4 ms·

I think it’s impossible for a human to read a mainstream body of minority political work and not come out with an association between black and oppressed. The e

by TimPC 5y ago

I think it’s impossible for a human to read a mainstream body of minority political work and not come out with an association between black and oppressed. The entire dominant narrative is that all minority groups are oppressed. That association is definitely present in the text. Maybe it’s the case that we need to explicitly remove all negative associations for things like skin colour (potentially a hard problem in its own right) to generate more egalitarian text. But it’s not merely a matter of AI getting things wrong some negative associations are actually present in the text.

mjburgess 5y ago
The association between "movie" and "tv" is "played on". The association between "jellybean" and "apple" is "smells similar". The association between "black" and "oppression" is "suffering". The association in each case is NOT likely to co-occur. AI is not detecting any kind of conceptual association. It is merely recording co-occurance. By interpreting co-occurance (statistical association) as meaningful, you are imparting a conceptual association to text it does not have. "Statistical association" is just a pun on "conceptual association". Machines do not detect associations; they record frequencies.
- bitcurious 5y ago
  > AI is not detecting any kind of conceptual association. It is merely recording co-occurance. Can you explain the famous example of “king - man + woman = queen” through this perspective? Naively it does seem to extend beyond statistical representation as it seems some semantic association is preserved through the mapping of language onto a vector space.
  - mjburgess 5y ago
    Well, it's essentially a coincidence mixed in with a bit of superstition. K - M has no semantics, its more like something you'd find in a teen magazine. "K - M" means as much, "Prince" or "Jesus Christ" or any of a number of words. The type of association underlying word vectors is just expecting to co-occur. So there are many cases we can enumerate where our expectation that Q occurs is modulated by M occurring. So if M hasn't occurred in some text, we expect Q rather than K. (Equally, in biblical literature, we might expect JC to occur; and in disney films, "prince"). And many of these famous examples are just tricks. Very nearly all of the industry sells this technology based on a few cases which happen to turn out "as a lay audience would expect", and neglect to include the many many cases where they do not. And to be clear, we should not expect "K - M" to be "Q" in anything other than a basically statistical sense, relative to some texts. "King - Man" isn't a semantic operation.

by TimPC 5y ago

mjburgess 5y ago

The association between "movie" and "tv" is "played on". The association between "jellybean" and "apple" is "smells similar". The association between "black" and "oppression" is "suffering". The association in each case is NOT likely to co-occur. AI is not detecting any kind of conceptual association. It is merely recording co-occurance. By interpreting co-occurance (statistical association) as meaningful, you are imparting a conceptual association to text it does not have. "Statistical association" is just a pun on "conceptual association". Machines do not detect associations; they record frequencies.

bitcurious 5y ago
> AI is not detecting any kind of conceptual association. It is merely recording co-occurance. Can you explain the famous example of “king - man + woman = queen” through this perspective? Naively it does seem to extend beyond statistical representation as it seems some semantic association is preserved through the mapping of language onto a vector space.
- mjburgess 5y ago
  Well, it's essentially a coincidence mixed in with a bit of superstition. K - M has no semantics, its more like something you'd find in a teen magazine. "K - M" means as much, "Prince" or "Jesus Christ" or any of a number of words. The type of association underlying word vectors is just expecting to co-occur. So there are many cases we can enumerate where our expectation that Q occurs is modulated by M occurring. So if M hasn't occurred in some text, we expect Q rather than K. (Equally, in biblical literature, we might expect JC to occur; and in disney films, "prince"). And many of these famous examples are just tricks. Very nearly all of the industry sells this technology based on a few cases which happen to turn out "as a lay audience would expect", and neglect to include the many many cases where they do not. And to be clear, we should not expect "K - M" to be "Q" in anything other than a basically statistical sense, relative to some texts. "King - Man" isn't a semantic operation.