Abstraction is defined as the process of making something easier to understand by ignoring the details that may be unimportant. Abstraction makes our day to day to day tasks doable/possible. For example, something as simple as looking what time your watch shows would have become humongous task, if you had to take in all the details of the watch like- length & width of needles, intricate internal mechanism with small and large gears interlocking through their tooth etc.- to name a few. But you ignore all this (i.e. unnecessary details) & just look at the time.
Similarly our neural networks, use abstraction to solve complex problems. Let us take simple route finding problem to drive home the point.
Let us assume that you decided to visit a city in Japan. Your mission is to reach the district headquarters as soon as you left the airport. The person who gave you the mission brief, say Mr M, forgot an important detail that was conveyed to him by a local of the city X, say Mr T. Mr T had told Mr M that one only had to follow the road signs, which not only told which turn you have to take to reach the city center but also how far you’re from it. Mr M realized his mistake and told you that, though he didn’t remember the exact advice of Mr T, but he can provide you with a dataset through which you could figure out the route to the city center. Being a proactive employee and AI engineer you readily accepted the challenge of building a model that can accurately predict the route to city center. But there is one problem, the dataset is in a language which neither you nor google translate understands. You have no clue, which column denotes what and categorical volume are an absolute nightmare.
Further the dataset has umpteen features, ranging from colour of houses on the way, traffic info, weather info, street vendors on the way, trees planted along the road, specie & age of those trees etc. Hence you don’t know what each column denotes, on top of that most columns are useless.
If you use linear regression to build your model then it would take ginormous amount of effort & ages in feature selection, tuning the model, validation etc., before you arrive at the accurate prediction. If the data of road signs was in form of images, then it would be practically impossible to achieve the desired result.
On the other hand if you chose to build a neural network, with nonlinear activation functions, then you’ll be able to make accurate predictions in a reasonable amount of time and with much more ease.
As you would have guessed by now, that we create neural networks to solve those problems with a level of accuracy that other algorithms simply can’t deliver. Even if they could, they would require excessive amount of time & effort. Thus having a linear activation function simply defeats the purpose of a neural network.