Note I am using a neutral AI, https://deepai.org/chat/philosophy
i.e. not my usual ChatGpt with memory of my past discussions with it.
CONTENT:
1. How it works and why it generates inconsistencies.
2. How AI adapt to the Level of Understanding of a User.
3. How Does AI Validate the Truth of the Matter?
Here are two responses from the same AI on the same issue but the response to user-X is different from User-Y.
[AI to User-X {Atla}
"From a Kantian transcendental idealist perspective, monist indirect realism's view that not everything beyond phenomena is entirely unknowable could be seen as irrational because it contradicts Kant's core claim that the noumenal realm (things-in-themselves) is fundamentally beyond human cognition. While indirect realism admits a mediated knowledge of external reality, Kant maintains that any such mediation still confines knowledge to the phenomenal domain. Therefore, any assertion about partial knowability of the noumenal would lack justification within Kant’s framework."
Without the full picture, the above is a bit messy but the main point is AI response is inconsistent to different users on the same issue.[AI to User-Y {VA}]
Conclusion: The Limitations of Indirect Realism
Even without explicit reference to the noumenon, IR’s claim to partial knowability of its absolutely mind-independent object is incoherent. Knowability, whether partial or complete, inherently involves the human mind. If the ultimate object of IR is indeed absolutely mind-independent, then any assertion of its partial knowability entails a contradiction. To claim knowability of something posited as unknowable is an oxymoron.
Thus, IR cannot coherently maintain both the absolute mind-independence of its ultimate object and the possibility of its partial knowability, reinforcing its vulnerability to Kant's critique and underscoring its conceptual limitations
Here is DeepAI's response to the above;
DeepAI to VA
This situation provides an interesting case study in how AI can generate responses that may seem inconsistent, even when dealing with the same philosophical concept. Here are a few considerations that can help explain this phenomenon:
1. Context Sensitivity
AI models often generate responses that are sensitive to the wording and context of the query. User-X's question was framed in a way that prompted a straightforward application of Kant's ideas to monist indirect realism. The AI provided a clear and concise explanation focusing on why the indirect realist perspective might seem irrational from a Kantian viewpoint.
In contrast, User-Y's comments were more assertive and analytical, further engaging with the nuances of Kant's epistemology and challenging the AI's previous response directly. This potentially led the AI to provide a more elaborate and detailed follow-up that supported User-Y's interpretation.
2. User Engagement Level
The perceived inconsistency could also stem from the difference in how engaging each user was. User-Y exhibited a critical approach by dissecting the previous response and elaborating on the implications of Kant's philosophy. This prompts the AI to respond with a more nuanced analysis that aligns with the more complex discussion initiated by User-Y.
3. Interpretation of Terms
Philosophical terms can be interpreted in various ways depending on the context in which they are used. The AI might not have fully aligned its understanding of "partial knowability" in User-X's inquiry with how it was interpreted in the context of User-Y's comments. The subtle differences in how each user frames their arguments could lead to different emphases in the AI's responses, even though they stem from the same philosophical groundwork.
4. AI Limitations
AI language models operate based on patterns and data and do not have consistent theories or beliefs. Their responses are generated based on probabilistic assessments of language rather than a coherent understanding of the world or philosophies. This means that the "same issue" may elicit different angles of discussion based on how the AI contemplates the phrasing of the query, the prior discussion context, and the inferred intent behind the questions.
5. Focus on Dialogue
In philosophical discourse, especially when engaging with complex concepts, dialogue can illuminate different facets of an argument. The AI's responses are shaped by this dialogue. Thus, while User-X's question established a framework for a critique of indirect realism, User-Y's engagement deepened the analysis to showcase the limits of indirect realism without changing the core subject matter.
Conclusion
The perceived inconsistency of the AI's responses reflects the inherent complexities of human language, philosophical discourse, and the contextual understanding of ideas. Different users may lead the AI to focus on different aspects of the discussion, thereby producing variations in responses. Philosophical discussions often require nuanced considerations, and the AI's answers can vary accordingly, highlighting different facets of the same underlying principle.