>> Machine learning is about filling in the gaps based off of a prediction.
>I think this is a generous interpretation of network-based ML.
This is False.
The definition of What you actually do with machine learning is Literally filling in the gaps based on prediction. If you can't see this you may not intuitively understand what ML is in actuality doing.
Let's examine ML in it's most simplest form. Linear Regression based off of 2 data points with a single input X and single output Y:
(0, 0), (3, 3)
With linear regression this produces a model that's equivalent to : y = x
with y = x you've literally filled an entire domain of infinite possible inputs and outputs from -infinite to positive infinite. From two data points I can now output points like (1,1), (2,2),(343245,343245) literally from the model y=x.
The amount of data given by the model is so overwhelmingly huge that basically it's infinite. You feed in random data into the model at speeds of 5 billion numbers per nano second you will NEVER hit an original data point and you will always be creating novel data from the model.
And there's no law that says the linear regression line even has to TOUCH a data point.
ML is simply a more complex form of what I described above with thousands of values for input, thousands of values for output and thousands of datapoints and a different best fit curve (as opposed to a straight line, to fit into the data points). EVEN with thousands of datapoints you know an equation for a best fit curve basically covers a continuous space and thus holds almost an infinite amount of creative data compared with the amount of actual data points.
Make no mistake. All of ML is ALL about novel data output. It is literally pure creativity..... not memory at all. I'm baffled by all these people thinking that ML models are just memorizing and regurgitating.
The problem this paper is talking about is that the outputs are often illusory. Or to circle back to my comment the "predictions" are not accurate.
>I think this is a generous interpretation of network-based ML.
This is False.
The definition of What you actually do with machine learning is Literally filling in the gaps based on prediction. If you can't see this you may not intuitively understand what ML is in actuality doing.
Let's examine ML in it's most simplest form. Linear Regression based off of 2 data points with a single input X and single output Y:
With linear regression this produces a model that's equivalent to : y = xwith y = x you've literally filled an entire domain of infinite possible inputs and outputs from -infinite to positive infinite. From two data points I can now output points like (1,1), (2,2),(343245,343245) literally from the model y=x.
The amount of data given by the model is so overwhelmingly huge that basically it's infinite. You feed in random data into the model at speeds of 5 billion numbers per nano second you will NEVER hit an original data point and you will always be creating novel data from the model.
And there's no law that says the linear regression line even has to TOUCH a data point.
ML is simply a more complex form of what I described above with thousands of values for input, thousands of values for output and thousands of datapoints and a different best fit curve (as opposed to a straight line, to fit into the data points). EVEN with thousands of datapoints you know an equation for a best fit curve basically covers a continuous space and thus holds almost an infinite amount of creative data compared with the amount of actual data points.
Make no mistake. All of ML is ALL about novel data output. It is literally pure creativity..... not memory at all. I'm baffled by all these people thinking that ML models are just memorizing and regurgitating.
The problem this paper is talking about is that the outputs are often illusory. Or to circle back to my comment the "predictions" are not accurate.