The challenges in teaching machines to see like humans

The development phase requires proper planning and consideration. For example, a young child can recognise an apple by someone pointing and naming the fruit a few times. From there, he would be able to recognise all sorts of apples.

But in machine learning, it takes thousands or millions of data and algorithms for the machine to identify it as an apple.

How much data is enough. 100, 1000, 10000?

Underfitting is when the model is too simplified to know the base structure of data. It has too little information to construct an accurate training model.

Overfitting is where researchers overgeneralise and hope the machine can produce insight by the end of the training.

When dealing with large datasets with many columns and variables, feature extracting is used to divide and reduce existing data into a manageable group.

But for image processing, machines can't extract features such as edges, shapes, or even size in this way. Instead, machines see and store an image in the form of a matrix of numbers. It gets more complicated when dealing with large images and datasets and eventually leads to the Curse of Dimensionality. Instead of processing each matrix, the machine will discard some features to more accurately map in a lower-dimensional space.