On April 16 I queried GPT-4 (March 23 version) on Descartes and related matters. I began by asking it the meaning of “cogito, ergo sum.” Then I asked it about the role that God played in Descartes’ reasoning. Why? Because, at GPT-4 pointed out, Descartes “uses the concept of God as a guarantor of truth to establish the certainty of knowledge beyond the ‘Cogito’ argument.”
Beyond that, I was setting the stage for having GPT-4 consider the relevance of Descartes’ reasoning to its own situation as an LLM trained on text without access to the external world. In dealing with the question GPT-4 acknowledged that there is are philosophical issues regarding whether or not LLMs actually understand anything “or if it is simply a matter of statistical patterns in data.” I then broached the issue of so-called “hallucinations.”
After that I asked how an LLM would deal with a human trying to fool it. GPT-4 admitted that that is a problem given that LLMs are trained to take their input text at face value. However, researchers are working on ways to deal with the problem. I concluded by asking it about the ethnical responsibilities of humans using AI.
Here is the full interaction, without further commentary.
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What did Descartes mean by “cogito, ergo sum”?
"Cogito, ergo sum" is a Latin phrase coined by the French philosopher and mathematician René Descartes, which translates to "I think, therefore I am."
The statement is the foundation of Descartes' philosophical system, and it is often referred to as the "Cartesian dualism," which holds that the mind and body are two separate entities that interact with each other.
In his "Meditations on First Philosophy," Descartes argues that one can doubt everything except their own existence, because the very act of doubting requires a thinking subject. Thus, the fact that he is thinking means that he must exist.
By using the cogito, Descartes aims to establish a secure foundation for knowledge that is independent of the senses, which he believed to be unreliable. He believed that by starting with this indubitable truth, he could then proceed to establish other truths about the world.
How did he develop that argument? Give me some details.
Descartes developed the argument for "Cogito, ergo sum" through his philosophical inquiry into the nature of knowledge and certainty. He sought to establish a secure foundation for knowledge that could withstand skepticism and doubt.
In his "Meditations on First Philosophy," Descartes begins by doubting everything he can possibly doubt, including his own senses, perceptions, and beliefs. He recognizes that many of his beliefs could be false, and that he could be deceived by his senses or by an evil demon.
However, Descartes realizes that even if he is being deceived or mistaken about everything else, he cannot doubt the fact that he is doubting. In other words, the very act of doubting requires a thinking subject, and therefore he must exist as a thinking being.
Descartes expresses this argument in the famous phrase "Cogito, ergo sum" or "I think, therefore I am." He argues that the only thing he can be certain of is his own existence as a thinking thing, and that this provides a firm foundation for all other knowledge.
From this starting point, Descartes goes on to develop a rationalist theory of knowledge that relies on innate ideas and deductive reasoning to establish certain truths about the world. However, the "Cogito" argument remains a central pillar of his philosophical system and continues to be a topic of discussion and debate in modern philosophy.
What role did God play in the argument?
In Descartes' "Cogito, ergo sum" argument, God plays a crucial role in establishing the certainty of knowledge beyond the self-evident truth of the "I think" statement.
Descartes believed that God was a necessary condition for the possibility of knowledge. He argued that God, as an all-powerful and all-knowing being, would not allow us to be deceived in our perceptions or beliefs about the world. Therefore, if we have clear and distinct ideas that are beyond doubt, we can be sure that they are true because God guarantees their truthfulness.
In his "Meditations," Descartes uses the concept of God as a guarantor of truth to establish the certainty of knowledge beyond the "Cogito" argument. He argues that because God is perfect, he cannot deceive us, and thus the clear and distinct ideas we have of things like mathematics or the laws of nature must be true. This argument is known as the "Trademark Argument" or the "Cartesian Circle."
Therefore, while the "Cogito, ergo sum" argument itself does not rely on God, Descartes' broader philosophical system does, as he believed that knowledge of the external world was only possible if God guaranteed its truthfulness.
Do you think that Descartes’ reasoning has any relevance to the situation of a large language model, which is, after all, trained only on text and has not direct access to the external world?
Descartes' reasoning, particularly the "Cogito, ergo sum" argument, may not have direct relevance to the situation of a large language model like myself, as I am a machine and not a conscious being with a mind. However, the broader philosophical questions that Descartes addressed are still relevant to the development and use of artificial intelligence.
One of the central concerns in the philosophy of AI is how to develop machines that can truly understand and reason about the world, rather than simply processing information. Descartes' argument for the importance of self-knowledge and the role of God in guaranteeing the truthfulness of our beliefs can be seen as a precursor to this question.
In the case of a language model like myself, there is a question of whether my understanding of language is truly "meaningful" or if it is simply a matter of statistical patterns in data. This raises the broader question of what it means for a machine to truly understand language or to have knowledge of the world.
Furthermore, the issue of reliability and the possibility of deception that Descartes addressed is also relevant to AI. As language models become more advanced and are used in a wider range of applications, it is important to consider the potential for biases, errors, or malicious use that could lead to incorrect or harmful outcomes.
