Digital Humanities (DH) is brimming with passionate individuals eager to explore the depths of human culture and history through the lens of technology. Now, a revolutionary tool is transforming the way these enthusiasts approach their work: artificial intelligence (AI). This article delves into the exciting applications of AI in the DH landscape, exploring how it can power people to work smarter, delve deeper, and unlock new avenues of understanding. I will show various ways in which AI can be used for DH work.

Writing

While AI cannot replicate the human touch and creativity fundamental to writing, it offers a diverse toolbox that can significantly enhance the writing process. From overcoming writer’s block and generating initial ideas to conducting research, checking grammar and style, and even exploring different writing styles, AI provides writers with a range of valuable tools to streamline their work and fuel their creative exploration. However, it’s crucial to remember that AI serves as a collaborator, not a replacement, in the writing journey. It is the human writer who ultimately wields the power of the pen, harnessing the capabilities of AI to refine their craft and unleash their unique voice.

Generative AI such as ChatGPT and Google Gemini can help in multiple ways with writing. They can make points for your blog, essay, or post. They can correct any grammatical mistakes. They can rewrite certain sentences. They can get you started with writing by writing the introduction to your piece. The figure below shows part of a response by Google Gemini AI when I asked it if AIs can write.

While one can not fully rely on AIs to write, they certainly are very useful as writing tools especially when you are providing them your own ideas. There are certain copyright implications when AIs are used for the generation of images but these concerns are highly reduced when AIs are employed for writing, especially when the human user is providing a unique idea that can be employed by the AI to write.

Translation

AI has revolutionized the field of translation by offering a suite of powerful tools and techniques that enhance the efficiency and accuracy of the translation process. Machine translation systems, such as Google Translate and DeepL, employ advanced algorithms like neural machine translation (NMT) to translate text between languages. These systems continuously improve through machine learning, analyzing vast amounts of translated data to refine their translations and capture nuances more effectively. Furthermore, Generative AIs such as Gemnini and Chat GPT also have their own peculiar way of translation that is distinct from tools like Google Translate. AI-driven translation memory tools, like SDL Trados and MemoQ, store previously translated segments and suggest them to translators when encountering similar content. This not only accelerates translation but also ensures consistency across documents and projects. Natural Language Processing (NLP) techniques further enhance translation quality by enabling AI systems to understand and generate human language more accurately. NLP algorithms analyze sentence structures, grammar rules, and contextual clues to produce translations that are contextually relevant and linguistically precise.

In addition, AI assists in managing glossaries and terminology databases, ensuring consistency of terminology throughout translations. These tools automatically identify and suggest appropriate translations for specific terms, reducing errors and maintaining coherence. AI can also aid in post-editing machine-translated content by providing suggestions for improving fluency, readability, and accuracy. Post-editing tools analyze translated text and offer alternative phrasing, correct grammatical errors, and highlight potential mistranslations for human editors to review and refine. Moreover, AI-driven content generation platforms assist in creating multilingual content by automatically translating existing texts into multiple languages. While these systems may not match the quality of human translation entirely, they serve as a valuable starting point for further refinement by professional translators. Overall, while AI has significantly streamlined and enhanced the translation process, human translators remain essential for tasks requiring cultural understanding, creative adaptation, and linguistic nuance, ensuring the highest quality of translation output.

Many such services are still under-development and free access is limited to Chat GPT and Gemini but in the future, we can expect to get more access to such tools that will significantly increase the speed and accuracy of translation. This can have major implications for DH work in various languages and for creating multilingual DH projects.

Image Generation

The realm of visual creation is undergoing a dramatic shift with the emergence of AI-powered image generation. This innovative technology empowers users to translate their written descriptions into stunning visuals, spanning the spectrum from photorealistic landscapes to abstract artistic expressions. Tools like DALL-E and Midjourney allow users to describe their desired image using specific keywords and phrases, prompting the AI to generate visuals in various styles, color palettes, and compositions. These tools unlock a universe of possibilities for artists, designers, and even casual users, enabling them to bring their creative visions to life in an entirely novel way. However, it’s crucial to acknowledge that AI image generation is still in its infancy. While tools like Stable Diffusion offer advanced customization options like image size and specific details, ethical considerations remain paramount. Concerns regarding potential biases within the training data and the ownership of AI-generated artwork are crucial aspects of this rapidly evolving technology. As this technology continues to develop, addressing these concerns will be essential to ensure its responsible and ethical application in the realm of visual creation.

