Comment by ytdytvhxgydvhh
7 months ago
I think that’ll define the industry for the coming decades. I used to work in machine translation and it was the same. The older rules-based engines that were carefully crafted by humans worked well on the test suite and if a new case was found, a human could fix it. When machine learning came on the scene, more “impressive” models that were built quicker came out - but when a translation was bad no one knew how to fix it other than retraining and crossing one’s fingers.
As someone who worked in rules-based ML before the recent transformers (and unsupervised learning in general) hype, rules-based approaches were laughably bad. Only now are nondeterministic approaches to ML surpassing human level tasks, something which would not have been feasible, perhaps not even possible in a finite amount of human development time, via human-created rules.
The thing is that AI is completely unpredictable without human curated results. Stable diffusion made me relent and admit that AI is here now for real, but I no longer think so. It's more like artificial schizophrenia. It does have some results, often plausible seeming results, but it's not real.
Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable? It sucks when things don't always work, but that is also kind of life and if the AI version worked more often that is usually ok (as long as the occasional failures aren't so catastrophic as to ruin everything)
> Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable?
The key difference is how tolerant the specific use case is of a probably-correct answer.
The things recent-AI excels at now (generative, translation, etc.) are very tolerant of "usually correct." If a model can do more, and is right most of the time, then it's more valuable.
There are many other types of use cases, though.
A case in point is the ubiquity of Pleco in the Chinese/English space. It’s a dictionary, not a translator, and pretty much every non-native speaker who learns or needs to speak Chinese uses it. It has no ML features and hasn’t changed much in the past decade (or even two). People love it because it does one specific task extremely well.
On the other hand ML has absolutely revolutionised translation (of longer text), where having a model containing prior knowledge about the world is essential.
Can’t help but read that and think of Tesla’s Autopilot and “Full Self Driving”. For some comparisons they claim to be safer per mile than human drivers … just don’t think too much about the error modes where the occasional stationary object isn’t detected and you plow into it at highway speed.
relevant to the grandparent’s point: I am demoing FSD in my Tesla and what I find really annoying is that the old Autopilot allowed you to select a maximum speed that the car will drive. Well, on “FSD” apparently you have no choice but to hand full longitudinal control over to the model.
I am probably the 0.01% of Tesla drivers who have the computer chime when I exceed the speed limit by some offset. Very regularly, even when FSD is in “chill” mode, the model will speed by +7-9 mph on most roads. (I gotta think that the young 20 somethings who make up Tesla's audience also contributed their poor driving habits to Tesla's training data set) This results in constant beeps, even as the FSD software violates my own criteria for speed warning.
So somehow the FSD feature becomes "more capable" while becoming much less legible to the human controller. I think this is a bad thing generally but it seems to be the fad today.
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> For some comparisons they claim to be safer per mile than human drivers
They are lying with statistics, for the more challenging locations and conditions the AI will give up and let the human take over or the human notices something bad and takes over. So Tesla miles are miles are cherry picked and their data is not open so a third party can make real statistics and compare apples to apples.
Or in some cases, the Tesla slows down, then changes its mind and starts accelerating again to run over child-like obstructions.
Ex: https://www.youtube.com/watch?v=URpTJ1Xpjuk&t=293s
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Well Tesla might be the single worst actor in the entire AI space, but I do somewhat understand your point. The lake of predictable failures is a huge problem with AI, I'm not sure that understandability is by itself. I will never understand the brain of an Uber driver for example
yes, who exactly looked at the 70% accuracy of "live automatic closed captioning" and decided Great! ship it boys!
My guess: They are hoping user feedback will help them to fix the bugs later -- iterate to 99%. Plus, they are probably under unrealistic deadlines to delivery _something_.
But rule-based machine translation, from what I've seen, is just so bad. ChatGPT (and other LLM) is miles ahead. After seeing what ChatGPT does, I can't even call rule-based machine translation "tranlation".
*Disclaimer: as someone who's not an AI researcher but did quite some human translation works before.
I think NM translation was broken all along. Not in the neural network part but in choosing the right answer. https://aclanthology.org/2020.coling-main.398.pdf
They are just trying to kill all Mine also https://techpanga.com/roblox-image-ids/
Since LLMs are loosely based on NM models, it seems research on newer sampling methods like Mirostat might help here.
Perhaps using a ML to craft the deterministic rules and then have a human go over them is the sweet spot.
Rules could never work for translation unless the incoming text was formatted in a specific way. Eg, you just couldn't translate a conversation transcript in a pro-drop language like Japanese into English sentence-by-sentence, because the original text just wouldn't have sentences in it. So you need some "intelligence" to know who is saying what.