Artificial intelligence in literature
Computers that write literature, algorithms that help us in everyday life, even whole robotic people that can communicate with us and make everyday life easier – with the term “artificial intelligence”. we combine a lot of weird and sometimes utopian ideas and myths. Which of these is actually realistic and how some aspects can also advance us in literary studies.
Three points of contact between AI research and literary studies:
1. Approaches from literary studies as a conceptual basis for implementations of artificial intelligence
2. Approaches from AI research and implementations of AI to support Literary studies
3. use of AI by digital humanities, or scholars to imitate the writing style and to create literature
Understanding Artificial Intelligence in Literature
Literary studies concepts as the basis of artificial intelligence
A literary research direction that is particularly interesting for the development of artificial intelligence is those on “Interactive Storytelling. Another approach, which has often been used as the basis for AIs, comes from the traditional narrative theory line.
Artificial intelligence as a tool for literature analysis
Many different tools are currently used in literary studies, some of which machine learning use. Machine learning is often a variant for a method that is also carried out differently, but which is performed by machine learning can be greatly improved. Learning works best when the computer combines very, very many properties of the category to be recognized in a complex way. If this is the case, one speaks of deep learning.
The results of machine learning tools could be improved many times over in the last few years by using deep learning techniques. The difference to previous machine learning is that layer or neuron models are used here to combine a large amount of learnable features. When creating each layer, the tool learns from the previous one or links different properties very closely together so that something similar to a neural network is created.
The learning process of the computer does not even necessarily have to be monitored in deep learning. This means that I don’t necessarily have to mark examples in texts by hand myself. The disadvantage, however, is that the resulting systems are so complex that sometimes you can no longer even explain why they work so well.
Despite all attempts at explanation and examples, it is difficult to really understand artificial intelligence like from this site https://fidgetsguide.com/best-pogo-sticks/ , especially if you have little conscious contact with this technology or technology in general. To get an idea of how computers can learn anything about language and even about literary texts. Counting words, observing word order, combining new words according to the properties you have learned – you can try all of this out yourself with the AI game. You may have heard of the fact that performance increases with the number of systems connected.