Given an n-character word, we want to predict which character would be the n+1th character in the sequence. For example, our input is “predictio” (which is a 9 character word) and we have to predict what would be the 10th character.
Which of the following neural network architectures would be best suited to complete this task?

Question

BlackTom AI · Accepted Answer

Question restatement: Given an n-character word, we want to predict the n+1th character in the sequence. For example, input: “predictio” (9 characters) and predict the 10th character. Answer options are: Fully-Connected Network; Recurrent Neural Network; None of the options mentioned here; Convolutional Neural Network.

Option 1: Fully-Connected Network. These networks treat inputs as a fixed-size vector and have no inherent mechanism to handle sequence order or temporal dependencies. To apply them to sequence forecasting, you’d either unravel the sequence into a fixed-length vector or process each step independently, losing useful memory of prior characters unless you add an external mechanism. This makes them inefficient for capturing long-range dependencies and character-by-character sequence dynamics.

Option 2: Recurrent Neural Network. RNNs maintain a hidden state that propagates through the sequence, allowing the model to remember information from previous characters as it processes the next one. This design directly aligns with the task of predicting the next character in a string, since the next character often depends on the preceding context. RNNs (and their gated variants like LSTMs/GRUs) are specifically engineered for sequential data and next-step prediction.

Option 3: None of the options mentioned here. This would be the choice if none of the listed architectures could handle the task, but that isn’t accurate given that at least one architecture (RNN) is well-suited for sequence prediction.

Option 4: Convolutional Neural Network. CNNs, especially 1D CNNs, can process sequences and capture local patterns, but they typically require fixed receptive fields and lack explicit sequential memory across arbitrary lengths unless stacked deeply or combined with special mechanisms. They are generally less natural for predicting the immediate next character in a long string compared to recurrent models, though they can be used for character-level language modeling with certain architectures. In terms of best fit for the straightforward next-character prediction task described, CNNs are not as well-matched as RNNs.

Summary: Among the given options, the architecture that best suits predicting the next character in a sequence is the Recurrent Neural Network, because it is designed to handle sequential data with temporal dependencies and retain information across previous inputs to inform the next prediction. The Fully-Connected Network lacks memory, the None of the options here is incorrect since a suitable option exists, and while CNNs can model some sequence aspects, they do not align as naturally with the step-by-step next-character prediction as RNNs do. The reasoning emphasizes why memory-enabled sequence processing (RNN) is the most appropriate choice for this task.

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