Why is image classification tasks are dominated by minimizing cost function instead of maximizing ones? I was watching a video of policy gradient by Andrej Karpathy at 10:00 there shows an equation for supervised learning for image classification.

$max\sum _{i}log \:p(y_i|x_i)$

I have worked with image classification models before but I always used a minimizing cost function aka loss function. I also never seen some one using maximizing cost function for image classification in the wild. 


Why image classification tasks are dominated by loss functions? 
What is the advantages of a minimizing loss function  over a maximizing
loss function in image classification?
Other than RL where else maximizing cost functions are used?
There is really no difference between minimizing a cost function and maximizing a value function.  One can be the reciprocal of the other, or the negative of the other, for example.