[rorykoehler]

> Cynically, neural networks are easier as you don't really have to think about your model. Give some examples with some classes and you're done. Or give examples of one class and let the neural net generate new ones. Doing away with the abstraction beforehand is an enticing prospect.

If you're trying to solve a well understood business problem sure but my issue with this is that you pigeonhole yourself and your solution. I'm much more interested in understanding the model than doing the implementation because that allows you to build on top of what you get out of the box in a framework for example. It's like learning React before learning Javascript. It might be a good short term solution but long term it certainly isn't.

[maweki]

Oh, I was not defending neural networks. This was the cynical sales pitch for the case where you don't want to employ mathematicians or computer scientists, but just throw code and computational resources at the problem.

[lopmotr]

But isn't that an important part of the value of neural networks? Mathematicians are expensive so we'd like a computer to make a model for us, just like drivers are expensive so we want self-driving cars.

[Retric]

The issue with that is NN fail in some really interesting ways so you still need a lot of effort to get a robust solution. Remember, after some serious investments by many organizations self driving cars are still in development. At the same time a few people have demonstrated a basic system that seems close without nearly that much investment. Unfortunately, the difference between a demo and working solution can be several orders of magnitude.

[westwing]

> It might be a good short term solution but long term it certainly isn't.

It is only a temporary solution - unless it works.

https://www.youtube.com/watch?v=pY7nx5Z6Kzo