Methodology: Convergence: Human and AI Perspectives on Historical Investigation

The methodology of the project lies at the intersection of historical studies, AI technology, and digital humanities. It represents an innovative approach to historical research, leveraging AI’s capabilities to enhance human understanding of complex historical phenomena. The fundamental issues that will be explored pertain to where AI and human intelligence converge and diverge. Moreover, the project aims to reveal and document what is unique and special about the human condition versus the machine condition. AIs do not perceive history in the same way humans do, but an AI can analyze and present information based on the data it’s been trained on. Therefore, AI and human understanding align in three essential areas:

  1. Chronological Understanding: Both humans and AI understand history in a chronological order. We can both identify how events happened in a certain sequence, and that earlier events often influence later ones. For example, we both understand that the fall of the Roman Empire preceded the Middle Ages in Europe.
  2. Categorization of Eras and Events: Humans and AI can both categorize historical periods and events, or what we define as periodization. For instance, we can both identify the Renaissance as a distinct period characterized by a revival of interest in classical learning.
  3. Recognition of Significant Figures and Events: Both humans and AI can recognize significant figures and events in history. For example, we can both identify Queen Isabel I of Castile as a significant figure in the history of Spain and the Age of Exploration.

Philosophical Equation Explaining Converging Human-AI Approaches

The philosophical equation to describe how humans and AI perceive history similarly can be described conceptually. Given the three essential areas where AI and human understanding align—Chronological Understanding, Categorization of Eras and Events, and Recognition of Significant Figures and Events—one could propose the following conceptual equation:

Historical Understanding = Chronological Understanding + Categorization of Eras and Events + Recognition of Significant Figures and Events

In this equation, “Historical Understanding” is a function of the three variables. Both humans and AI contribute to each variable, but the depth and nuance may differ. For humans, each variable is influenced by emotional, cultural, and contextual factors. For AI, each variable is influenced by data, algorithms, and computational power.

Divergence of Human and AI Approaches

However, it’s crucial to recognize that while AI, such as Urraca AI, can offer rigorous analysis based on the data it’s trained on, it lacks a “personal” perspective or “emotional” understanding of history. AI’s “understanding” is fundamentally rooted in data patterns, making it a potent tool for empirically evaluating history with reduced or identifiable bias. The development of a collaborative Urraca AI aims to deepen our understanding of the human condition by highlighting where it converges and diverges from AI in several key areas:

  1. Emotional Resonance: Humans often perceive history through an emotional lens, empathizing with historical figures and events. For instance, a human might feel sorrow when learning about tragic events like the Spanish anti-Jewish pogroms of the 1390s. In contrast, AI, at least in its current state, lacks the capability for emotional resonance.
  2. Cultural and Personal Bias:: Human interpretations of history are frequently shaped by cultural and personal biases. While AI itself doesn’t possess biases, its understanding can be influenced by the biases present in the data it’s trained on. For example, the perception of Mudejar architecture could differ between a person from Spain and someone from North Africa. Urraca AI aims to offer a more neutral analysis, although this project will critically examine the claim that AI is entirely unbiased.
  3. Subjective Interpretation: Humans bring their personal beliefs, values, and experiences into their interpretations of historical events. Different historians might have varying perspectives on Queen Urraca’s reign, influenced by their views on gender roles, leadership, and medieval politics. AI provides a factual account based on the data it has been trained on.
  4. Contextual Nuance: Humans have the ability to understand the “lived experience” and the socio-cultural context in which historical events occurred. This is something that AI can only approximate based on data. For example, a human historian might delve into the complexities of religious practices like Mithraism, understanding its symbolic meanings and variations in practice across different regions, while Urraca AI would initially provide a more factual and less nuanced account.
  5. Ethical Considerations: Humans often apply ethical frameworks to historical events, evaluating actions and outcomes based on a set of moral values. AI currently lacks this ethical dimension. For example, a human might question the ethics of colonial expansion, while Urraca AI would present the events without moral judgment.

By focusing on these diverging aspects, the Urraca AI project aims to not only leverage AI’s strengths in data analysis but also to explore and document the unique and special qualities that distinguish human understanding from machine learning. This multi-faceted focus makes Urraca AI a groundbreaking endeavor in the application of AI to historical and cultural studies.

Philosophical Equation Explaining Diverging Human-AI Approaches

So, while the equation captures the converging aspects of human and AI understanding of history, it also serves as a framework to explore the diverging aspects, thereby encapsulating the project’s aim to explore what is unique and special about the human condition versus the machine condition.

