Machines don't learn part II: maladaptive logical and statistical application
Why do machines "learn" in maladaptive ways?
The root cause of the issue is the mishandling of the Problem of Universals, along with the resultant shoehorning of logical and statistical solutions.
Logic cuts and divides everything in the world into distinct parts, down to supposed universal properties across different objects.
Let's see how this creates issues, starting with The Raven Paradox.
You see a bunch of apples that aren't black, and that somehow supposed to contribute to a perception that all ravens are black? To some that may seem to be a crock of nonsense because it starkly runs against intuition (what the heck does an apple's color have to do with those of ravens?), yet that's how learning is supposed to work according to Bayesian treatment of the issue. It's an equation, so that's how it's supposed to work... It's an obvious case of shoehorning. The solution doesn't sound sensical, and if theory runs against common sense then "so be it, because we as people must also work that way because there's no better solution"? Really? Sounds like a fallacy of paucity of imagination.
No, I think not. Forcing "off-topic" considerations in learning is simply forcing a bad solution onto the problem. The Bayesian statistical solution to the Problem of Universals is wrong.
What is the mistreatment here? It's the assumption that properties exist as universal objective properties. There are multiple things wrong with that assumption:
A solution to Zeno's motion paradoxes is thus:
Okay, now that we've talked about how machines don't learn, one may wonder how learning actually occur.
It's a psychological process, with mental impressions built over time involving both firsthand experiences and secondhand descriptions:
https://philosophy.livejournal.com/1382079.html
The root cause of the issue is the mishandling of the Problem of Universals, along with the resultant shoehorning of logical and statistical solutions.
Logic cuts and divides everything in the world into distinct parts, down to supposed universal properties across different objects.
Let's see how this creates issues, starting with The Raven Paradox.
You see a bunch of apples that aren't black, and that somehow supposed to contribute to a perception that all ravens are black? To some that may seem to be a crock of nonsense because it starkly runs against intuition (what the heck does an apple's color have to do with those of ravens?), yet that's how learning is supposed to work according to Bayesian treatment of the issue. It's an equation, so that's how it's supposed to work... It's an obvious case of shoehorning. The solution doesn't sound sensical, and if theory runs against common sense then "so be it, because we as people must also work that way because there's no better solution"? Really? Sounds like a fallacy of paucity of imagination.
No, I think not. Forcing "off-topic" considerations in learning is simply forcing a bad solution onto the problem. The Bayesian statistical solution to the Problem of Universals is wrong.
What is the mistreatment here? It's the assumption that properties exist as universal objective properties. There are multiple things wrong with that assumption:
- "Color" is subjective. It doesn't exist "out there in the world" but entirely in your head https://www.extremetech.com/extreme/49028-color-is-subjective and as such it's a description of the subject and not the object
- How do we assure ourselves that a property attached to one kind of object can be identical to a property attached to another kind of object at all? Isn't that another assumption subject to debate? Let's say for the sake of argument that there is a black apple. How do we come to know that the "black" of black apples can be identical to that of the "black" of black ravens? One property supposedly arise from the skin of a fruit while the other from a collection of barbs of feathers. Isn't this a comparison between the colors "apple black" and "raven black" instead? How is that any different from comparing, say, apples with oranges?
A solution to Zeno's motion paradoxes is thus:
- There is persistence of memory of object "A" from perceived static position A1 until the next perceived static position A2, all the while the position of dynamic object "Ã" is unidentifiable
- There is perceived movement of A from A1 to A2
- Perception and conception of A1, A2... is quantized, progression of à is still continuous
- Thus, wherever the object is positioned logically (i.e. identified), it is already not at the logical position
Okay, now that we've talked about how machines don't learn, one may wonder how learning actually occur.
It's a psychological process, with mental impressions built over time involving both firsthand experiences and secondhand descriptions:
https://philosophy.livejournal.com/1382079.html