If these ethical concerns are settled, something which seems unlikely, then these image generation AIs can prove to be very helpful for DH work, helping us create pictures and illustrations. OpenAI is now even testing video generation which can prove to be even more useful and help with a variety of DH projects.

Coding

Another field in which AI can be very helpful is generating code. AI is revolutionizing code generation, aiding developers in various tasks. Through neural networks, it offers auto-completion tools, speeding up coding with intelligent suggestions. It also assists in code synthesis from high-level specifications, enabling faster development. AI aids in refactoring and optimization by identifying inefficiencies and suggesting improvements. Additionally, it facilitates rapid prototyping by generating and refining code iteratively. Despite challenges, AI promises to reshape software development, making it smarter and more efficient. The figure below shows what Gemini AI gave as output when I asked it for a certain code.

Conclusion

In conclusion, I think it is important to acknowledge the various ways in which AI can help us make our work better and more efficient. At the same time, there are technical and ethical concerns that are attached to it. Technical concerns include that the writing style of AI is different than humans, the code it might generate might be wrong, the images might have some problems, or the translation output by it has problems. At the end, we need to find the errors and correct them. That is where the human factor remains very important.

An introduction to GDSL and its tools has already been given in a previous blog post. In this blog, I will attempt to explain the utility of another GDSL tool, namely n-gram. An n-gram is a contiguous sequence of n items from a given sample of text or speech. These items can be characters, words, or even other units like phonemes or syllables, depending on the context. N-grams are widely used in natural language processing (NLP) and computational linguistics for various tasks, including language modeling, text analysis, and machine learning.

The “n” in n-gram represents the number of items in the sequence. Commonly used n-grams include:

1. Unigrams (1-grams): These are single items, which are typically individual words. For example, in the sentence “The quick brown fox,” the unigrams are “The,” “quick,” “brown,” and “fox.”
2. Bigrams (2-grams): These consist of pairs of adjacent items. In the same sentence, the bigrams would be “The quick,” “quick brown,” and “brown fox.”
3. Trigrams (3-grams): These consist of sequences of three adjacent items. For the same sentence, the trigrams would be “The quick brown” and “quick brown fox.”

N-grams are often used in language modeling to estimate the probability of a specific word or sequence of words occurring in a given context. They are also used in various NLP tasks, such as text generation, machine translation, and sentiment analysis. N-grams provide a way to capture some of the context and relationships between words in a text, which can be useful for many language-related applications.

In GDSL, the n-gram analysis can be used in two ways:

1. Word Cloud: Word Cloud is a visual representation of a collection of words, where the size of each word is proportional to its frequency or importance in the text. Typically, word clouds are used to quickly and visually convey the most prominent words in a piece of text, making it easy to identify the most common or significant terms at a glance.
2. Term Frequency: Term Frequency (TF) is a fundamental concept in natural language processing, information retrieval, and computational linguistics. It serves as a quantitative measure of the frequency of occurrence of a specific term or word within a document or text corpus, thereby aiding in the assessment of the term’s significance and relevance in a particular textual context. In essence, TF offers a means to quantify the emphasis placed on individual terms within documents

Both these tools can provide a useful way to understand the main concepts, ideas, and words in a textual corpus. Here is an example of a word cloud made from our test content set.

To attain precision in n-grams, qualifiers in search can be utilized. First, create a content set CS with parameters X and Y. Then generate a hypothesis Z about CS. Z could be about the influence of another factor, an explanation behind certain events, or a correlation with other factors. Once the hypothesis has been generated, incorporate it into your search by adding yet another parameter that corresponds to Z. Now, the new content set created by parameters X,Y and Z would be a subset of the prior content set. Analyzing (A∩B)’ union would give insight into what data was not taken into account when parameter Z was introduced. This can usually aid in identifying different clusters of data within the same corpus. In this case, the word clouds can also aid in visual identification since the word clouds would appear to be different for the two content sets.

For example, compare the first word cloud of the data set with parameters X and Y where X = Pakistan, Y = War and function = AND. The hypothesis here was that in this content set, there are two clusters; one that reports the war between India and Pakistan and another that reports the war between Pakistan and Afghanistan (and the Soviet Union). To check for this, parameter Z was added (Z = India). Given this, (A∩B)’ must be analyzed. And rightly so, Soviet is not found in XYZ but is available in XY. This confirms our hypothesis.

Although this might be a little complex, it can help greatly in understanding and qualifying data.

The place of the missing data can also tell about the frequency of it in XY as a whole.