Asking ChatGPT4 About ‘Contemporary Methods of Studying Talmud - Brisk vs. Academia; and the Artifically Intelligent Lamdan’
After receiving further pushback on my speculation that the academic Talmud method may be harder to automate than the Brisker method, I decided to ask ChatGPT4 what it thought. To my surprise, ChatGPT4 disagreed with me, and believed that the Brisker method would be more difficult for an AI to replicate. Here’s the full conversation:
(Link to full conversation here: https://chat.openai.com/share/bee0e359-48d1-4e66-a3d0-0611bd1b37fb)
EB: I'm an academic scholar of talmud. Give me a concise comparison between the traditional Brisker / analytic / conceptual method vs the academic method of talmud interpretation
ChatGPT4: Sure, I'd be happy to compare these two approaches to Talmud study.
Traditional Brisker/Analytic/Conceptual Method: This method, developed by Rabbi Chaim Soloveitchik and others from the Brisk (Brest, Belarus) Yeshiva, emphasizes a logical and analytical approach. The Brisker method typically focuses on the precise definition of legal concepts and categories within the Talmud, often drilling down to the essence of a law or principle. This might involve abstractly deconstructing and reconstructing various Talmudic arguments or ideas, and it often results in a complex, multi-layered analysis of the text.
Academic Method: The academic or critical method, often associated with university settings, takes a historical-critical approach. It emphasizes understanding the Talmud within its historical, linguistic, and cultural context. This method often utilizes external disciplines like archaeology, philology, and comparative religion. It may involve comparing different versions of the text, analyzing the development of legal and theological ideas over time, and understanding the Talmud in relation to other contemporaneous Jewish and non-Jewish works.
In brief, the Brisker method focuses on abstract and conceptual analysis of the Talmud's legal discussions, whereas the academic method emphasizes historical, linguistic, and contextual analysis.
EB: Which one do you think is more amenable to automation via AI LLLM
ChatGPT4: Both methods pose interesting opportunities and challenges for an AI language model like me.
Traditional Brisker/Analytic/Conceptual Method: The highly abstract and logical nature of this approach might be more suitable for AI. The capacity of AI to parse complex structures and reasoning chains might lend itself to this method. However, the depth of analysis and multi-layered arguments typical of the Brisker method could be difficult for an AI to fully replicate. Also, the Brisker method often relies on an implicit, nuanced understanding of the broader Talmudic discourse, which might be challenging for AI.
Academic Method: The academic method also presents potential for AI due to its reliance on cross-referencing, pattern recognition, and contextual analysis, which are strengths of many AI models. However, challenges might include understanding historical and cultural context, as well as grasping subtleties and complexities of ancient languages. AI models might also struggle to incorporate insights from various external disciplines that often inform the academic method.
Overall, elements of both methods could potentially be automated, but a full, nuanced understanding of Talmudic study, particularly the subtler aspects of each method, might still be beyond the capabilities of current AI.
EB: If you had to choose which would more likely be successfully simulated with AI in the next 10 years, which one would it be
ChatGPT4: If I had to choose, I would lean towards the academic method. This is primarily because many aspects of the academic approach align more closely with existing AI capabilities and trends in AI research. For example, an AI can be trained to recognize patterns and make cross-references across large bodies of text, essential skills in the academic method. Additionally, advances in natural language processing and understanding could improve an AI's ability to analyze texts in their historical and linguistic context.
The Brisker method, while not impossible for AI, could prove more challenging due to its reliance on abstract thinking and deep, nuanced understanding of Talmudic discourse, which are areas where AI currently has limitations.
However, it's important to note that this is a rough estimate based on current AI capabilities and trends. The actual trajectory of AI development may vary and lead to unexpected advancements.
EB: Expand on why Brisker method would be challenging, with concrete illustrations
ChatGPT4: Sure, let's take a deeper look at why the Brisker method could be challenging for AI.
The Brisker method involves understanding the underlying conceptual framework of Talmudic discourse and using this understanding to analyze and compare different passages and legal principles. This requires a nuanced understanding of the subtleties of the Talmudic text and a deep appreciation of the specific legal concepts at play.
For example, consider a classic Brisker analysis where two seemingly contradictory Talmudic rulings are harmonized by introducing a novel conceptual distinction. Rabbi Chaim Soloveitchik, for instance, reconciles two apparently conflicting rulings regarding the liability for damages caused by a borrowed cow. He proposes that the liability in Jewish law (Halacha) for borrowed items is not unitary but bifurcated into two different categories: intrinsic and extrinsic liability.
This analysis requires not only a logical deduction but also an intuitive understanding of the conceptual structures of Talmudic law. The AI would not only need to identify the contradiction, but also to independently arrive at a previously unarticulated conceptual distinction that resolves the contradiction.
The Brisker method often relies on these kinds of deep, novel insights into the conceptual structure of Jewish law. This involves a kind of creative and abstract thinking that is currently beyond the capabilities of AI.
Moreover, the Brisker method often involves a tacit understanding of the broader Talmudic discourse and the specific modes of argumentation and analysis employed by the Talmudic sages. AI models, while powerful, are ultimately statistical machines that don't truly understand the text they process. Even advanced models may lack the deep, nuanced understanding of Talmudic discourse required for effective Brisker analysis.