A Brief Conversation with ChatGPT

Artificial Intelligence systems, or simply AI, have been in the news constantly over the last six months. Most articles and news reports follow two overarching themes: 1.) AI will revolutionize virtually every field to which it can be applied, dramatically increasing productivity, though it may also mean making many humans who do some/many jobs obsolete; and 2.) AI must be responsibly and ethically integrated into those fields lest we lose control and something heretofore undefined results.

I think it far too soon to adjudicated one way or the other on those concerns. These systems are, despite surface-level appearances, basically still in their infancy, and we have no idea yet what sort of novel ways humans will attempt to leverage them. There are, obviously and rightfully, some reasons to be concerned about future applications and how they might impact human lives and productivity. There are probably moral and philosophical considerations, as well, though I’m not adequately armed to discuss those things at present. But I’m not (yet) worried that my kids will one day live in a Skynet-induced hellscape.

In February, New York Times writer Kevin Roose published a two-hour conversation with Microsoft’s chatbot, code named Sydney and powered by OpenAI’s technology. OpenAI is the maker of ChatGPT. The conversation is long and interesting and if you have even a cursory interest in AI technologies you should give it a read. I got to thinking that I’d like to give ChatGPT a test drive, so to speak, and two days ago I finally was able to make an account and access it (previous attempts were thwarted due to the volume of traffic to the site). I had also wanted to try out the image creation component, in part because my band has been thinking of leveraging AI for visuals for our next album, but that requires a small fee. I did not go as long nor as in depth as Roose did in his test. He’s a paid employee of one of the largest, most respected newspapers in the world and I’m, well, just a random intelligence analyst. To be frank, I really didn’t know what to even ask or discuss; I’m moderately competent at researching things on my own. But I was curious.

I was largely impressed with the outcome, even if my efforts were only brief and somewhat shallow in depth. I do, however, wish that the system had some method of citations so that an enterprising writer or researcher could follow up on the things ChatGPT can spit out. These systems do have great utility, however asking some questions to get started on a research project would still require the writer to do searches in a more traditional way even if the AI’s answers are perfectly correct. In any case, below is a transcript of my questions and ChatGPT’s subsequent responses, inspired in part by a conversation I had with my youngest son recently on his birthday. I have not changed any words, but have added some comments/thoughts where appropriate. I hope that you’ll be so inclined to give these systems your own test drives.

Halen: If there’s a complex item and, over time, all of that items parts have been replaced, what would that be called? [Comment: I purposefully neglected to use the possessive of “items” because I was curious if it would call out the error; I didn’t figure that it would have any issues with understanding the error based on other conversations I’ve read about with AI systems.]

ChatGPT: If all the parts of a complex item have been replaced over time, it could be referred to as a “fully refurbished” or “completely rebuilt” item. Alternatively, if the replacement parts significantly alter the original design or functionality of the item, it might be called a “modified” or “upgraded” version. The specific terminology used can vary depending on the context and industry involved.

Halen: Would you characterize that as Theseus’s Ship, as well? [Comment: I asked the above question in the way that I did because I wanted to see if it would describe the paradox from the outset rather than provide a simpler answer; it clearly did the latter, so I wanted to expound upon the idea to set up discussion of logical fallacies. Not that the AI particularly cared about my motivations.]

ChatGPT: Yes, the scenario you described can be compared to the philosophical concept known as “Theseus’s Ship” or “Ship of Theseus.” According to the paradox, if all the parts of a ship are replaced over time, is it still the same ship?

In the case of the complex item you mentioned, where all the parts have been replaced, one could draw parallels to the Ship of Theseus paradox. If every part of the item has been replaced, one could question whether it is still the same item or if it has fundamentally transformed into something new.

The Ship of Theseus paradox raises interesting questions about identity, continuity, and the nature of objects. Different perspectives and philosophical frameworks can offer varying interpretations and arguments regarding the nature of identity in such cases.

Halen: Could an AI, over time, have something similar happen? Could it be described in terms of that paradox if all its parts were replaced?

ChatGPT: Yes, the Ship of Theseus paradox can be applied metaphorically to an AI system as well. If all the components or parts of an AI system were gradually replaced or upgraded over time, it raises questions about the continuity and identity of the AI.

From a philosophical standpoint, one can ponder whether the AI remains the same entity or if it has transformed into something new with each replacement or upgrade. Does the continuous evolution of the AI’s hardware, software, and underlying algorithms alter its identity or core essence?