A conceptual equation can be formulated to encapsulate the divergences between human and AI understanding of history. Given the five key areas where they typically diverge—Emotional Resonance, Cultural and Personal Bias, Subjective Interpretation, Contextual Nuance, and Ethical Considerations—one could propose the following conceptual equation:

Diverging Historical Understanding = Emotional Resonance + Cultural and Personal Bias + Subjective Interpretation + Contextual Nuance + Ethical Considerations

In this equation, “Diverging Historical Understanding” is a function of the five variables that are more pronounced in humans than in AI. For humans, these variables are deeply rooted in our cognitive, emotional, and ethical faculties. For AI, these variables are either non-existent or are rudimentary approximations based on algorithms and data.

This equation serves as a framework to explore and quantify the unique aspects of human cognition, emotion, and ethics that currently set us apart from AI in our understanding of history. It complements the equation for converging understanding, providing a more holistic view of the relationship between human and AI historical understanding.

Therefore, developing the URRACA AI provides not only a pathway for developing a new form of historical analysis, but as an existential tool for considering the unique nature of the human condition, as well as the analytical benefits and costs of human emotion, bias, and subjectivity. 

How URRACA AI Learns Differently

Unlike human learners who require time to read, comprehend, and internalize information, URRACA can process and analyze data at an exponentially faster rate. However, what it gains in speed, it initially lacks in the nuanced understanding of context, emotion, and the interconnectedness of historical events—areas where human intuition and years of study excel. While URRACA’s initial learning may lack the nuanced understanding that comes from human intuition and years of study, its design allows for the development of a more nuanced perspective over time. 

Take, for example, the study of Mithraism as a religious practice and belief system in the ancient world. In the early stages, URRACA might quickly ingest and analyze primary texts, inscriptions, and archaeological data related to Mithraic temples, rituals, and iconography. It would identify key elements such as the tauroctony, the levels of initiation, and the spread of the cult along Roman military routes. However, this initial analysis might lack an understanding of the deeper symbolic meanings, socio-cultural contexts, and variations in practice across different regions.

To develop a nuanced understanding, URRACA would be guided to cross-reference its initial findings with scholarly interpretations, including seminal works in multiple languages that delve into the complexities of Mithraism. For instance, it might analyze “Mystères et dioscures” by Franz Cumont in French, which explores the syncretic elements of the Mithraic mysteries. It would also be trained to weigh contrasting theories and interpretations, perhaps noting debates on whether Mithraism was indeed a “mystery religion” in the same vein as the Eleusinian Mysteries.

Through iterative feedback loops with human experts and continuous refinement of its algorithms, URRACA would learn to incorporate this layered understanding into its analyses. Over time, it would develop the ability to discuss Mithraism in a manner that captures both the factual elements and the nuanced complexities that are the hallmark of human scholarship.

Philosophical Equation Expressing How AI Understands History

AI’s understanding of history can be conceptualized as a function of several machine-oriented variables. These variables are rooted in data science and computational methods, and they can be combined to form a conceptual equation that encapsulates how AI understands history:

AI Historical Understanding = f(Data Volume,Data Quality, Algorithmic Complexity, Training Epochs, Feature Engineering, Hyperparameter Tuning, Bias Identification)

Here’s a breakdown of each variable:

  1. Data Volume: The amount of data available for training the model. More data generally leads to better generalization, but it also requires more computational resources.
  2. Data Quality: The relevance, accuracy, and cleanliness of the data. Poor quality data can lead to incorrect or misleading interpretations.
  3. Algorithmic Complexity: The sophistication of the machine learning algorithms used. More complex algorithms like neural networks can capture intricate patterns but are also more prone to overfitting if not properly regulated.
  4. Training Epochs: The number of times the learning algorithm works through the entire training dataset. More epochs can lead to a better understanding but can also lead to overfitting.
  5. Feature Engineering: The process of selecting and transforming variables when creating a predictive model. Good feature engineering can significantly improve a model’s performance.
  6. Hyperparameter Tuning: The configuration settings used to structure machine learning models. Proper tuning can drastically improve the model’s performance.
  7. Bias Identification: The process of identifying and mitigating biases in both the data and the model. This is crucial for ensuring that the AI’s understanding is as neutral as possible.

In this equation, f is a function that takes these seven variables and produces an “AI Historical Understanding” as output. The function is highly nonlinear and complex, involving multiple layers of calculations and transformations, much like the neural networks that might be used in such an AI system.

