Imagine if LLMs remained mostly an academic interest for just a few years longer than it did before going commercial. How many issues could’ve been worked out by researchers and engineers with an eye towards scientific advancement rather than monetization?
Imagine if AI models were trained exclusively on peer-reviewed datasets, each one specialized in a single discipline, and maybe others specialized in interdisciplinary studies.
They might not be able to synthesize new ideas due to their fundamental architecture, but they could at least streamline certain tasks like literature reviews and metadata collation. They could provide sanity checks before submitting for review. Machine Learning models could even perform more complex data analysis tasks than LLMs would be capable of.
But no, instead we have Artificial Idiocy injected into everything, deepfakes and disinformation proliferating, and people going crazy from using chatbots to replace therapy…
I can’t help but to feel like this is happening with all AI. Social media comments from Facebook, Reddit, X etc are low effort and flushed out with bots.
This was predicted early on with LLMs that the information would eventually go into a feedback loop where the AI feeds off other AI hallucinations and they all go downhill fast.
I don’t remember having heard any practical solutions to the problem so far. They work best on real data, but they rapidly grew to the point where they are generating dramatically more artificial data than humans are generating real data, so they have hopelessly polluted their own well.
Its a very difficult problem to deal with no obvious solutions that are at all cheap, easy, or even feasible, so someone’s going to have a really, really smart idea for them to get over that hurdle. Add on to that the fact the types of AIs most impacted by his problem, the LLMs, are the ones that are currently the most heavily subsidized by venture capital. So, not only are they facing increasing technical hurdles, they are about to get increasingly expensive to operate at the same time as the seed funding is used up and they have to switch to a revenue-positive business model.
Interesting. Maybe they will have to start proactively surveying mass amounts of people instead of relying on free internet social media.
I don’t understand the appeal of AI for most things. The amount of incorrect information it gives is already too high making it unreliable. The benefit seems to be with brainstorming ideas or dealing with fiction.
An AI trained on Facebook comments would be stupider than an AI trained on nothing at all
Imagine if LLMs remained mostly an academic interest for just a few years longer than it did before going commercial. How many issues could’ve been worked out by researchers and engineers with an eye towards scientific advancement rather than monetization?
Imagine if AI models were trained exclusively on peer-reviewed datasets, each one specialized in a single discipline, and maybe others specialized in interdisciplinary studies.
They might not be able to synthesize new ideas due to their fundamental architecture, but they could at least streamline certain tasks like literature reviews and metadata collation. They could provide sanity checks before submitting for review. Machine Learning models could even perform more complex data analysis tasks than LLMs would be capable of.
But no, instead we have Artificial Idiocy injected into everything, deepfakes and disinformation proliferating, and people going crazy from using chatbots to replace therapy…
You may be saying this for comedy value but you’re totally right
Grok Vs Meta fighting for the title of artificial stupidity
I can’t help but to feel like this is happening with all AI. Social media comments from Facebook, Reddit, X etc are low effort and flushed out with bots.
This was predicted early on with LLMs that the information would eventually go into a feedback loop where the AI feeds off other AI hallucinations and they all go downhill fast.
Do you know how they planned to fix the problem?
I don’t remember having heard any practical solutions to the problem so far. They work best on real data, but they rapidly grew to the point where they are generating dramatically more artificial data than humans are generating real data, so they have hopelessly polluted their own well.
Its a very difficult problem to deal with no obvious solutions that are at all cheap, easy, or even feasible, so someone’s going to have a really, really smart idea for them to get over that hurdle. Add on to that the fact the types of AIs most impacted by his problem, the LLMs, are the ones that are currently the most heavily subsidized by venture capital. So, not only are they facing increasing technical hurdles, they are about to get increasingly expensive to operate at the same time as the seed funding is used up and they have to switch to a revenue-positive business model.
Interesting. Maybe they will have to start proactively surveying mass amounts of people instead of relying on free internet social media.
I don’t understand the appeal of AI for most things. The amount of incorrect information it gives is already too high making it unreliable. The benefit seems to be with brainstorming ideas or dealing with fiction.
Nothing is reliable. AI should get us into the habit of double checking everything.
Very true