While reading this i got the idea that this article is attacking the current standards for “how to order things in nature”
I have two things to say in response:
Direct Instruction and i guess the scientific method in general both claim/prove that you can cut reality at the exact joints required to make only those hypotheses that explain the thing available. (So we can come up with unfalsifiable set of data on what a “red” is).
Only real data about a thing should be stored, flat things that say something concrete about the thing. Categorizing the thing in a scale with other similar things, meta infomation, should be done for each unique question posed.
For example: 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..). You can then query the database for things you want, for example, all things with fins, and then dolphins will fit the meta label, do the same thing for all things with gills and the dolphin will not be in the result.
In short: Labels are situational and should be clearly defined as to what kind of characteristic they scan for and what they’re use is.
As a knowledge designer. I would store all the simple facts in a database, and then use a conditional script to select the best label for any given query at that moment. This means i throw away the current concept of “fish” whatever it is, and make it concrete by asking: “What specific characteristics are you interested in for this particular query?” (We can decide on common queries that we want to make international standards, like we have now for fish, but we need to make clear in what situations that standard even means anything)
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.
While reading this i got the idea that this article is attacking the current standards for “how to order things in nature”
I have two things to say in response:
Direct Instruction and i guess the scientific method in general both claim/prove that you can cut reality at the exact joints required to make only those hypotheses that explain the thing available. (So we can come up with unfalsifiable set of data on what a “red” is).
Only real data about a thing should be stored, flat things that say something concrete about the thing. Categorizing the thing in a scale with other similar things, meta infomation, should be done for each unique question posed. For example: 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..). You can then query the database for things you want, for example, all things with fins, and then dolphins will fit the meta label, do the same thing for all things with gills and the dolphin will not be in the result.
In short: Labels are situational and should be clearly defined as to what kind of characteristic they scan for and what they’re use is.
As a knowledge designer. I would store all the simple facts in a database, and then use a conditional script to select the best label for any given query at that moment. This means i throw away the current concept of “fish” whatever it is, and make it concrete by asking: “What specific characteristics are you interested in for this particular query?” (We can decide on common queries that we want to make international standards, like we have now for fish, but we need to make clear in what situations that standard even means anything)
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.