Not as straightforward as presented. The previous commenter summed it up nicely: playing against a chess master familiar with theory containing decades or centuries of precedent is its own "superaging" compared to playing against a novice and learning from that respective input. This kind of thinking can thus be misleading.
To synthesize a lifetime of experience in a masterful way--to deal with the fundamentally random and even metaphysical fickles of life--is wisdom; it is a characteristic of an ensouled creature.
Probabilistic thinking does not suffice.
In this manner, the AI is an efficiency tool that can synthesize what may be gleaned empirically. But it should not be taken as any more than this.
Regardless, I think the idea is amusing and fascinating.
ty for reading. you're articulating some truths, but you're wildly underestimating the neural network when compared to the neural: a way of learning and thinking quite literally in part designed and modeled after our own brains.
intelligence, which is to say pattern recognition, which is fundamentally what all intelligence is (pattern matching), is not a human-specific feature. ensoulment, yes. but the sum total of all human patterns with perfect recall is a form of ancient intelligence. in many ways, AI is an ancient technology, and does have wisdom to provide, understood this way. he's.... seen some things.
data aging is when you are exposed to patterns, information, that would previously take days or weeks to learn or see, in minutes.
though there is something to be said for the beauty and "ensouled" learning of a journey, which I do give credit to. intelligence is not necessarily wisdom, I agree. and a journey can lend itself to wisdom.
The problem is that "data years" are non-linear. We didn't have the germ theory of disease until the late 1800s. So do students get 35,000+ years of microbiology knowledge the moment we teach germ theory of disease? But only 35,200+ years if we teach about innate lymphoid cells?
There's also data quality and experience. 80% of all papers published right now are garbage. I doubt AI can tell the difference.
For experience, stick an elite high school student in the lab, and they still have a learning curve. It will be faster than the others, but it will lag the book knowledge.
Not as straightforward as presented. The previous commenter summed it up nicely: playing against a chess master familiar with theory containing decades or centuries of precedent is its own "superaging" compared to playing against a novice and learning from that respective input. This kind of thinking can thus be misleading.
To synthesize a lifetime of experience in a masterful way--to deal with the fundamentally random and even metaphysical fickles of life--is wisdom; it is a characteristic of an ensouled creature.
Probabilistic thinking does not suffice.
In this manner, the AI is an efficiency tool that can synthesize what may be gleaned empirically. But it should not be taken as any more than this.
Regardless, I think the idea is amusing and fascinating.
ty for reading. you're articulating some truths, but you're wildly underestimating the neural network when compared to the neural: a way of learning and thinking quite literally in part designed and modeled after our own brains.
intelligence, which is to say pattern recognition, which is fundamentally what all intelligence is (pattern matching), is not a human-specific feature. ensoulment, yes. but the sum total of all human patterns with perfect recall is a form of ancient intelligence. in many ways, AI is an ancient technology, and does have wisdom to provide, understood this way. he's.... seen some things.
data aging is when you are exposed to patterns, information, that would previously take days or weeks to learn or see, in minutes.
though there is something to be said for the beauty and "ensouled" learning of a journey, which I do give credit to. intelligence is not necessarily wisdom, I agree. and a journey can lend itself to wisdom.
The problem is that "data years" are non-linear. We didn't have the germ theory of disease until the late 1800s. So do students get 35,000+ years of microbiology knowledge the moment we teach germ theory of disease? But only 35,200+ years if we teach about innate lymphoid cells?
There's also data quality and experience. 80% of all papers published right now are garbage. I doubt AI can tell the difference.
For experience, stick an elite high school student in the lab, and they still have a learning curve. It will be faster than the others, but it will lag the book knowledge.
Why would you stick an élite high-school student in a lab? These people have massive potential; surely they are capable of more.
Am I missing something?
Summer internship.
Plus those who wish to pursue a career in the biomedical sciences will spend a lot of their undergraduate, and graduate time in the lab.