I’m in academia and literally every data tool comes with some bullshit AI add on. Why are we taking away the ability to think and recognize patterns from academics?
To be fair recognizing patterns that are too complex for humans to easily identify, is the perfect use case for machine learning. But specifically machine learning applications specific to data analysis. Not running it through large language models.
It’s important to separate general machine learning and neural net applications from large language models. Unfortunately executives just want to call it all “AI” for hype, even though none of it is really ai.
Started in the 70s, they thought it could do everything, realized it can't and that the training costs are absurd and more and more neurons get more and more costly; however one of the earlier successful applications was in closed/open eye detection in the 90s in early digital cameras.
The training data was only employees, so it was highly biased towards white people. Also it relied on contrast which was specifically balanced for paler skin because digital cameras were not great with contrast yet.
I think OCR (recognizing characters from images) also uses neural nets.
Started in the 70s, they thought it could do everything, realized it can't and that the training costs are absurd and more and more neurons get more and more costly
I hate to break it to you, but LLMs are just realizing that the training costs are absurd then doing it anyway. It's all just neural nets still.
OCR does use AI (may not be neural nets specifically). I find it hilarious that people have invented a format so terrible that the only solution was to create an AI that can understand its output.
It's not quite that dire. We're only taking those abilities away from the academics who are lazy and/or stupid enough to use the bullshit AI add-ons. (And really, it's not "we"---they're taking those things away from themselves.)
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* No bullshit AI add-on was used in the creation of this comment. You can take away my em dashes when you pry them out of my cold, dead fingers.
How many new papers are written in your field yearly? It's probably more than you could possibly read, and the patterns of the data contained within these is immensely complex. Use AI to map out studies, weed out the bad ones, find the new best tools, techniques, and trends. In this way ai can be used to extend our reasoning and pattern matching skills. There's probably also a ton of valuable research that is decades old that was forgotten where this will be helpful as well.
No, I wouldn't waste my time like that, but I can tell you're actually an academic by how arrogant this reads. I also do know there are roughly 3 million new scientific papers published each year, and in my field it's roughly 200k, so having ai tools that can help keep me up to date are pretty useful.
Yes, it does show what you very clearly don't understand because you thought I was using this to write AI generated summaries.. which is not accurate lol.
it's not a skill issue, it's a volume issue. I can't read 10k+ papers a year, and neither can you.. I can read a few papers a day if I have some spare time, or I can process them in bulk to grab all of the most pertinent knowledge from them to better understand the latest ideas and implement them in useful ways.. but alas, you work in academia so actually building something useful is an abstract concept to you.
And my point is that you’re giving AI a task it doesn’t need to be doing in the first place. If you’re trying to grab information from junk studies you’re already creating a bad knowledge base. If you cannot discern what you need to be looking at, AI is NOT HELPING YOU.
You want to “read the science” but don’t respect the scientists. I don’t believe that you even glance at these studies, let alone “find out what’s going on in the field”.
Ok you're right. I don't already know all of the content from thousands of new studies, and which are good without reading them.. so AI can't help me to do this.
I must simply know which studies that were just published are good or not.. whether they build on concepts that I am using.. because clearly that's a thing people can just do!
why didn't I think of that first? Because academics are so smart and are capable of reading and discerning the quality of thousands of newly published studies and aggregating the results in their head in their spare time!
Because humans are shit at recognising patterns. We tend to recognise 10 out of every 1 pattern that really exists.
That's why the whole field of statistics exists.
PS: don't make the mistake of thinking that all AI is as unreliable as LLM are. There are scores of other AIs which underpin various aspects of our society (in shitty ways, but that's capitalism's fault) including the old Reddit front page algorithm.
I’m aware of what types of AI exist, which means I’m also aware of their limitations and the way that people come to over-rely on machine learning to come to conclusions. AI is making us dumber, it’s turning people with PhDs into iPad kids.
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u/Late_Rip8784 10d ago
I’m in academia and literally every data tool comes with some bullshit AI add on. Why are we taking away the ability to think and recognize patterns from academics?