For instance, I did not associate “model collapse” with artificial training data, largely because of my scope of thinking about what ‘well crafted training data’ must look like (in order to qualify for the description ‘well crafted.’)
Yet, some might recognize the problem of model collapse and the relationship between artificial training data and my speculation and express a negative selection bias, ruling out my speculation as infeasible due to complexity and scalability concerns. (And they might be correct. Certainly the scope of what I was talking about is impractical, at a minimum, and very expensive, at a maximum.)
And if someone does not engage with the premise of my comment, but instead simply downvotes and moves on… there does appear to be reasonable cause to apply an epithet of ‘epistemic inhumility.’ (Or would that be better as ‘epistemic arrogance’?)
I do note that instead of a few votes and substantially negative karma score, we now have a modest increase in votes and a net positive score. This could be explained either by some down votes being retracted or several high positive karma votes being added to more than offset the total karma of the article. (Given the way the karma system works, it seems unlikely that we can deduce the exact conditions due to partial observability.)
I would certainly like to believe that if epistemic arrogance played a part in the initial down votes that such people would retract those down votes without also accompanying the votes with specific comments to help people improve themselves.
Yet, some might recognize the problem of model collapse and the relationship between artificial training data and my speculation and express a negative selection bias, ruling out my speculation as infeasible due to complexity and scalability concerns. (And they might be correct. Certainly the scope of what I was talking about is impractical, at a minimum, and very expensive, at a maximum.)
I have no proof yet of what I’m going to say but: a properly distributed training data can be easily tuned with a smaller more robust dataset—this will significantly reduce the cost of compute to align AI systems using an approach similar to ATL.
a properly distributed training data can be easily tuned with a smaller more robust dataset
I think this aligns with human instinct. While it’s not always true, I think that humans are compelled to constantly work to condense what we know. (An instinctual byproduct of knowledge portability and knowledge retention.)
I’m reading a great book right now that talks about this and other things in neuroscience. It has some interesting insights for my work life, not just my interest in artificial intelligence.
Forgot to mention that the principle behind this intuition—largely operating as well in my project is yeah “pareto principle.”
Btw. Novelties, we are somehow wired to be curious—this very thing terrifies me of a future AGI will be superior at exercising curiosity but if such same mechanic can be steered—I see a route that the novelty aspect, a route as well to alignment or a route to a conceptual approach to it...
You make some good points.
For instance, I did not associate “model collapse” with artificial training data, largely because of my scope of thinking about what ‘well crafted training data’ must look like (in order to qualify for the description ‘well crafted.’)
Yet, some might recognize the problem of model collapse and the relationship between artificial training data and my speculation and express a negative selection bias, ruling out my speculation as infeasible due to complexity and scalability concerns. (And they might be correct. Certainly the scope of what I was talking about is impractical, at a minimum, and very expensive, at a maximum.)
And if someone does not engage with the premise of my comment, but instead simply downvotes and moves on… there does appear to be reasonable cause to apply an epithet of ‘epistemic inhumility.’ (Or would that be better as ‘epistemic arrogance’?)
I do note that instead of a few votes and substantially negative karma score, we now have a modest increase in votes and a net positive score. This could be explained either by some down votes being retracted or several high positive karma votes being added to more than offset the total karma of the article. (Given the way the karma system works, it seems unlikely that we can deduce the exact conditions due to partial observability.)
I would certainly like to believe that if epistemic arrogance played a part in the initial down votes that such people would retract those down votes without also accompanying the votes with specific comments to help people improve themselves.
I have no proof yet of what I’m going to say but: a properly distributed training data can be easily tuned with a smaller more robust dataset—this will significantly reduce the cost of compute to align AI systems using an approach similar to ATL.
I think this aligns with human instinct. While it’s not always true, I think that humans are compelled to constantly work to condense what we know. (An instinctual byproduct of knowledge portability and knowledge retention.)
I’m reading a great book right now that talks about this and other things in neuroscience. It has some interesting insights for my work life, not just my interest in artificial intelligence.
As a for instance: I was surprised to learn that someone has worked out the mathematics to measure novelty. Related Wired article and link to a paper on the dynamics of correlated novelties.
Forgot to mention that the principle behind this intuition—largely operating as well in my project is yeah “pareto principle.”
Btw. Novelties, we are somehow wired to be curious—this very thing terrifies me of a future AGI will be superior at exercising curiosity but if such same mechanic can be steered—I see a route that the novelty aspect, a route as well to alignment or a route to a conceptual approach to it...