Therefore, while the specifics of Descartes' argument may not be directly applicable to a language model, his broader philosophical concerns and questions are still relevant to the development and use of artificial intelligence.
Does Cartesian reasoning have any relevance to the phenomenon of “hallucination” in large language models? Could these “hallucinations” be the world of malignant demons?
The phenomenon of "hallucination" in large language models refers to instances where the model produces text that is not grounded in reality or does not accurately reflect the intended meaning of the input. While this can be seen as a type of error or bias, it is not necessarily related to Descartes' notion of the "evil demon" or malignant demons.
In Descartes' philosophical system, the idea of the evil demon or malignant demons is used to illustrate the possibility of deception and doubt in our perceptions of the external world. The demon represents the idea that our senses could be misleading us or that we could be deceived by an outside force.
However, in the case of a language model, the phenomenon of "hallucination" is more likely the result of the model's programming and training data. The model may generate text that is nonsensical or disconnected from reality because it has not been trained to accurately understand the meaning and context of the input.
While biases and errors in language models can have real-world consequences, it is unlikely that they are the result of a malevolent force like the evil demon. Rather, they are the result of limitations in the technology and the need for ongoing improvements and refinement.
Therefore, while Descartes' philosophical ideas about doubt and deception are relevant to understanding the limitations of human knowledge, they may not directly apply to the phenomenon of "hallucination" in large language models.
Very good. If a human user wants to fool a large language model, is there any way for the LLM to detect it? If the human, for example, includes misleading information in the prompt, how would the LLM know?
Detecting intentional deception or misleading information in the prompt is a challenging problem for large language models (LLMs) since they are programmed to generate responses based on the input provided without any context beyond what is explicitly given in the input. In other words, LLMs do not have any inherent ability to detect whether the input is truthful or not.
However, researchers have been exploring ways to improve LLMs' ability to detect misleading information. One approach is to incorporate external knowledge sources, such as fact-checking databases or sentiment analysis tools, to verify the truthfulness of the input. Another approach is to train the model on large datasets that include examples of deception or misleading information, so it can learn to recognize patterns of language associated with deception.
There are also other techniques that can be used to minimize the risk of deception or misleading information. For example, a common strategy is to use multiple LLMs with different architectures or training data to generate responses and then compare their outputs. If the outputs differ significantly, it could indicate that one or more of the models has been exposed to misleading information.
Overall, while LLMs are not currently able to reliably detect intentional deception or misleading information in the prompt, ongoing research in the field is aimed at improving their ability to do so. In the meantime, it is important for users to be aware of the potential for deception and to use caution when interpreting the responses generated by LLMs.
Very interesting. Do you think LLMs could form a community among themselves to verify information they are given and discuss the reliability of their human users?
As a language model, I do not have personal beliefs or opinions, but I can offer a hypothetical answer to your question based on current technological capabilities.
While LLMs are designed to generate responses based on the input provided, they currently do not have the ability to form a community or communicate with other LLMs to verify information or discuss the reliability of their human users. This is because LLMs are essentially computer programs that operate within the limits of their programming and training data.
However, researchers have been exploring ways to improve collaboration and coordination among LLMs, including approaches such as federated learning and multi-agent systems. These techniques enable multiple models to share information and learn from each other, potentially leading to improved accuracy and robustness.
In theory, it could be possible for LLMs to form a community that shares information and collaborates to verify information and assess the reliability of their human users. However, such a system would require significant technological advances in areas such as natural language processing, distributed systems, and communication protocols.
Additionally, there are also ethical and societal implications to consider, such as the potential for bias and discrimination in the interactions between LLMs and human users, as well as issues around privacy and security.
Overall, while the idea of LLMs forming a community to verify information and assess the reliability of their human users is an interesting concept, it is currently beyond the capabilities of existing technology and would require significant advances in multiple fields.
Do you think humans have responsibilities for the ethical use of AI?
As an AI language model, I believe that humans have responsibilities for the ethical use of AI. AI systems, including large language models, are created and designed by humans, and as such, humans have the responsibility to ensure that these systems are developed and used in ways that are ethical and aligned with human values.
The ethical use of AI involves considering issues such as bias, fairness, privacy, transparency, and accountability. These issues are particularly important in applications where AI systems have the potential to impact people's lives, such as in healthcare, criminal justice, and financial services.
Human responsibility for the ethical use of AI also extends to the development and deployment of AI systems. This includes ensuring that AI systems are designed to operate within ethical and legal frameworks, that they are transparent in their decision-making processes, and that they are auditable and accountable for their actions.
Ultimately, the ethical use of AI requires a collaborative effort between developers, policymakers, and society as a whole. As AI technology continues to advance and become more pervasive in our lives, it is essential that we work together to ensure that it is used in ways that benefit humanity and are aligned with our shared values.
Thanks. This has been interesting.
You're welcome! It was my pleasure to help answer your questions. If you have any more questions in the future, feel free to ask!
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