This can already be done but it doesnt work how you want it to.

Data is first gathered, whether it be images or text or even both (multimodal). This data is then transformed into numbers, more specifically number matrixes. In PyTorch these are called TENSORS.

Tensors of data is fed into the neural network in batches, or small parts of the dataset at a time. After each batch of data the neural network's weights and biases are adjusted using an optimization algorithm. One pass of all the data into the neural network is called an EPOCH.

The neural network is then tested for accuracy using a loss function.

The neural network can be trained multiple epochs (or times) on the same dataset to increase accuracy (minimize the loss function)

The closer the loss function is to zero the more accurate the NN model

Depending on the amount of parameters, hidden layers, epochs, and data this can require a lot of computational power and of course electricity.

The amount of electricity and cooling that gpu, tpu, or cpu (whatever the company is using) racks require is why this costs millions of dollars.

In order for a neural network to learn it has to be retrained with more data server-side not client-side.