It is worth noting that the application of the Ship of Theseus paradox to an AI system may be more abstract since we are dealing with a digital entity rather than a physical object. However, the underlying concept of identity and continuity can still be contemplated in the context of an evolving AI.

Halen: That’s a good point regarding a digital entity rather than a physical object. [Comment: I was honestly quite impressed that the answer was so nuanced as it pertains to AI systems. I was curious if it would make a distinction between its physical components and, well, software. It mostly did, without me having to ask directly.]

ChatGPT: Indeed, the nature of digital entities, such as AI systems, introduces unique considerations when contemplating the Ship of Theseus paradox or similar thought experiments.

Unlike physical objects, which have a tangible presence and discrete parts, digital entities exist in the realm of information and computation. The components of an AI system, such as algorithms, models, and data, can be modified, replaced, or upgraded without a physical change or loss of identity.

In the context of AI, the question of identity and continuity may revolve more around the underlying functionality, behavior, and capabilities of the system rather than the physicality of its parts. As long as the AI system maintains its purpose, core functions, and overall behavior, it can be seen as a continuation of its original identity, even if its components have undergone significant changes.

However, it is important to note that these philosophical discussions surrounding the nature of identity in digital entities are still topics of ongoing debate and exploration, and different perspectives and theories exist regarding the essence of digital identity and the impact of component replacement or modification.

Halen: Ah. In the context of physical components, the paradox would be more readily applicable. Less so for the underlying systems such as those you describe.

ChatGPT: Yes, that’s correct. The Ship of Theseus paradox finds more direct applicability and resonance when considering physical objects with tangible components. The paradox raises thought-provoking questions about the nature of identity and the impact of component replacement or modification on the continuity of an object.

In the case of physical components, the paradox is often discussed in terms of gradual replacement. If every part of an object is replaced over time, at what point does it cease to be the same object? Does its identity reside in its material composition, its form, its function, or something else entirely?

While the paradox may not have a straightforward answer, it stimulates contemplation on the philosophical notions of identity and the essence of objects.

In contrast, when considering digital entities like AI systems, the discussion around the Ship of Theseus paradox becomes more abstract. The focus shifts to the functional aspects, behavior, and purpose of the AI system rather than the physical components themselves. The continuous evolution and modification of digital systems can be seen as a natural part of their lifecycle without necessarily challenging their identity or continuity in the same way as with physical objects.

Halen: Tell me about another, random philosophical paradox.

ChatGPT: Certainly! Let’s explore the “Paradox of Buridan’s Ass.”

The Paradox of Buridan’s Ass is a thought experiment that poses a dilemma regarding decision-making and rationality. It is named after the 14th-century philosopher Jean Buridan, who used it to illustrate a paradoxical scenario.

The paradox involves an ass that is equally hungry and equidistant between two equally appealing bales of hay. The crucial element is that the ass has no reason to prefer one bale of hay over the other since they are identical. In this state of perfect indecision, Buridan argued that the ass would starve to death, unable to make a rational choice between the two options.

The paradox raises questions about the nature of decision-making, rationality, and the role of preferences. On one hand, it highlights the importance of preferences in guiding our choices. Without any discernible difference or preference, the ass becomes paralyzed by indecision. On the other hand, it challenges the notion of rationality, as the ass, being equally hungry and equidistant, seemingly fails to make any rational decision, leading to an irrational outcome.

The Paradox of Buridan’s Ass encourages reflection on the limits of rational decision-making, the role of preferences and values in guiding choices, and the potential consequences of indecision or perfect rationality in certain scenarios.

It’s worth noting that this paradox has generated various responses and proposed solutions over the years, ranging from arguments about the relevance of subjective factors to the introduction of slight differences or external influences that could break the symmetry and allow for a decision to be made.

Halen: Very interesting. It seems to me that the ass would have a preference largely due to physiology. Perhaps the ass favors things on the left as opposed to the right.

ChatGPT: That’s an intriguing perspective! Introducing physiological preferences or asymmetries is indeed one way to approach the Paradox of Buridan’s Ass and potentially resolve the dilemma.

In reality, animals often exhibit subtle physiological biases or preferences that could influence their choices. For example, some animals might have a natural inclination to turn or move in a particular direction, preferentially favoring the left or right side. Such biases, even if slight, could break the symmetry of the scenario and provide a basis for the ass to make a decision.