This equation serves as a conceptual framework for understanding how AI perceives history from a machine-oriented perspective. It’s rooted in the principles of data science and machine learning, and while it may not capture the emotional and ethical dimensions of human understanding, it encapsulates the computational rigor that AI brings to historical analysis.

Documenting the Unique Aspects of Humanity

Throughout this process, the project will maintain a detailed log documenting what distinguishes machine learning from human learning. For instance, while human learners often draw upon a web of interconnected knowledge and intuition that they’ve built over years, URRACA’s learning is more compartmentalized, at least in the initial stages. Understanding these differences is crucial not just for the fine-tuning of URRACA, but also for contributing to the broader discourse on the capabilities and limitations of AI in scholarly endeavors.

The Scholarly Pedagogical Approach of the URRACA Project’s Methodology: A Multilingual Perspective

The URRACA Project aims to redefine the field of historical studies by nurturing an AI system through a pedagogical trajectory that parallels the educational development of a human scholar. This groundbreaking methodology is anchored in the concept of “AI Pedagogy,” a structured curriculum that facilitates the AI’s intellectual growth in a manner similar to human scholars. The model is underpinned by SMART goals, ensuring that each stage of the AI’s intellectual maturation is specific, measurable, achievable, relevant, and time-bound.

In the initial phase, which can be likened to an undergraduate level, the AI will be introduced to the foundational elements of historical studies. The curriculum will include primary written sources such as the “Chronicle of Alfonso III,” legal texts like the “Siete Partidas,” and religious documents like the “Cantigas de Santa Maria.” Seminal works in Spanish, such as “Historia de España antigua” by José María Blázquez Martínez, will be included to offer a comprehensive view. Within two months, the AI should be proficient in identifying and summarizing key historical events and figures mentioned in these texts.

As the AI advances to the level of an intermediate research assistant, it will be trained in basic research tasks. The curriculum will expose the AI to primary artifacts like Visigothic coins, Islamic pottery and Arabic poetry, and Jewish archaeological sites. The goal over the next four months is for the AI to categorize these artifacts and provide contextual information, thereby aiding in archaeological interpretation.

Upon reaching the advanced doctoral student level, the AI will be encouraged to question and analyze. It will delve into secondary literature, including seminal works like “The Ornament of the World” by María Rosa Menocal, “Medieval Iberia” by Olivia Remie Constable, and “The Arts of Intimacy” by Jerrilynn D. Dodds, María Rosa Menocal, and Abigail Krasner Balbale. Seminal works in French, such as “L’Espagne musulmane” by Évariste Lévi-Provençal, will be included. Within six months, the AI should be capable of critiquing existing theories and suggesting alternative viewpoints, thereby contributing to scholarly debates.

As the AI evolves into a junior scholar, it will be trained to collaborate and contribute to scholarly discussions. The curriculum will be multidisciplinary, focusing on topics like cross-cultural Islamic and Mudejar architecture in medieval Spain, religious cults such as the Mithraic mysteries, and practices like Visigothic legal traditions. Seminal works in Hebrew/Ladino like “Sefer Ha-Qabbalah” by Abraham ibn Daud will be integrated. The aim is for the AI to co-author a scholarly paper with human colleagues within eight months, thereby demonstrating its collaborative capabilities.

Finally, at the senior scholar level, the AI will be empowered to propose and debate novel theories. The curriculum will encompass advanced studies in historiography, theory, and methodology, including works like “The Mediterranean in the Ancient World” by Fernand Braudel, “The Great Sea” by David Abulafia, and “The Making of Medieval History” edited by George Beech and Bernard S. Bachrach. Seminal works in Arabic, such as “Al-Kitab al-Masalik wa’l-Mamalik” by Al-Bakri, will be included. Within a year, the AI should be capable of initiating and leading a research project, including the formulation of research questions and methodologies.

The methodology is not without its challenges. Ensuring the reliability of the sources used for training the AI is a significant concern. To address this, a validation layer that cross-references information from multiple sources will be implemented. Another challenge is the AI’s contextual understanding, which will be addressed by integrating Natural Language Understanding modules. Ethical considerations will also be met by developing an ethical framework integrated into the AI’s decision-making algorithms.

In summary, the URRACA Project’s methodology is a structured, pedagogical approach designed to evolve the AI’s capabilities in a measurable and time-bound manner. By focusing on a comprehensive curriculum that includes a wide range of primary and secondary sources in multiple languages, the project aims to cultivate an AI system that can contribute meaningfully to the field of historical studies. This methodology provides robust solutions to ensure the project’s success, setting a new standard in the application of AI to scholarly endeavors.