this post was submitted on 09 Aug 2023
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I think you misunderstand the problem. Sure it starts with small amounts of output fed into the input, but as it continues to generate large amounts of output, overtime, more and more of the output makes it into the input.
And again, limiting LLMs to pre-2023 training data ensures they never get smarter. Human knowledge expands as LLMs at best are locked into a constant state of 2023 knowledge.
Not inevitably. You're assuming that each "generation" of AI is being trained on a data set that's just blindly harvested. AI trainers are already spending a huge amount of effort curating their training sets, it's become quite apparent that the quality of the training set is important and you can't just dump a giant raw pile of everything into it to get good results. This would just be another thing for them to consider.
To a certain extent, yes, the training data is blindly being dumped in. There's no way terabytes of training data is being manually reviewed for accuracy. If for no other reason, it doesn't economically make sense to do so. It's simply not feasible for humans to manually currate all of that data and even if they did, human error still exists.
Your disbelief doesn't mean it's not happening. The data sources that go into AIs are indeed curated selectively. Honestly, what do you think happens, a webcrawler is told to just "go nuts" and whatever random data it spits out gets fed right in? Trainers pick their sources carefully. They deduplicate it, they format it, they do a lot of work on it.
Perfection is not required. Human error is fine in manageable amounts.