You don’t get Nobel prizes for going to math class. What you do get is a basic understanding of math, which is more than sufficient to correct you on this. This was covered in Algebra, man. Review your notes.
They grow proportionately to ax^n . Correspondingly, for values of x < 1, they look very similar to a simple linear slope. For values of x > 1, they grow very rapidly. Both portions are part of the function, it doesn’t suddenly “become” exponential at the rapid increase, it’s exponential the whole time.
What metric are you using? Data can’t really be fit to a curve without data to plot.
The entire contention is you misunderstanding how exponential functions work., i.e. “if it’s exponential, shouldn’t we be rapidly accelerating by now?” Betrays a fundamental misunderstanding.
People don’t expect AI to be exponential because of existing data. It’s because once AI starts significantly improving itself, the advancement of AI, x, starts to apply to itself x^2 .
We won’t know if it is, in fact, exponential until after the “knee” of the curve. But a slow advancement now does not preclude rapid acceleration in the near future. You’ve repeatedly demonstrated throughout the thread that you don’t understand this.
Without the “knee” of the curve there is no exponential growth.
What best describes this curve
Edit: Maybe I have it wrong. From now on I will only model data as exponential functions because they are potentially exponential and the data set is just incomplete.
Ooh you took a math class. When are they delivering your Nobel prize?
You don’t get Nobel prizes for going to math class. What you do get is a basic understanding of math, which is more than sufficient to correct you on this. This was covered in Algebra, man. Review your notes.
What was covered? That functions are used to describe data sets?
In algebra? The basic properties of exponential functions, for one.
Let’s start there then. What are the basic properties of exponential functions?
They grow proportionately to ax^n . Correspondingly, for values of x < 1, they look very similar to a simple linear slope. For values of x > 1, they grow very rapidly. Both portions are part of the function, it doesn’t suddenly “become” exponential at the rapid increase, it’s exponential the whole time.
Well there it is
What type of growth would you use to describe the advancement of AI?
What metric are you using? Data can’t really be fit to a curve without data to plot.
The entire contention is you misunderstanding how exponential functions work., i.e. “if it’s exponential, shouldn’t we be rapidly accelerating by now?” Betrays a fundamental misunderstanding.
People don’t expect AI to be exponential because of existing data. It’s because once AI starts significantly improving itself, the advancement of AI, x, starts to apply to itself x^2 .
We won’t know if it is, in fact, exponential until after the “knee” of the curve. But a slow advancement now does not preclude rapid acceleration in the near future. You’ve repeatedly demonstrated throughout the thread that you don’t understand this.
Without the “knee” of the curve there is no exponential growth.
What best describes this curve
Edit: Maybe I have it wrong. From now on I will only model data as exponential functions because they are potentially exponential and the data set is just incomplete.