21–30 of 366 posts
If a site aims to commoditize shared expertise, royalties should be paid. Why would anyone willingly reduce their earning power, let alone hand away the right for someone else to profit from selling their knowledge, unattributed no less. Best bet is to book publish, and require a license from anyone that wants to train on it.
Why open source anything, let alone with permissive licensing, right?
Stackoverflow mods and power users being arseholes reduces the use of Stackoverflow. ChatGPT is just the first viable alternative.
Sure the mods were arseholes etc.. but before gpt never minded using it .
It's a losing battle to try and maintain walled gardens for these corpuses of human-generated text that have become valuable to train LLMs. The horse has probably already bolted. I see this as a temporary problem however because LLMs are transitional. At some point it won't be necessary to train an LLM on the entirety of Reddit plus everything else ever written because there are obvious limits to statistical models l…
> Another issue is how much IP consumption constitutes theft? If an LLM watches every movie ever made, that's probably theft.
It's hard to reconcile those two views, and I don't think theft is defined by "how much" is being stolen.
Don't they just reduce the Q part of Q&A? And since the Q was A-d by AI doesn't that mean that A was there already and people just couldn't find it but AI did?
The answer by humans is a) publicly accessible b) hallucination-free (although it still may not be correct) c) subject to a voting process which gives a good signal of how much we should trust it. Which makes me think, maybe a good move for Stack Overflow (which does not allow the submission of LLM-generated answers, wisely imo) would be to add an AI agent that would suggest an answer for each question, that people c…
Stackoverflow mods and power users being arseholes reduces the use of Stackoverflow. ChatGPT is just the first viable alternative.
Earlier quoted context omitted.
Why open source anything, let alone with permissive licensing, right?
To a degree, yes. I only open source work where I expect reciprocal value from other contributions.
If a site aims to commoditize shared expertise, royalties should be paid. Why would anyone willingly reduce their earning power, let alone hand away the right for someone else to profit from selling their knowledge, unattributed no less. Best bet is to book publish, and require a license from anyone that wants to train on it.
Why open source anything, let alone with permissive licensing, right?
It's a losing battle to try and maintain walled gardens for these corpuses of human-generated text that have become valuable to train LLMs. The horse has probably already bolted. I see this as a temporary problem however because LLMs are transitional. At some point it won't be necessary to train an LLM on the entirety of Reddit plus everything else ever written because there are obvious limits to statistical models l…
> At some point it won't be necessary to train an LLM on the entirety of Reddit plus everything else ever written because there are obvious limits to statistical models like this and, as a counter point, that's not how humans learn. You may have read hundres of books in your life, maybe even thousands. You haven't read a million. You don't need to. I agree but I think it may be privileging the human intelligence mech…
Do you mean in theory or currently? Because currently, LLMs make simple errors (eg [1]) and are more capable of spitting out, well, nonsense. I think it's safe to say we're a long way from LLMs producing anything creatively good.
I'll put it this way: you won't be getting The Godfather from LLMs anytime soon but you can probably get an industrial film with generic music that tells you how to safely handle solvents, maybe.
Computers are generally good at doing math but LLMs generally aren't [2] and that really demonstrates the weaknesses in this statistical approach. ChatGPT (as one example) doesn't understand what numbers are or how to multiply them. It relies seeing similar answers to derive a likely answer so it often gets the first and large digits of the answer correct but not the middle. You can't keep scaling the input data to have it see every possible math question. That's just not practical.
Now multiplying two large numbers is a solvable problem. Counting Rs in strawberry is a solvable problem. But statistical LLMs are going to have a massive long tail of these problems. It's really going to take the next generational change to make progress.
[1]: https://www.inc.com/kit-eaton/how-many-rs-in-strawberry-this...
[2]: https://www.reachcapital.com/2024/07/16/why-llms-are-bad-at-...