| HN Mirror

Isn't deep learning the correct approach though?

> No. It's a piece of a much larger puzzle and only a partial piece at that. An overfit piece that people are over-applying. This is why things are overly complex and filled with statistics...

I mean we are trying to emulate biological neural networks which is fundamental to intelligence.

> There is far more functional complexity to the underlying biology. This is why studying neurobiology/neuroscience have value as opposed to resorting to ever more complex statistical models that no one understands.

Do you think that the artificial neural networks we are using right now are not rigorous enough?

> Of course not. Out of all the amazing people centered on it, no one can say why/how it works. Is it magic? lol... That should tell you something and trigger a red flag. I can state why it works and already have. It's just not something that's convenient and would necessarily cause one to admit that its not the broad general answer were looking for....

So, for some time, those centered on this paradigm are probably going to build out wildly elaborate NNs. They will require boat loads of data and computational power and achieve great outputs. Coincidentally this fits nicely in the cloud computing model that the big tech titans maintain.

Somewhere down the line, more solid and thought through computational models inspired by actual understanding will come out that will shake the very foundation of said approaches and so will go another page in tech history.

NNs are biological inspired. With all of the fanfare surrounding them, You maybe never stopped to question how inspired.

Drop Paul King/Paul Bush a line a Quora or dig through some of their posts.

It's better to talk w/ a Neuroscientist/Computational Neuroscientist about this stuff IMO.