believe Hofstadter is more worried about people prematurely adopting this technology en masse simply because, by it's inherently impressive nature and mysterious inner-workings, it generates its own terrific PR which clouds the perception of its actual efficacy at a deeply cognitive task.
Kevin Kelly in his book What Technology Wants speaks to the same willingness for human societies to so eagerly jump into new, unproven or potentially dangerous technologies without ever stopping to consider the effects of the adoption and log-term usage on their societies. A whole chapter is about how the Amish culture is not necessarily anti-technology so much as they are slow, disciplined adopters of technology. They hold their people's culture above all other values including productivity or connectivity and so to them it's a clear decision to at first be skeptical of new technologies and to deliberate the adoption of these technologies until the elders of the community can come to a consensus. Even then they usually allow small "pilots" or testing of the technology with a select few individuals at first. Kelly doesn't suggest we adopt the Amish culture, simply that there is something to be learned here.
I think A.I would need to become a fully robust form of consciousness capable of generating its own novel ideas spontaneously and to form a sort of gestalt from a mix of information, logic, patterns but also "feelings" (or whatever term would be used for "feelings" in a conscious AI) in order to produce the same type of translation a human can. Granted, even without consciousness it will likely come extremely close over time with more training and improvements to the algorithms but as Hofstadter explains it takes more than a sort of algorithmic proficiency:
"..To me, the word “translation” exudes a mysterious and evocative aura. It denotes a profoundly human art form that graciously carries clear ideas in Language A into clear ideas in Language B, and the bridging act not only should maintain clarity, but also should give a sense for the flavor, quirks, and idiosyncrasies of the writing style of the original author. Whenever I translate, I first read the original text carefully and internalize the ideas as clearly as I can, letting them slosh back and forth in my mind. It’s not that the words of the original are sloshing back and forth; it’s the ideas that are triggering all sorts of related ideas, creating a rich halo of related scenarios in my mind. Needless to say, most of this halo is unconscious. Only when the halo has been evoked sufficiently in my mind do I start to try to express it—to “press it out”—in the second language. I try to say in Language B what strikes me as a natural B-ish way to talk about the kinds of situations that constitute the halo of meaning in question."
I predict we will prematurely adopt machine-learning powered translation at the cost of loosing depth, clarity and the richness of the human expression of ideas but for the majority of us the impact may be minimal especially if great human literatures are still translated by humans.
I think it's just as much that Hofstadter doesn't want people making overstated claims giving a bad name for AI research (again!) when people finally realize how limited the current approaches are.
I agree it's probably just as much a warning to prevent overstated claims in general as it's harmful for numerous reasons.
Translation is an "AI-complete" problem, if I may coin a phrase. Because language expresses human ideas, and languages do not map 1-1, proper translation can sometimes require a complete understanding of the source material (in order to properly circumlocute in the target language).
This is not "quite a tough problem to solve"; it is on par with the Turing test. Hofstadter is not complaining about how awful it would be if computers could pass the Turing test - I'm sure he would be quite as impressed as you - he is saying that given that Strong AI is pure fantasy at the moment, it would be a tragedy and an insult to language to abandon human translation in favour of a half-assed mechanical version.
I think Google translate is pretty amazing, but yeah, there's no need to declare game over. We are only beginning.
Overestimating progress goes back to the early days of computers. Seeing impressive results, people think the computer must possess something close to human intelligence on some level, because a human would have to be pretty smart to perform that well.
But no, with computers with have idiot savants.
Yeah, with all the people making ridiculously overstated claims about what deep learning can do I'm pretty sure we've got another AI winter coming.
And it's a shame. I really want to see more genuine progress in AI research; I really want to understand _what consciousness is_. But this boom/bust cycle that happens every time there's a tiny bit of real progress is a painfully inefficient way of getting there.
If there is a winter, I think it's going to be very different than in the past. What people used to call a "winter" was a drying-out of the funding for AI research. However, in the past, this research was funded primarily by public money, and specifically by defense budgets. And it was cut when scientists failed to produce the army of super robots the generals thought they were promised. In the present however, there is a lot of money put into AI research by the industry - Google, Facebook, Microsoft, Amazon and IBM, as well as many other, smaller companies.
The amount of investment in AI by those companies is simply unprecedented and so is the number of people who -drawn by this river of dosh, like moths to a flame- are pursuing AI as a career (even if that only means the statistical machine learning side of AI that those companies invest into).
What this means is that the current branch of AI has become "too big to fail". And that has nothing to do with how successful it is. As long as it can be monetised and the industry leads can show some return to their investment, "AI" will keep growing.
A winter, if it comes, will be a winter of knowledge- not of funds. We will end up with so much unusable, meaningless, laughably bad "research" that any significant contribution to knowledge will simply be buried under a ton of rubbish, never to be found out.
So the money will keep flowing in. But what will come out the other end will be utter nonsense.
(Agreeing with the rest of the comment) Unfortunately, it is not. There are several start-ups naively using it for cheap translations of product descriptions and reviews (e.g. Etsy, TripAdvisor, GoogleMaps reviews). At some point they started to translate everything to the system's language(?) by default, which rarely ever works. Even worse, the translations turn out so bad that it feels as if you are looking at a page made up of badly written spam mails. Not really a good way of gaining customer trust.
As - for instance on Etsy - I had a hard time displaying the original English product descriptions after they introduced it (the ones translated to German were simply indecipherable), I really wonder whether their popularity in non-English speaking countries has seen a dip after they introduced this side-widely.
I appreciate that Hofstadter explains the difficulty of translating so rigorously, and hope people in charge who have no decent experience with second language usage (and thus think Google Translate already does a "good-enough" job) listen.
Great article. Seems a logic continuation of his 1997 book "Le ton beau de Marot: In Praise of the Music of Language" (a book primarily about the challenges involved in translation), which I enjoyed when it came out.
Automatic image captioning research has some very impressive results in the recent years. Also there are other ideas in this domain that can handle language quite well (e.g. visual question answering). Interpreting classes in images as symbols, it appears as if symbolic and statistical approaches can in principle be combined. Wondering whether this can somehow be transferred to learning language for automatic translation.