Nowadays, few people doubt the usefulness of artificial intelligence in preparing various written works: from generating ideas to finding more interesting topics. It has also become a tool for performing the most basic editorial functions, such as checking grammatical, spelling, or punctuation errors in texts. It seems that AI will inevitably take over a significant portion of these tasks from humans in the future. However, debates continue about whether this technology could handle more complex tasks and replace literary critics, especially given the vast size of AI databases, which are expected to grow even larger.
AI’s Potential to Replace Literary Critics
A literary critic is not just any reader. These are individuals with specific knowledge and abilities to apply literary theories, models, and other concepts to academically assess the quality, originality, and societal impact of a literary work.
To critically evaluate a work, textual analysis is essential. Some of the key elements involve understanding the writer’s style, tone, and the themes they explore from various perspectives, as well as grasping the subtleties of the plot and the characteristics of its characters. All of this contributes to a quality assessment of the work.
The question arises: how capable is AI in this area? Essentially, this technology has vast databases and the potential to be programmed to include all the necessary aspects in its analysis.
When properly trained, machine learning can be invaluable, as it has the ability to process large amounts of information quickly, saving time. It can also recognize certain literary models or methods and detect their recurrence across different texts.
Moreover, this partially reduces the issue of objectivity, as a literary work can be evaluated impartially. Additionally, natural language processing capabilities allow it to analyse texts beyond the surface, as it can understand context and offer insights that may go unnoticed by human readers.
Challenges of AI as a Literary Critic
Despite AI’s potential in this field, several challenges arise. First and foremost, artificial intelligence is essentially programmed to operate according to specific rules. However, literary criticism and analysis are fundamentally interpretive. Therefore, there is a risk that system might approach a text from a particular angle, potentially missing other important details.
Complex literary works often involve a lot of contextual material, such as history, specific to a location or time period, or information linked to particular knowledge, which system might not recognize unless it is explicitly provided.
These aspects become the primary challenges for neural network in performing literary analysis, as it risks becoming limited and incomplete.
Final Word
At least for now, it seems that after considering all the possibilities and risks, the best solution is the collaborative partnership between humans and AI, where machine learning is not left to perform literary analysis independently, and human critics contribute their insights and observations.
As machine learning continues to advance, there is a growing possibility and threat to current literary critics that machine tools might perform their work faster and with the same level of quality in the future. Sources: Medium, Kharis Publishing, Powerdrill
