I’ve been very fed up with AI for about past 6 months, completely started to boycott it, discuss it with people of all kinds of different views, and I found joy and pride once again in my own work.
But the world issues coming from AI, and even more so from the billionaires and empires behind it seem to further pile up to insane heights. I’ve been trying to learn more and more about it, and after my bachelor and masters I am considering pursuing phd and research surrounding AI, especially from the critical perspective, which seems to be deeply neglected in the research pov.
This is still a few years in the future, and lot could change, but I am curious what do people here think about pursuing such a thing, and if in current academic world it is even something that would be possible doing, given lot of the grants and funding of AI research comes from these companies that just want to gain even more power through it.
As I said, I am already trying to know as much as possible about it, but I would like to look more deeply into its impact on society, impact on students etc.
Do you think this would be a worthwile endevaour? And if not, where do you think I should be heading to make change about this while not completely starving to death?
I mean, ai isn’t inherently bad. It’s more of an issue how the big companies do push it.
Ai in research is phenomenal, especially in medicine applications. It’s not a black & white issue.
And we are just at the beginning of it, best of luck! U’d also need to differntiate between LLM(general use cases) and task/research specific ai.
Oh yeah, sorry for not specifying, I would like to critically study LLMs, and IF it gets more reasonable and susteinable, I would consider some of these other, more benefical AI uses.
Yeah, it’s really important to specify, what cases because they aren’t the same at all.
There’s even some really good uses cases for llms in companies. With a declining demographic most European countries face, a goal for company could f.e. be being more efficient, since (f.e. the company I worked for) 30-40% of staff will be in retirement age in the next 10 years. And if you work with documents (what we did) there’s a real benefit of llms classifying and compressing these documents. We speak of 10s of thousands a day. And the now used systems lack in flexibility to reliably classify or even read some of those documents. On top of that, you don’t need a 200B+ model for those tasks.
But that’s the good side in my eyes.
There’s loads more of problematic and socio economic issues with those models. Especially revolving around how people learn, decide and interact with each other.
You’re diving into a really broad field here and you’ll have to pick out very specific cases. It is for sure a super interesting field.
And on top of that, it’s a really old computer science field, dating back to the 60ies. It just now comes to “fruition” since our tech advanced so much that we can actually process these stupid amounts of data.
Before open ai & others popped up this all was labeled under computer linguistics & Data science, which just doesn’t sound as sexy I guess.
I think these days I just associate the term AI with LLMs or genAI in general, and I prefer “Machine learning” for the other models. I also believe, that for working with documents and data, we should look into other methods and ways instead of LLMs, since these models are usually extremelly “bloated” and just expensive and unsustainable to train and run. I might not have enough experience in the field “yet”, but I do believe there are other ways to go about it for the benefical tasks. And I won’t even talk about the data gathering methods that were used for these LLMs, which are not transparent at all, and mostly involve stolen work… That could be another topic worthy of research for sure.
I think if you open up a study, you should and probably need to be specific with the terms. Since llms are just large machine learning models. Just not trained for a single specific use case. You can also achieve very impressive results with small models, you don’t need chatgpt 5 for document classification. You can also fine-tune these models for specific tasks and/or “lobotomize” them. But f.e. go with a small qwen model with just 36B parameters or less and you will get very good results. And sure there are the good old OCR methods but you’ll need a significant pipeline behind a classic ocr machine. And it would probably still fail to decipher/classify a machine written document with hand written annotations. When you use a decent LLM, it will in most cases be able to differentiate between handwriting & machine letters, it will be able to output both in different variables and it might even be able to put the annotations in context to the original document. And this is an enormous task to program by hand.
And when we talk about speed and sustainability, not every document would be thrown at the expensive model first. But you would build a layered approach, so that 95% of the easy documents would be handled by a cheap and fast solution, but when that has a low confidence, then you would hand the document over to the bigger slower model.
Then add graphs or tables to the document and you’ll be nearly completely lost with a classic approach.
I’ve been working in this field for a couple years, so I speak from personal experience.
But still all those models still have an issue with context sizes and you and your business pipeline will fail if you don’t know the boundaries of what’s possible today. For the most high profile cases there should always be a human in the loop. Do companies do that? Most likely not, but they can get in big trouble if they make a critical mistake, at least in Europe, can’t speak for the wild West/US.
Note: You can self host qwen3.6 with 32gb or better 64gb and play it. It is shockingly good.
Data gathering and theft of IP is a completely different topic. But “luckily” many people now upload their data for free, directly to one of the big hosting companies. But privacy is also a different topic.
So again, be very specific if you choose your topic.
Of course yeah, if I would be writing actual research I would be more concious of the terms used. I see you have a lot of experience and have been using LLMs a lot and searching for ways to use them conciously, and that’s kind of part of the reason I would like to study them (and Machine Learning in general) more deeply. You seem to have the practical experience, but I would also like to personally look into it with the academic view, and see how these arguments really stand and how to improve the actual uses, while pushing against the bogus uses and claims that many people and mostly companies keep telling us. There’s still many years until then, maybe I’ll choose a different path to push against companies and the bullshit we are dealing with today… But I think this field might be something I am genuinely interested, and if I may actually do some worthwhile change during it, it would be a nice bonus…
I’d say most people who successfully run llms and do use it consciously are actual “AI-engineers” with a proper computer science background.
The people i’ve met who shout the loudest about their ai use are usually nothing burgers or management positions who didn’t come from an IT background and just see the golden goose “Ai” and want a piece of it.
If you want to get in touch with people having experience go and look for meetups :)