On the topic of translation/ language learning, I still find Google Translate better for when I need to type emails in Russian (my older family who only speak it) as far as grammar is concerned than what LLMs spit out.
I'd be surprised if Google Translate isn't powered by a specialized, Transformer-powered model we would now recognize as a "small" language model heavily constrained to a specific task.
The transformer was invented as a machine-translation algorithm, later adapted to create LLMs. I think it's a reasonable hypothesis that a ML system designed for translation will always beat an LLM role-playing a translation system (assuming equal computing power and engineer effort).
We're going to see is a lot of stuff people use LLMs for moving back to using bespoke algorithms solving the one specific issue. LLMs are just machine learning unlocked for the masses, where you can ask it "translate this for me", "what's the sentiment of this text", and it just does it. Any of these were already achievable before LLMs, they just needed a machine learning engineer to implement.
I myself started an LLM driven project to classify and sort all the photos I've got. I just ask an LLM to poop out tags for an image. Using a proper algorithm for this is possible, but I'd need to put in some effort to actually get it set up. LLMs do make it easier since they can directly, instructively help you that set up, and provide an easy stream of training data to distill out a model perfectly fit to your usecase.
I think majority of work currently going through LLMs is such inefficient tasks that can be solved quicker and better with a limited algorithm.
Having an app means exactly nothing. How good your language skills will be after N years of learning is the actual measure of whether it's worth anything.
> ai code dev has enabled people to make tools for themselves that they didn't have before.
...that sounds pretty worthless tbh. The promise of AI wasn't that you can bang out your own little half baked duolingo clone. It was that every engineer was supposed to become hundreds or thousands of percent more productive. The assumption being that things generally would therefore get noticeably better.
We're seeing hundreds of percent more commits. We're not seeing that translate to any real world improvement of anything. That's the problem.
Call me "ignorant" if you want, but back it up by showing what all this "AI productivity" has actually accomplished in real economic terms. Not vaporware.
Out of curiosity, what real economic terms would you have to see to be convinced?
Do you mean more earnings per share for corporations or real utility impacts on social systems (e.g., more new drugs)
When I ask Claude about the AI ROI, it seems to cite that ~95% of AI PoC's are negative ROI but the 5% that do have ROI tend to have decent return -- my guess is mostly back office clean up to reduce expenses and increase revenue/profit.
IIRC Uber claim their 1.5k/mo budget lead to no new value creation.
FWIW, I look at the situation with similar skepticism. One argument you could make was what Marc Andreesen said with the hypothesis that "all the big companies get nuked", so no new valued gets created but the large market cap companies get eaten by thousands of little pirañas. Obviously, so far, that doesn't seem to be the case.
> Do you mean more earnings per share for corporations or real utility impacts on social systems (e.g., more new drugs)
I mean real utility impacts. More reliable and efficient software, better features, etc. So far we've seen none of that. In fact, it seems like the opposite--more volume but a much lower quality product.
Hang on! What's the proposed causal chain from AI to famine here? Inference -> energy prices -> fertilizer/food prices? Or are you referring to the war?
Most people would prefer an excellent language learning app to simply be available and ready for them to use, rather than having to come up with how such an app could best work, and to get an AI to implement it by themselves, and having to iterate on that. Just like most people simply want to buy nice clothes, and don’t want to become fashion designers and managers of a clothing factory. So far it doesn’t look like AI is making the former (readily available excellent apps) more widespread.
As someone also building a language-learning app for myself, it's not too hard to create an excellent app right now, but the "readily available" part of your requirements is now 10x harder than a few years ago. Users are getting bombarded by new apps, so marketing an app, which was already hard before AI, is now much, much harder.
I could try to market this app, but I tried that 2 years ago, and it was already essentially a failure. So I'm probably going to keep it for myself or just try to find a few users who like it and will use it for free.
I think I agree. But it's nice to have the option. Furthermore, i think it's too early to see the results of this, as technology takes a while to diffuse. Since there are so many people trying to take advantage of the hype to make a quick buck, it takes time to figure out what is actually useful and what is not
To give just a single example, ai code dev has enabled people to make tools for themselves that they didn't have before.
I've made a language learning app for myself. Its working better than Duolingo so far, for me. Its not really public