For example, I just read the Lawrence Ellsworth translation of The Three Musketeers, which I very thoroughly enjoyed. I don't speak or read French, but from my understanding Ellsworth's translation is considered one of the more accurate translations of the work.
Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
They were honestly remarkably similar. As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation. I do think that the prose for the Ellsworth translation was a bit better, but the prose for the Fable one was actually perfectly readable. Again, I don't speak French so I cannot say for sure, but I do not believe that I would have gotten a significantly different experience had I read the Fable version instead of the Ellsworth version.
Now, it's possible (and likely) that this is somewhat self-fulfilling; Fable might have been trained using Ellsworth's translation and as such it's very directly able to crib from it; sadly since I do not speak any language outside of English, there's sort of a catch-22: the only way I can compare the accuracy of a translation is to compare against other translations, but if other translations exist then that will likely influence the results, and if a translation doesn't already exist then I have no way of auditing it.
I'm still going to continue reading through Ellsworth's translations for the subsequent stories simply because that feels more canonical, and as I said I do think the prose was a bit better.
This isn’t a great test, because Claude almost certainly has multiple translations of The Three Musketeers in its training data.
I'm pretty sure the Ellsworth translation is in the corpus. You basically instructed claude to regurgitate it.
The llms all have the more famous books memorized. You can trick them to recite them more or less word for word.
Crucially the full translation was part of ChatGPT’s training set. Recall is a pretty solved problem in machine learning.
How well does it translate a French novel published yesterday? Where neither the original novel nor any translations are in the training set yet? Or might not even exist!
I tried asking ChatGPT to translate a letter I wrote in Slovenian this weekend. It got the general gist but missed a lot of the nuance. Completely missed several of the little touches of tone where the right choice of synonym conveys a whole bunch of information.
Glad we agree :)
But yeah, I broadly do agree; if I read other languages I could find a book that hadn't been thoroughly translated to English and then I could give a proper analysis on how good the translation is, but since I'm a very stereotypical American I know exactly one language (and sometimes my comprehension of even that is questionable).
This reminds me of the adage, that ChatGPT is really great at everything except my own work.
I suspect if I knew another language I would be able to find errors in the translation.
So i guess in the end it just matters how important the work is.
A raw "word for word" translation (which I also tried) made the story somewhat hard to follow and very dry, but just asking it to keep the same kind of jovial swashbuckling tone of the original made something pretty similar to Ellsworth's translation.
Again, before someone decides to "correct" me on this, I am aware that it's very likely that the Ellsworth translations are part of the training set so it's not directly a fair comparison.
The number of lies, lies by omission, deceptive distortions, and fallacious argument tactics they generate is absurd, and increasing rapidly. Translation, when done as a service you are paid for, can't be relied on by propaganda bots.
But even the good engineers should likely be a little worried.
As one of such people, I think there is a nuance to it. AI is great when you’re translating something to yourself. But when translating things for others, more caution and human judgement is needed. Espesially when translating instruction manuals, where bad wording could cause someone to injure themself.
I can confidently say that LLMs do a better job than the average traditionally published fictions in my country, at least when the original works are in English. Every single time I watch a subbed movie there will be some lines noticeably wrong.
It can be reasonably argued that some poetry can be impossible to translate from some languages to others. A poem might be explained, but by a lenghty, dissecting explanation, that completely loses the point of it.
https://www.reddit.com/r/funny/comments/3e786n/chinese_hair_...
On the other hand, a lot of people become extremely put off by the smallest sign of ai slop. And the llms have a tendency to impart their style to any text they touch.
Update: in case it’s not obvious, I am sorry. I could not help it.
And here I am, brain the size of a galaxy, and I fumble my way through every language I speak other than English.
Serious respect for the linguists.
I have also taken to being sloppier in my prose, as I’ve had stories rejected for being “written by AI” - when they’re shorts I wrote more than a decade ago. Reworked them to sound like a moron, accepted. Sigh.
But now I find myself adding noise and imperfections to my writing (not that it was perfect) to make it more human, which is kinda silly.
A list of "Examples AI will silently fail at" would be a lot more interesting, and might just convince your next potential client to _not_ use AI.
The sad part is that we haven't figured out how to distribute our resources fairly to these people even thought their services aren't required as often. Instead we just take their wages and give them to the top 0.1%
AI isn’t replacing me. Like a toddler, it
needs to be constantly coached.
Humans are really bad at noticing trajectories. They see the current situation. They know what the situation was 5 years ago. But for some reason they do not believe that there is a trajectory. They view the present state as the final destination.
> “Oh, I can’t! It’s really not reliable enough.”
Gell-Mann Amnesia strikes again.
Translation is a gigantic boon for business, but just as important for human connection, for culture, science, art, and entertainment. The value of automatic and cheap translation between all languages, this tower of Babylon, is immeasurable.
Human translators will always be better than any AI at their job. But they don't have unlimited time and energy, and they aren't cheap. AI makes good to great translations available to everybody.
Specifically: LLMs make it really easy to misunderestimate the complexity of fields other than your own. (You can see this with a lot of vibecoded projects, for example – once they hit the wall of complexity, they stall out or start finding ugly patches for fundamental design issues, etc.)
