Shortcomings of ANNs

Artificial Neural Networks (ANNs), including powerful architectures like Convolutional Neural Networks (CNNs) which excel at tasks like image recognition, operate fundamentally on fixed, independent inputs. While revolutionary for their time, applying these networks directly to sequential problems reveals significant limitations. Trying to force sequential data into the mold of a standard ANN is like trying to read a book by looking at all the words scrambled on a single page – you lose the narrative, the flow, and the crucial dependencies between elements.

Let's look at the specific reasons why standard ANNs struggle with sequential data:

• Fixed Sizes: making them unsuitable for variable-length sequences without complex workarounds like padding or truncation;

• No Parameter Sharing Across Time: forcing the network to learn the same feature independently at each possible time step;

• Inefficient for Long Sequences: processing long sequences with ANNs can be computationally expensive and memory-intensive; as the number of parameters grows linearly with sequence length.

These fundamental architectural constraints mean that standard ANNs lack the ability to effectively capture temporal dependencies or maintain memory of past information within a sequence. They treat each input instance largely in isolation, which is a critical disadvantage when the output depends not just on the current input, but on the entire history of inputs. Overcoming these limitations requires a neural network design that is inherently built to handle sequences, remembering previous data points and applying learning across time steps. This is precisely the gap that Recurrent Neural Networks (RNNs) were designed to fill.