[1] Can’t they both be not objective? Why make it a point of one or the other? A bit of a false dichotomy, there.
[2] There is no single “Internet”—there are specific spaces, forums, communities, blogs, you name it; comprising it. Each has its own, subjective, irrational, moderated (whether by a single individual, a team, or an overall sentiment of the community: promoting/exalting/hyping one subset of topics while ignoring others) mini/sub-culture.
This last one, furthermore, necessarily only happens to care about its own specific niche; happily ignoring most of everything else. LessWrong used to be mostly about, well—being less wrong—back when it started out. Thus, the “rationality” philosophy. Then it has slowly shifted towards a broader, all-encompassing EA. Now it’s mostly AI.
Compare the 3k+ results for the former against the 8k+ results for the latter.
Every space is focused on its own topic, within whatever mini/sub-cultural norms are encouraged/rewarded or punished/denigrated by the people within it. That creates (virtually) unavoidable blind spots, as every group of people within each space only shares information about [A] its chief topic of interest, within [B] the “appropriate” sentiment for the time, while [C] contrasting itself against the enemy/out-group/non-rationalists, you name it.
In addition to that, different groups have vastly different [I] amount of time on their hands, [II] social, emotional, ethical, moral “charge” with regards to the importance they assign to their topic of choice, and emergent from it come out [III] vastly different amounts of information, produced by the people within that particular space.
When you compile the data set for your LLM, you’re not compiling a proportionately biased take on different topics. If that was the case, I’d happily agree with you. But you are clearly not. What you are compiling is a bunch of biased, blindsided in their own way, overly leaning towards one social, semantic, political, epistemological position; sets of averaged sentiments. Each will have their own memes, quirks, “hot takes”. Each will have massively over-represented discussions of one topic, at the expense of the other. That’s the web of today.
When you “train” your GPT on the resulting data set then, who is to say whether it is “averaging” the biases in between different groups? Can you open up any LLM to see its exact logic, reasoning, argumentation steps? Should there be any averaging going on, after all—how is it going to account for disproportionately represented takes of people, who simply have too much time and/or rage to spare? What of the people, who simply don’t spend too much on the web to begin with? Is your GPT going to “average in” those as well, somehow?
What would prevent the resulting transformer from simply picking up on the likelihood of any given incoming prompt matching the overall “culture” of any single community, thus promptly completing it as if it was a part of an “average” discussion within that particular community there? Isn’t it plain wishful, if not outright naive*, to imagine the algo will do what you hope it will do—instead of what is the easiest possible thing for it to do?
* the fact a given thought pattern is wishful/naive doesn’t make you wishful/naive; don’t take it personally, plz
[1] Can’t they both be not objective? Why make it a point of one or the other? A bit of a false dichotomy, there.
[2] There is no single “Internet”—there are specific spaces, forums, communities, blogs, you name it; comprising it. Each has its own, subjective, irrational, moderated (whether by a single individual, a team, or an overall sentiment of the community: promoting/exalting/hyping one subset of topics while ignoring others) mini/sub-culture.
This last one, furthermore, necessarily only happens to care about its own specific niche; happily ignoring most of everything else. LessWrong used to be mostly about, well—being less wrong—back when it started out. Thus, the “rationality” philosophy. Then it has slowly shifted towards a broader, all-encompassing EA. Now it’s mostly AI.
Compare the 3k+ results for the former against the 8k+ results for the latter.
Every space is focused on its own topic, within whatever mini/sub-cultural norms are encouraged/rewarded or punished/denigrated by the people within it. That creates (virtually) unavoidable blind spots, as every group of people within each space only shares information about [A] its chief topic of interest, within [B] the “appropriate” sentiment for the time, while [C] contrasting itself against the enemy/out-group/non-rationalists, you name it.
In addition to that, different groups have vastly different [I] amount of time on their hands, [II] social, emotional, ethical, moral “charge” with regards to the importance they assign to their topic of choice, and emergent from it come out [III] vastly different amounts of information, produced by the people within that particular space.
When you compile the data set for your LLM, you’re not compiling a proportionately biased take on different topics. If that was the case, I’d happily agree with you. But you are clearly not. What you are compiling is a bunch of biased, blindsided in their own way, overly leaning towards one social, semantic, political, epistemological position; sets of averaged sentiments. Each will have their own memes, quirks, “hot takes”. Each will have massively over-represented discussions of one topic, at the expense of the other. That’s the web of today.
When you “train” your GPT on the resulting data set then, who is to say whether it is “averaging” the biases in between different groups? Can you open up any LLM to see its exact logic, reasoning, argumentation steps? Should there be any averaging going on, after all—how is it going to account for disproportionately represented takes of people, who simply have too much time and/or rage to spare? What of the people, who simply don’t spend too much on the web to begin with? Is your GPT going to “average in” those as well, somehow?
What would prevent the resulting transformer from simply picking up on the likelihood of any given incoming prompt matching the overall “culture” of any single community, thus promptly completing it as if it was a part of an “average” discussion within that particular community there? Isn’t it plain wishful, if not outright naive*, to imagine the algo will do what you hope it will do—instead of what is the easiest possible thing for it to do?
* the fact a given thought pattern is wishful/naive doesn’t make you wishful/naive; don’t take it personally, plz