He’s too cheap to pay for better data annotators. I heard a few weeks ago that I’m getting a fraction of what others are paid at other firms for the same work.
Hate to break it to you but quality of data isn’t the fundamental problem with LLMs. It’s that they are trying to use statistics to encode entire thought processes into hidden variables from conversation snippets. They want to use statistics to go from many individual interactions to a large model, and then use that model to predict individual interactions again. Which you can do with statistics, but it’s predicting the average text that follows the prompt, not the correct text (it has no concept of correctness; whenever it “talks” about it, that’s just the average text that follows, not any particular insight into what’s correct or even how it works).
That’s not to say that the quality of the training data has no impact; it can have a huge impact. I’m just saying that even if the training data was perfect, the LLM will still get things wrong in its output.
I listened to a podcast with a couple smart mathematicians talking about AI recently and this rings true based off what I heard them discuss.
They hypothesized that only verifiable domains can really see advances due to AI. So mathematics, physics, a load of the other sciences, and medical research. Even programming, as long as you have a pre-designed solution.
But for problems where you can’t look at a solution and say “yeah, that’s an optimal solution or close to it”, ie basically any business problem; they are much less useful, a big reason being what you mentioned in your comment.
He’s too cheap to pay for better data annotators. I heard a few weeks ago that I’m getting a fraction of what others are paid at other firms for the same work.
Hate to break it to you but quality of data isn’t the fundamental problem with LLMs. It’s that they are trying to use statistics to encode entire thought processes into hidden variables from conversation snippets. They want to use statistics to go from many individual interactions to a large model, and then use that model to predict individual interactions again. Which you can do with statistics, but it’s predicting the average text that follows the prompt, not the correct text (it has no concept of correctness; whenever it “talks” about it, that’s just the average text that follows, not any particular insight into what’s correct or even how it works).
That’s not to say that the quality of the training data has no impact; it can have a huge impact. I’m just saying that even if the training data was perfect, the LLM will still get things wrong in its output.
I listened to a podcast with a couple smart mathematicians talking about AI recently and this rings true based off what I heard them discuss.
They hypothesized that only verifiable domains can really see advances due to AI. So mathematics, physics, a load of the other sciences, and medical research. Even programming, as long as you have a pre-designed solution.
But for problems where you can’t look at a solution and say “yeah, that’s an optimal solution or close to it”, ie basically any business problem; they are much less useful, a big reason being what you mentioned in your comment.
Are you stating that you work for Meta?
And also that they’re a shitty data annotater.