My humble opinion; AI is supposed to be the acronym for artificial intelligence, but marketing has usurped it to refer to machine learning, which is nothing more than a neo-language for defining statistical equations in a semi-automated way. An attempt to dispense with mathematicians to develop models.
What amount of energy is necessary for an event to be reflected in a statistic? You have a box of 2x2 meters with balls of data, and a string with a diameter of 1 meter with which to surround the highest concentration of balls possible, and those that remain outside, there they stay. Statistics and lack of precision are concepts that go hand in hand (someones say even it is not an science).
> My humble opinion; AI is supposed to be the acronym for artificial intelligence, but marketing has usurped it to refer to machine learning, which is nothing more than a neo-language for defining statistical equations in a semi-automated way.
Sure. Hardly controversial.
> An attempt to dispense with mathematicians to develop models.
What...? No. Definitely not.
> What amount of energy is necessary for an event to be reflected in a statistic? You have a box of 2x2 meters with balls of data, and a string with a diameter of 1 meter with which to surround the highest concentration of balls possible, and those that remain outside, there they stay. Statistics and lack of precision are concepts that go hand in hand (someones say even it is not an science).
I have no idea what this is saying. It sounds like you're shitting on statistics all of a sudden, which is weird, given that you seemed to favor mathematicians in the first part.
>I have no idea what this is saying. It sounds like you're shitting on statistics all of a sudden, which is weird, given that you seemed to favor mathematicians in the first part.
Mathematicians are specialized in problem solving, and as humans, their ability to predict and analyze data makes them more reliable developing models than a statistical equation. They have quite more tools than statistics one.
Someway, it is like if using the acronym AI to define statistical algorithms leads to a false sense of greater reliability than such human review, or even that it is not needed a deep human review. ML statistics takes algorithms out of the oven long before mathematicians does, at the expense of a big in accuracy difference.
The problem I think is people may take important decisions based in the result of such statistical algorithms without questioning
Having built models, I’d claim that it’s art based upon science, perhaps not too different than engineering a building. At every stage there are decisions to be made with tradeoffs. Over time, the resulting model could be invalidated or perhaps perform better. It’s remarkably difficult to approach or even define a “best” model.
What’s most peculiar to me is that somehow AI is becoming more distinct from math or stats and that there’s a notion by running pytorch one is able to play god and create sentience.
>Statistical thermodynamics contains all the necessary tools you need to answer your balls in a box question
The balls in a box example shows how ML statistics work. The string is adjustable, it can be adapted to different contours, but you have to discard data.
How do you compensate for the inclusion of data in the model without discarding others? The string has a limit in diameter by design, and you need to know the content of most of the data to make good decisions.
It's not that AI has been conflated with machine learning—those are words that are supposed to refer to the same thing. The confusion is conflating either with slapdash applied statistics.
What amount of energy is necessary for an event to be reflected in a statistic? You have a box of 2x2 meters with balls of data, and a string with a diameter of 1 meter with which to surround the highest concentration of balls possible, and those that remain outside, there they stay. Statistics and lack of precision are concepts that go hand in hand (someones say even it is not an science).