Automated historical and cultural knowledge

Generative AI models like language models (LLMs) and image generators can offer novel ways of exploring historical and cultural knowledge. For instance, LLMs trained on diverse cross-cultural texts could recreate historical narratives, generate interactive histories, and offer insights into cultural influences and connections. An example is using Generative Pretrained Transformer 3 (GPT-3) by OpenAI to provide interactive storytelling by taking historical facts and generating immersive narratives, thereby making history more engaging.

In terms of visual culture, AI image generators could take descriptions of historical scenes or events and generate detailed imagery. For instance, given a description of a Renaissance painting or an ancient architectural monument, a model like DeepArt or DALL·E could generate an interpretative visual reconstruction. Additionally, such AI tools can recognize patterns in thousands of paintings or artifacts allowing an unprecedented scale of cultural analysis. These applications can automate the understanding and dissemination of historical and cultural knowledge, making it more interactive and accessible.

This is an example of an application you can build using Clevis, a versatile development tool. The app, named 'Automated Historical and Cultural Knowledge,' provides an engaging platform for users to learn about various historical and cultural topics through an interactive quiz format.

The application's operation unfolds in a series of clear steps. Initially, the app prompts the user to input a historical or cultural topic they are keen to learn about. This step is labeled as Text Input. The next step, Http Request, involves the app making an API call to retrieve pertinent information based on what the user enters.

Once the data is fetched, it's passed on to ChatGPT, an AI developed by OpenAI. This is the ChatGPT step. ChatGPT then processes this information and generates multiple-choice questions centered around the retrieved information. These questions are then presented to the user in another Text Input step, where they pick a preferred answer.

Finally, the selected answer is processed, and the app reveals the correct response. Additionally, it provides further context and offers a concise topic explanation in the Display Output phase. Thus, users not only get to test their knowledge but also learn insightful details about the chosen topic.

You can tailor and build similar applications within this area using the Clevis tool.