By considering physiological factors or subtle biases, we introduce a relevant element that can guide the ass’s choice and resolve the paradox. It highlights the role of subjective or non-rational factors in decision-making, suggesting that even seemingly rational decisions may have underlying influences that are not purely based on objective evaluations.

Your observation underscores the importance of considering additional factors, beyond pure rationality, when analyzing decision-making scenarios and philosophical paradoxes like Buridan’s Ass.

Halen: In a similar vein, can you explain to me a random logical fallacy?

ChatGPT: Certainly! Let’s discuss the logical fallacy known as the “appeal to ignorance,” also referred to as argumentum ad ignorantiam.

The appeal to ignorance is a fallacy that occurs when someone argues that a proposition is true because it has not been proven false or that it is false because it has not been proven true. It relies on the lack of evidence or knowledge as the basis for drawing a conclusion.

Here’s an example to illustrate the fallacy:

Person A: “There must be intelligent life on other planets because no one has proven that there isn’t.” Person B: “But there is no evidence to support the existence of intelligent life on other planets.” Person A: “Exactly! That’s why it must be true.”

In this example, Person A argues that the proposition of intelligent life on other planets is true based on the lack of evidence to disprove it. This reasoning is fallacious because the absence of evidence or proof does not necessarily make a claim true or false.

The appeal to ignorance disregards the need for positive evidence to support a claim. It mistakenly assumes that a lack of evidence against a proposition automatically validates it. However, in logic and critical thinking, the burden of proof lies on the person making the claim, not on others to disprove it.

To avoid this fallacy, it is important to recognize that the absence of evidence is not equivalent to evidence of absence. A proposition should be evaluated based on positive evidence, logical reasoning, and a careful examination of available information, rather than relying on a lack of evidence to draw conclusions.

Halen: Well put. [Comment: I missed an opportunity to bring up Hitchens’ Razor: “What can be asserted without evidence can also be dismissed without evidence.”]

ChatGPT: Thank you! I’m glad you found the explanation helpful. Logical fallacies are important to recognize in order to ensure clear and valid reasoning. If you have any more questions or if there’s anything else I can assist you with, please feel free to ask!

Halen: Do you think that AI can suffer from logical fallacies?

ChatGPT: AI systems themselves, such as language models like me, are designed to process and generate information based on patterns and examples in the data they have been trained on. As a result, AI systems can generate responses that may appear logical on the surface but can still be susceptible to certain types of logical fallacies.

AI systems can inadvertently reproduce fallacious reasoning if they encounter examples of such reasoning during training. For instance, if an AI model is trained on data that includes instances of the appeal to ignorance fallacy, it might learn to produce responses that exhibit similar flawed reasoning.

However, it’s important to note that AI systems do not possess conscious awareness or subjective experiences, so they do not “believe” or hold genuine opinions. They are tools created by humans and are only as reliable as the data and algorithms used to build and train them.

The responsibility for identifying and addressing logical fallacies in AI-generated responses lies with the users and developers of AI systems. It is essential to critically evaluate the information provided by AI and not solely rely on it for making important decisions or judgments. Humans should exercise critical thinking skills and be aware of the limitations and potential biases of AI systems.

Halen: Excellent point. I did not think about that. As an old Master Sergeant told me, “Garbage in, garbage out.” Would that be applicable to this? [Comment: I used a colloquialism primarily to see how well it understood such things, particularly in the context of a somewhat formal philosophical discussion.]

ChatGPT: Absolutely! The saying “garbage in, garbage out” is highly applicable in the context of AI systems. It emphasizes the importance of input quality in determining the output or results produced by a system.

AI systems heavily rely on the data they are trained on. If the training data is biased, incomplete, or of poor quality, it can significantly impact the performance and reliability of the AI system. Inaccurate, misleading, or flawed data can lead to biased or unreliable outputs, which can be considered as “garbage” in terms of the desired results.

To mitigate this, it’s crucial to ensure high-quality, diverse, and representative data during the training process of AI systems. Additionally, ongoing monitoring, evaluation, and improvement of AI models are necessary to address and minimize biases, errors, and logical fallacies that might arise.

Ultimately, understanding the potential limitations and biases of AI systems, and being mindful of the quality of input data, helps in promoting more reliable and responsible use of AI technologies.