Fundamentally that’s a purely ethical decision about whether a machine doing something should be treated with the same understanding the same as a human would. But we don’t consider remembering something with a neurochemical storage the samd as taking a picture of something even if they’re basically the same.
That’s splitting hairs on definitions, with no change in meaning. It’s still not copying the data, and is far closer to a person learning than to a picture taken by a camera.
It is actually not like a person learning at all. The only way you could believe this is if you have no grasp of the mathematics that are the basis of the multi-dimensional statistical analysis which is neural network training, and haven’t bothered to do any reading on it.
There’s a reason I referenced curve fitting.
Here is a better explanation than I could give, by someone who knows better than me:
It actually is, but you want it to be literally the same. Given the options of what it can be compared to, learning is the most appropriate, which is exactly why it’s named Machine Learning. The only reason you could believe otherwise is if you have no concept of language, and believe that it must be literally identical to a person learning in order to use the same verbiage.
It does not; which is the reason why it’s called learning. It’s the closest, and most accurate approximation, regardless of the mathematical operations on which it’s based.
Fundamentally that’s a purely ethical decision about whether a machine doing something should be treated with the same understanding the same as a human would. But we don’t consider remembering something with a neurochemical storage the samd as taking a picture of something even if they’re basically the same.
That’s because taking a picture is an idetic copy. Machine learning is NOT copying, it’s learning - hence the name.
This is a misunderstanding based on confusion between technical and colloquial terminology.
A machine learning model “learns” information in the same way that a curve fitting algorithm “learns” the shape of a data set.
This is not the same as the colloquial meaning of human learning. It is a mathematical process.
That’s splitting hairs on definitions, with no change in meaning. It’s still not copying the data, and is far closer to a person learning than to a picture taken by a camera.
It is actually not like a person learning at all. The only way you could believe this is if you have no grasp of the mathematics that are the basis of the multi-dimensional statistical analysis which is neural network training, and haven’t bothered to do any reading on it.
There’s a reason I referenced curve fitting.
Here is a better explanation than I could give, by someone who knows better than me:
Large Language Models explained briefly by 3Blue1Brown
It actually is, but you want it to be literally the same. Given the options of what it can be compared to, learning is the most appropriate, which is exactly why it’s named Machine Learning. The only reason you could believe otherwise is if you have no concept of language, and believe that it must be literally identical to a person learning in order to use the same verbiage.
It does not; which is the reason why it’s called learning. It’s the closest, and most accurate approximation, regardless of the mathematical operations on which it’s based.