Comment by zodiac | Hacker News Reader

Comment by zodiac | Hacker News Reader

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> Look at the promise of AI: "machines that think like humans!". Look at the reality: "We're generating value!". OK.

OK, I hadn't realised we had such different implicit views on what the "goal" of AI / AI research was. Of course, I agree that the goal of "having machines think like humans" is a valid goal, and "generates value by serving production traffic" is not a good subgoal for that. However, this is not the only goal of AI research, nor is it clear to me that for e.g. public funding bodies see it as the only goal.

I use MT (at least) every week for my job and for my hobbies, mostly translating stuff I want to read in another language. I love learning languages but I could not learn all the languages I need to a high enough level to read the stuff I want to read. The old non-NMT approaches produced translations that were often useless, whereas the NMT-based translations I use now (mostly deepl.com), while not perfect, are often quite good, and definitely enough for my needs. Without NMT, realistically speaking, there is no alternative for me (i.e., I can't afford to pay a human translator, and I can't afford to wait until I'd learned the language well enough). So how can you say that AI "remains completely useless"?

Basically, you have implicitly assumed that "make machines that think like humans" is the only valid goal of AI research. And, from that point of view, it is understandable that evaluating NMT systems for how well they approach that goal using human evaluations, has many downsides. However, while some people working on NMT do have that goal, many of them also have the goal of "help people (like zodiac) translate stuff", and in the context of that goal, human evaluation is a much better benchmark target.

In general, yes, that's it. But to be honest I'm actually not that interested in making "machines that think like humans". I say that's the "promise of AI" because it was certainly the goal at the beginning of the field, specifically at the Dartmouth workshop where John McCarthy coined the term "Artificial Intelligence" [1]. Researchers in the field have varying degrees of interest in that lofty goal but the public certainly has great expectations, as seen everytime OpenAI releases a language model and people start writing or tweeting stuff about how AGI is right around the corner etc.

Personally, I came into AI (I'm a PhD research student) because I got (really, really) interested in logic programming languages and well, to be frank, there's no other place than in academia that I can work on them. On the other hand, my interest in logic programming is very much an interest in ways to make computers not be so infuriatingly dumb as they are right now.

This explains why I dislike neural machine translation and similar statistical NLP approaches: because while they can model the structure of language well, they do nothing for the meaning carried by those structures which they completely ignore by design. My favourite example is treating sentences as a "bag of words", as if order doesn't make a difference- and yet this is a popular technique... because it improves performance on benchmarks (by approximately 1.5 fired linguists).

The same goes with google translate. While I'd have to be more stubborn than I am to realise that people use it and like it, I find it depends on the use case -and on the willingness of users to accept its dumbness. For me, it's good where I don't need it and bad where I do. For example, it translates well enough between languages I know and can translate to some extent myself, say English and French. But if I want to translate between a language that is very far from the ones I know - say I want to translate from Hungarian to my native Greek- that's just not going to work, not least because the translation goes through English (because of a dearth of parallel texts and despite the fact that Google laughably claims its model actually has an automatically learned "interlingua") and the result is mangled twice and I get gibberish on the other end.

I could talk at length on why and how this happens, but the gist of it is that Google translate stubbornly refuses to use any information to decide what translation to choose, among many possible translations of an expression, by looking at the frequencies of token collocations- and nothing else. So for example, if I ask it to translate a single word, "χελιδόνι", meaning the bird swallow, from Greek to French, I get back "avaler" which is the word for the verb to swallow- because translation goes through English where "swallow" has two meanings and the verb happens to be more common than the bird. The information that "χελιδόνι" is a noun and "avaler" is a verb exists, but Google translate will just not use it. Why? Well, because the current trend in AI is to learn everything end-to-end from raw data and without prior knowledge. And that's because prior knowledge doesn't help to beat benchmarks, which are not designed to test world-knowledge in the first place. It's a vicious circle.

So, yes, to some extent it's what you say- I don't quite expect "machines that think like humans", but I do want machines that can interact with a human user in a slightly more intelligent manner than now. I gave the example of SHRDLU above because it was such a system. I'm sad the effort to reproduce such results, even in a very limited domain, has been abandoned.

P.S. Sorry, this got a bit too long, especially for the late stages of an HN conversation :)

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[1] That was in 1956: https://en.wikipedia.org/wiki/Dartmouth_workshop

I hear you about those language pairs that have to be round-tripped through a third language. I completely agree, too, that the big open questions in NLP are all about understanding meaning, semantic content, pragmatics, etc rather than just syntax.

I don't think that "NMT and similar techniques" ignore meaning by design though. What they do do by design, compared to expert systems etc, is avoid having explicitly encoded knowledge (of the kind SHRDLU had). Take word2vec for instance, it's not NMT but fits into the "statistical NLP" description - its purpose is to find encodings of words that carry some semantic content. Now, of course it's very little semantic content compared to what an expert could plausibly encode, but it is some semantic content, and this content improves the (subjective human) evaluation of NMT systems that do use word2vec or something similar.

Also, we should carefully distinguish "prior knowledge" as in "prior common-sense knowledge" and "prior linguistic knowledge". The end-to-end trend eschews "prior linguistic knowledge", while current NLP systems tend to lack "common-sense" knowledge, for rather different reasons.

End-to-end training tends to eschew prior linguistic knowledge because it improves (subjectively evaluated) performance in real-world tasks - I believe this is true for MT as well, but an easier example if you want to look into it is in audio transcription. I don't think there's a consensus about why this happens, but I think it is something like - the previously way people were encoding linguistic knowledge was too fragile / simplified (think about how complicated traditional linguistic grammars are), and if that information can somehow be learned in the end-to-end process, that performs better.

Lacking "common-sense" knowledge - that's more in the realm of AGI, so there's a valid debate about to what extent neural networks can learn such knowledge, but the other side of that debate is that expressing common-sense knowledge in today's formal systems is really hard and expensive, and AIUI this is also something that attempts to generalize SHRDLU run into. But it is definitely incorrect to say that it's ignored by anyone by design...

BTW, the biggest improvements (as subjectively evaluated by me) I've seen in MT on "dissimilar languages" have come from black box neural nets and throwing massive amounts of (monolingual or bilingual) data at it, rather than anything from formal systems. I use deepl.com for Japanese-English translation of some technical CS material, and that language pair used to be really horrible in the pre-deep-learning days (and it's still not that good on google translate for some reason).

Sorry for the late reply again.

I agree about word2vec and embeddings in general- they're meant to represent meaning or capture something of it anyway. I'm just not convinced that they work that well in that respect. Maybe I can say how king and queen are analogous to man and woman etc, but that doesn't help me if I don't know what king, queen, man or woman mean. I don't think it's possible to represent the meaning of words by looking at their collocation with other words- whose meaning is also supposedly represented by their collocation with other words etc.

I confess I haven't used any machine translation systems other than google translate. For instance, I've never used deepl.com. I'll give it a try since you recommend it although my use case would be to translate technical terms that I only know in English to my native Greek and I don't think anything can handle that use case very well at all. Not even humans!

Out of curiousity, you say neural machine translation is better than earlier techniques, which I think is not controversial. But, have you tried such earlier systems? I've never had the chance.

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