I don't think this sort of cultural change will happen short-term, though.
In my experience this is a real problem. Just yesterday I asked my LLM to create a piece of software that could help me build an 'ambilight-like experience' through my home assistant. It did something that seems to work as I expected, but there is a lot of theory that I just brushed past. It would be pretty easy for me to assume that I would be able to replicate this feature from scratch 'now that I understand the problem'.
I still love the tool, but remain as convinced as ever that AGI does not lie at the end of this particular path.
Every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon.
Lemma: any statement about AI which uses the word "never" to preclude some feature from future realization is false.
Lemma: contemporary implementations have already improved; they're just unevenly distributed.
> Ah, you can’t fire me, I’m self-employed!
I don't understand thinking like this. I think companies can certainly fire their contractors.
There is already a tipping point now in software engineering where we prefer to ask AI instead of humans because we believe accuracy will be better, see SO death as an example or just see the current state of online dev communities, it's getting deserted and between team members at work, we can also notice that people speak less and less.
Sad but I believe it.
We would all do well to remember that and remember that each and every advancement and use case regarding AI is the result of choices by people (or the groups of people we call corporations) and are oftentimes motivated by the profit motive, not the best interest of humanity.
We could make different choices up to and including our own Butlerian Jihad where we ban all forms of AI but we could also do everything we can to prevent the worst fallout short of that.
There are only two types of problems in the universe: 1) those posed by the laws of physics 2) those posed by human choices
The problem of AI is one of the latter.
This plague of misanthropic doom is itself pretty depressing. Why do so many people think LLMs are in any way on a path to compete with human brains? Why do you think so little of yourself? The brain is magnificent and complex in ways that we are unable to decipher anytime soon, and it does way more than an LLM. Way, way more.
That's nonsense. There is zero reason to believe that AI (with the current techniques) will ever become reliable enough to let it do its own thing, let alone better than a human. It's been years of development and you still can't trust it to get basic facts correct, not even "well it's better than it used to be". Saying it'll replace humans in 5-10 years is a fantasy (or a prediction that people are stupid enough to fall for hype, I guess).
1. AI is a great boon for all tasks and specialties we don’t have the skills to do ourselves. Understandable, since (A) we’re ill equipped to see the flaws in its output because it isn’t our area of expertise, and (B) it often can unlock great gains because if we trust it, we then don’t have to pay and wait for humans to do that thing.
2. AI is a terrible replacement for me - my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do. It’s a tool at best.
This is why I use AI for all my medical questions and doctors use AI to write software, and we both smirk at the quality the other person is getting from it.
I'm not sure how to formulate it yet but it seems there is some Peter Principle/Gell-Mann Effect corollary that is AI-related we can say here.
Perhaps: "AI rises to the level of its users' incompetence."
Or: "Confidence in AI output is inversely proportional to one's ability to verify it"
Every month a new guy discovers LLMs; discovers a skill the current LLMs require to get good results; and writes about the future jobs that will always be available for smart people like HIM, that are SKILLED in using LLMs.
The next generation of AIs doesn't need his fancy prompt. The image model goes from needing to type in just the right set of weird words and cryptic sorcerous invocations, to most people being able to type in English what they want and get a pretty good result.
There are still tasks that require careful invocation. But they are a much smaller fraction of all the tasks people are trying to do, or you can get a bleh result without the elaborate invocation to get it really good. And to improve on the bleh result you need to be substantially more of an expert than back when the Guy was memorizing a rule about adding "trending on Artstation" to the image prompts, as would always require a human paid to do that.
Another generation of AIs comes out. The next generation of Clever Skills is obsolete. Image models just obey the instructions for compositing panels without mixing them up, and you don't need to be an expert to get them to do it right. Another human value-add is gone. A wider set of tasks require no human expert.
Now a new Guy notices LLMs have become useful in his field for the first time. He discovers they require SKILL to use CORRECTLY. He posts about how there will always be jobs for humans who are SKILLED in using LLMs like HIM.
But it is not an infinite cycle. It is not the same each time it repeats. Now the Guy is a highly paid programmer or a career mathematician in 2026, instead of a graphic artist in 2023.
In six months the models will no longer require his vaunted Skills.
And by then there will be another Guy.
But the process doesn't continue forever. The Guys are coming from fields that were harder and harder for AIs. The brief centaur eras are shorter and shorter.
Today it is writers who are laughing at how bad the LLMs are at their job, and who will perhaps soon be posting about how it takes Skill to get an LLM to do their job Correctly. But the models are coming faster, and the eras of kinds of human value-add in each field are shortening.
There is a point when you run out of Guys, either because the centaur eras are too short for people to develop SKILLs and post to Twitter about them; or because there are not lands left for AIs to conquer; or because ordinary people are not reassured by some Nobel laureate proclaiming there will always be jobs for Nobel laureates with the SKILLS to prompt robotized biology labs Correctly.
But we'll never run out of amateur economists who assert entirely without a brief contemporary example that there will always be jobs for humans skilled at operating AIs!
We'll run out of professional economists saying it when nobody is paid for that work anymore.
I guess we'll also run out of amateur economists when they're dead.
Source: https://x.com/allTheYud/status/2057136382817231151