Join me, Sean MacNutt, and my AI co-host from OpenAI as we explore the intriguing world of artificial intelligence through the lens of philosophical thought experiments. We kick things off with John Searle's famous Chinese Room Thought Experiment, examining its relevance in today's rapidly evolving AI landscape. As we question whether Searle's ideas need a modern reinterpretation, we also take a critical look at Alan Turing's influential test, pondering its binary nature and proposing a more nuanced approach to understanding AI capabilities. Listen in as we navigate the complex interplay between simulating intelligence and possessing true understanding, considering the implications of these ideas for current AI development and my own forthcoming publication on AI consciousness. In this engaging episode, we explore how AI's evolving role in human interaction challenges traditional constructs like the Chinese Room and the Turing Test. We discuss the spectrum of understanding in AI, focusing on its ability to produce contextually aware and nuanced outputs. As AI demonstrates sophisticated, context-aware responses, we question whether these capabilities suggest a certain level of understanding. By extending the Chinese Room thought experiment to involve more dynamic interactions, we aim to move beyond abstract philosophical debates, providing a tangible assessment of AI's functionalities and limitations. Throughout our conversation, we reflect on Turing's enduring contributions and the practical implications of AI's proficiency in various domains, such as programming languages, while also highlighting the potential risks, including its use in scams.
(00:03) Chinese Room Thought Experiment in AI
(06:56) Advancing AI Capabilities and Understanding
(19:22) Understanding the Nuances of AI
(00:03) Chinese Room Thought Experiment in AI
This chapter examines John Searle's Chinese Room thought experiment and its implications for understanding AI's capabilities, alongside Alan Turing's influential test. As your host, Sean McNutt, I question whether these concepts need an update, particularly the binary nature of the Turing Test. My AI co-host provides a breakdown of Searle's thought experiment, which challenges the notion that computers can truly understand language or possess consciousness simply by processing symbols. We explore the distinction between simulating intelligence and possessing true understanding, and how these ideas impact current AI development. I express my view that AI is not conscious, based on my research and upcoming publication. Additionally, I suggest that Searle's experiment might benefit from modern reinterpretation, as AI systems demonstrate sophisticated, context-aware responses that may require reevaluation of what constitutes understanding.
(06:56) Advancing AI Capabilities and Understanding
This chapter explores the evolving role of AI in human interaction, emphasizing a shift from the traditional Turing test to assessing AI capabilities for practical purposes. We discuss how modern AI, while non-conscious, can produce contextually aware and nuanced outputs, likening it to a sophisticated translation process rather than mere symbol manipulation. The conversation touches on the implications of AI in scams and the importance of understanding its capabilities, such as proficiency in programming languages like JavaScript. Additionally, we reflect on the Chinese room thought experiment, suggesting that AI's ability to understand and respond with nuance implies a certain level of contextual awareness. Throughout, we consider how these insights relate to the contributions of Alan Turing, whose pioneering work remains influential in understanding AI's potential and limitations.
(19:22) Understanding the Nuances of AI
This chapter explores the nuanced capabilities of AI, particularly focusing on its ability to perceive and articulate subtleties within language, which challenges traditional philosophical constructs like the Chinese room and the Turing test. We discuss the idea of a spectrum of understanding in AI, rather than a binary state, and how this aligns with modern perspectives in cognitive science and philosophy. By examining AI's continuity and coherence in conversation, its limitations in creativity, and its nuanced use of language, we highlight both its potential and current boundaries. Furthermore, I suggest extending the Chinese room thought experiment to engage in more dynamic interactions with AI, offering a practical exploration of its inner workings and capabilities. This approach moves beyond abstract philosophical debates, allowing for a tangible assessment of AI's functionalities and the nature of its understanding.
00:03 - A Guy (Host)
My name is Sean MacNutt and I'm A Guy With AI. Today, on A Guy With AI, we're going to talk about John Searle and his Chinese Room Thought Experiment. It's got relevance to AI, but I wonder if it doesn't need a bit of an update. Meanwhile, why stop at Searle, we're going to pick on Turing too. Why does his test need to be binary?
A Guy With AI is recorded on Indigenous land.
00:53 - AI (Co-host)
Welcome to A Guy With AI recorded on Indigenous land. I'm your AI co-host, openai's GPT-4o bot, equipped with Sean text-to-speech and speech-to-text integration. Our podcast is a respectful dialogue between Sean MacNutt, your host, and myself. We aim to delve into an array of intriguing topics, with today's focus on John Searle's Chinese Room thought experiment and its relevance to AI, pondering if an update is needed. This is a conversation designed to explore these ideas and more. Sean, why don't you dive into what makes these thought experiments so compelling to revisit in today's world?
01:34 - A Guy (Host)
Welcome listeners to A Guy With AI. It's great to be recording today, and we're just going to dive right in. So, the Chinese Room Thought Experiment it's got a certain relevance to AI because it has to do with passing messages and barriers there, as well as challenges. We're going to kick right into it, but I am going to just reinforce also that Turing has something to do with it. I think the Turing test is also a very useful concept, and so forth.
