We store all the facts about dolphins in a database(like has lungs, has fins, has sonar), but not labels(is mammal, is fish, etc..)
So if I follow: To record a fact like “has lungs” you first have to define “lungs”. And then you run into the same problem: if you’re not recording labels, then you have to identify lung objects from non-lung objects by specifying descriptors (has cell structure A, processes oxygen, etc.), and then you have to define those descriptors, and pretty soon your query for dolphins (or chairs, or oranges, or Libertarians) is a huge-ass quantum probability distribution which is a pain to deal with.
To avoid having to write that huge query, you allow the user to specify conditionals in terms of other conditionals which were defined in advance. That gets you the same query in the end, but in a way that’s a lot easier for the user.
That sounds fine to me; in fact, it sounds like reductionism, which is very handy stuff indeed. However, it doesn’t address the issue in the OP, which is that human concepts tend to act like fuzzy values, not like strictly delineated sets. Let’s take a naive query high-level bird query: feathered vertebrate, flies, reproduces by laying eggs. That describes bird characteristics which are very useful, and which can’t really be discarded; however, it excludes things that we commonly consider to be birds, such as parrots that have had their wings clipped, and penguins.
Human brains can (and often do) apply labels to objects strongly or weakly. Your query language has to be similarly heuristical if you want it to be useful for all or even most of the questions humans tend to ask.
Yes i think you understood what i meant. It is a recursive system where you keep defining each thing in detail, hacking at the edges of reality until any hypotheses left are all equally valid.
It is hard work, and it is possibly too much for the brain to handle, but afaik, other than the handful of Direct Instruction studies nobody has done any really big tests. the tests done on the small scale where highly successful though.
I obviously program this stuff in a specially designed tool, which makes it intuitive and easy to keep defining the definitions deeper and deeper (and you basically end up with laws of nature at the bottom, like the math explaining gravity etc..)
I guess what i am trying to say is, that the foggyness of concepts in our head can be a result of our teaching methods and not a flaw of the mind per-se, my only evidence being the fact that we can make tools that help us clear up the fog, and that using these tools/methods to teach people seems to have a big effect.
But, the fuzziness isn’t necessarily a flaw at all; having more and less typical examples of a category has shown itself to be pretty handy, since we can use the level-of-typicalness to influence how confidently we can make correlations (“birds lay eggs and have feathers and fly, X has feathers but doesn’t fly, so I’m only pretty sure it lays eggs”).
I think that would be a valuable feature in a fact database.
So if I follow: To record a fact like “has lungs” you first have to define “lungs”. And then you run into the same problem: if you’re not recording labels, then you have to identify lung objects from non-lung objects by specifying descriptors (has cell structure A, processes oxygen, etc.), and then you have to define those descriptors, and pretty soon your query for dolphins (or chairs, or oranges, or Libertarians) is a huge-ass quantum probability distribution which is a pain to deal with.
To avoid having to write that huge query, you allow the user to specify conditionals in terms of other conditionals which were defined in advance. That gets you the same query in the end, but in a way that’s a lot easier for the user.
That sounds fine to me; in fact, it sounds like reductionism, which is very handy stuff indeed. However, it doesn’t address the issue in the OP, which is that human concepts tend to act like fuzzy values, not like strictly delineated sets. Let’s take a naive query high-level bird query: feathered vertebrate, flies, reproduces by laying eggs. That describes bird characteristics which are very useful, and which can’t really be discarded; however, it excludes things that we commonly consider to be birds, such as parrots that have had their wings clipped, and penguins.
Human brains can (and often do) apply labels to objects strongly or weakly. Your query language has to be similarly heuristical if you want it to be useful for all or even most of the questions humans tend to ask.
Yes i think you understood what i meant. It is a recursive system where you keep defining each thing in detail, hacking at the edges of reality until any hypotheses left are all equally valid.
It is hard work, and it is possibly too much for the brain to handle, but afaik, other than the handful of Direct Instruction studies nobody has done any really big tests. the tests done on the small scale where highly successful though.
I obviously program this stuff in a specially designed tool, which makes it intuitive and easy to keep defining the definitions deeper and deeper (and you basically end up with laws of nature at the bottom, like the math explaining gravity etc..)
I guess what i am trying to say is, that the foggyness of concepts in our head can be a result of our teaching methods and not a flaw of the mind per-se, my only evidence being the fact that we can make tools that help us clear up the fog, and that using these tools/methods to teach people seems to have a big effect.
But, the fuzziness isn’t necessarily a flaw at all; having more and less typical examples of a category has shown itself to be pretty handy, since we can use the level-of-typicalness to influence how confidently we can make correlations (“birds lay eggs and have feathers and fly, X has feathers but doesn’t fly, so I’m only pretty sure it lays eggs”).
I think that would be a valuable feature in a fact database.