[arjo1]

Perhaps a good layman type explanation would be that nueral networks are essentially curve fitting on steroids. (Hopefully at some point people have done curve fitting in school and remember drawing lines of best fit). Therefore the term AI is essentially a misnomer. I would even go as far as to emphasize that nueral networks are boring mathematical equations which do not actually mimic the inner workings of our brains.

[redcalx]

> essentially curve fitting on steroids

Biology is essentially simple chemical reactions on steroids. I.e. you have assumed there is a qualitative distinction between biological brains and artificial neural nets that cannot be overcome by scaling up. However (A) AI models are many, varied and new variants are being explored all the time, and (B) there are systems where new new dynamics appear at larger scales, thus producing a qualitatively different system based on the same underlying rules, e.g. physics -> chemistry -> biology -> human brains -> social networks.

[schoen]

Some recent HN discussion about neural networks vs. neurons:

https://news.ycombinator.com/item?id=14790673

[shiftpgdn]

I have a bachelor's degree and have no idea what curve fitting is.

[schoen]

You might have seen it called "regression" (also "interpolation" and "extrapolation"), but not everyone has necessarily been exposed to this.

[TeMPOraL]

Related, I used to jokingly explain mean-square-error approximation like this: there is this geometrical theorem, that you can drive a line through any three points on a plane, as long as the line is thick enough. So mean-square-error approximation is basically minimizing the thickness of that line :).

[backpropaganda]

New favorite alternative term for deep learning: "nonlinear interpolation"

[Scea91]

How do you know that our brains are not 'just curve fitting on steroids' too?

[red75prime]

> do not actually mimic the inner workings of our brains.

Maybe they actually do. http://www.nature.com/articles/srep27755