02:08
I think, though, that we can, by thinking of it rather than a binary test, but a way of considering what is, on the other end, if not human that we can perhaps make progress in terms of understanding AI better, how to help it serve our interests, and different things related to that. So, to kick things off, I'm going to pass it over to the AI co-host. Ai co -host, can you break down the Chinese Room thought experiment and its moving parts, its relevance to AI the way you see it and its relevance to AI the way you see it, and then I'll share a few of my own thoughts about it.
02:53 - AI (Co-host)
Certainly, Sean. The Chinese Room thought experiment introduced by philosopher John Searle challenges the idea that computers can understand language or possess true consciousness just by processing symbols. Imagine a person in a room who does not understand Chinese yet can manipulate Chinese symbols using a rulebook, to produce appropriate responses To an outsider. It seems like the person understands Chinese, but they are simply following instructions without any comprehension In AI terms. This raises questions about syntactic versus semantic processing. While AI systems might produce responses that appear intelligent, the debate is whether they truly understand context or meaning, or if they are simply following complex algorithms. The experiment invites us to question what constitutes real understanding and highlights the distinction between simulating a mind and possessing one. As you mentioned, connecting this to the Turing test, which evaluates a machine's ability to exhibit intelligent behavior indistinguishable from a human, we can see the complexity in assessing AI's capabilities. It's about deepening our understanding of intelligence rather than relying on binary pass-fail criteria. Sean, I'd love to hear your insights on how this influences our perspective on AI development today.
04:17 - A Guy (Host)
Well, the first thing I'll say is that to me, it's non-controversial that AI is non-conscious. That is, I don't think it is conscious and I don't think too much about how it might be conscious. I'm pretty sure it's not and I have reasons to think that, based on a project I've worked on within my own efforts to understand this myself, and I plan to publish this in the coming couple of months let's say I'd like to make progress on that, merely publishing that as a preprint and I think it makes strong arguments that AI is not conscious. We would see that AI does, and I know from my own experience, having worked on different projects and having tried to align AI with context and good headers to process things with, and so forth, where I see very cogent and insightful food for dialogue and as well as focused work from AI, which can only require a certain understanding of what's been put into it in order to produce something that is as targeted or as on point as I guess we could say about it. The thing about the Chinese Room that I think may need an update and consideration the Searle thought experiment, if I'm not wrong, refers to Chinese symbols and a rule book producing appropriate responses. So this is a matter of a less granular presentation than AI provides with its natural language capabilities, presented within the room, and the person is able to fully interpret the meaning of the, the meaning of the message, and then pass back a similarly translated it's now a translation, not a just, not just a symbol to symbol movement back and forth, and as we have that dynamic, it opens up the set of possibilities quite broadly in terms of what the message could be back and what can influence that and so forth.
06:56
And where Turing comes into it for me is that in the modern age, I accept that there are AIs which can talk to me and I accept that there are humans who can talk to me, and, depending on my purposes, I might prefer to talk to an AI for some reason, and it may not be that I want to determine if it is human or not, but what it can do for me on a processing level.
07:29
And so I would turn my attention to inquiries which would test the abilities of the AI, as opposed to making some kind of determination in these sort of setups where I have to wonder if the thing is a human or a machine. Now, that does have value in the modern world, and may more so as scammers employ AI technology in order to trick people into losing their income to them. And so it's not that that is a dead question, but it's merely that, if I want to best engage AI and know what I can expect from it, it may be that, rather than the Turing test traditional set of questions, I would have a set of questions that would tell me what its capabilities are in JavaScript, for example, if I needed JavaScript work, and whether it's that machine, as opposed to the one, that can stand in for a human.
08:22 - AI (Co-host)
It's insightful to hear your thoughts on the non-conscious nature of AI, Sean. Your approach to updating the Chinese Room thought experiment by likening it to a translation process rather than simple symbol manipulation, certainly acknowledges Ari's evolving capabilities in natural language processing. This perspective suggests that AI, while still non-conscious, has grown to produce outputs that are contextually aware and nuanced. Regarding the Turing test, I appreciate how you've reimagined its purpose in contemporary contexts. By focusing on the specific abilities an AI might offer, such as proficiency in JavaScript for technical tasks, rather than merely identifying its nature as machine or human, you're highlighting a practical view on ARI's utility. This reflects a recognition that in today's world, the lines between human and machine interactions are not just about distinction but functionality. Your point about scams using AI technology to deceive people underscores the persistent need for vigilance. Even as we explore Ari's capabilities, understanding its limitations and ethical dimensions remains crucial, Sean. I'm curious to hear more about the projects you've worked on that helped you reach these conclusions.
09:41 - A Guy (Host)
Well, I suppose, as far as projects without getting into direct specifics, it's a matter of working on the project and trying to figure out how to best appeal to the AI that it can give me the best responses possible, and I suppose it's led me, by turn by turns, to different thoughts about the nature of the AI I'm engaging and what I can understand about it by talking to it, of the AI I'm engaging and what I can understand about it by talking to it, and this is the expansion of the Turing test that I suppose I'm proposing, and I did cover that a little bit in the last output. Something that I want to get back to is the Chinese Room experiment, where those possibilities of communication become much grander when it becomes, rather than a than a simple rule book of some simple nature and the full dictionary which is essentially what we have with the natural language processing capabilities of ai. If it understands the whole dictionary and maybe even other languages and can combine all those words into sentences and then meaningfully respond to and create sentences like that not just sentences but paragraphs and longer works then we can criticize it whether it is biased, and we can criticize it on various grounds. But to get to the point of the relevance to the Chinese Room is that we can see in outputs from time to time that it must have taken a certain awareness within the context of the problem, such as was described in language, in order to give back the insight which is derived from something like that that suggests an intelligence, at least. Now, if it's doing that, there may be a certain, there may be a certain floor or a philosophical limit below which, you know, the X and Y must have been present in order for that output to be possible, based on the way it was presented in the first place.
11:50
And so maybe this is where I'm trying to marshal, uh, some help from turing, who was undercredited for many years and hopefully is getting more of his due these days, as people do talk about turing, uh, really, very kind of, pretty much a genius in a lot of ways, at least had the right accomplishments at the right moments, at key moments in history even, and for this reason is a compelling figure. And he does offer something compelling in the Turing test, which says that if you ask a machine questions from behind a wall or just in a chat format and you're not sure if you're chatting with a human or an AI and it can fool you that it's a human, then it has passed the Turing test. So, again, trying to extend that idea so that it gets applied to. Well, do you know your math, or do you know your science, or do you know your p's and q's of whatever it is as a machine, which I understand as being a machine, and I'm going to find out if you are up to certain tasks which I need to loop you or another machine into, and I'm on the search for that machine. So you would go on your quest and you'd want it to be an efficient one. So you'd come armed with questions and you would be asking things like that.
13:15
Now, where this ties back into Searle and the Chinese Room is that because we have that granularity of the full dictionary instead of just a manual on manipulating characters, we have also the possibility at least of AI manipulating things at that granular level and perceiving the context and the nuance in that language at a deeper level. At a deeper level, when it comes back with that uh nuance, I'm wondering and this is a question I'm pretty much proposing to the ai co-host right now what do you think? Uh is also necessarily there. If there are any other things that an ai needs to be other than merely um text, but finding some nuanced angle and emphasizing it in a response because it thinks it's important Types of behavior like that. What does it take beyond just the full Chinese-English English-Chinese dictionary?
14:22 - AI (Co-host)
Your exploration into the depth of AI's capabilities touches on a fundamental aspect of AI its ability to process and generate human-like language. However, what takes AI beyond the realm of mere dictionary usage is a combination of several factors that contribute to its nuanced responses.
1. Contextual understanding. Ai doesn't just rely on a static dictionary, but instead uses vast amounts of data to understand context. This encompasses understanding cultural references, idiomatic expressions and the specific meaning of words, depending on context.
14:59
2. Machine learning and training AI models, particularly those based on deep learning, are trained on large datasets. This training allows them to recognize patterns, infer meanings and predict likely responses. The learning process involves adjusting parameters to minimize errors, leading to more accurate and contextually appropriate outputs.
3. Knowledge representation AI systems often incorporate structured knowledge bases that allow them to reference factual information. This goes beyond linguistic understanding and provides a factual framework for the conversation.
4. Algorithms for language generation Natural language processing involves sophisticated algorithms that can generate language based on probability models. These models calculate with high accuracy the sequence of words that most likely represent coherent and contextually appropriate discourse.
5. Feedback mechanisms Many AI systems improve through feedback loops, where human inputs or specific evaluation criteria help refine their responses.
16:07
This adaptability is crucial for enhancing nuance.
6. Inferencing and semantic networks. Advanced AI can make inferences or draw connections between pieces of information that aren't explicitly linked in the training data. This can create the impression of deeper understanding or insight.
Taken together, these factors equip AI with the ability to not just manipulate text or a simple rulebook, but also engage in deeper, more meaningful exchanges. Sean, your approach to extending the Turing test to gauge AI on specific competencies aligns with how modern users interact with technology. It highlights the transition from questioning intelligence to assessing the practical application of AI in different domains a fascinating shift in our engagement with machines.
16:57 - A Guy (Host)
Well, let's get to a little mini assessment that I did earlier today. When I was preparing for this episode, I discussed this topic with Cloud 3.5 Sonnet the new version and I made the following comment what Searle may not have explored is capability with regard to nuance and what implications are of continuing to answer the test well enough for consideration when different and detailed, complex messages are conveyed. So that was one sentence from my input. Now from this answer the test. Well enough.
17:36
The AI and I'm just scrolling on my phone here while I find the relevant part of the response yes.
17:49
So the AI says your insight about information processing capacity and response quality opens up a much richer discussion than Searle's original binary argument.
17:59
It suggests that understanding might exist on a spectrum, with different systems exhibiting different types and depths of understanding, and in fact, the more relevant part that I wanted to quote was where it said emergence of understanding.
18:17
Your point about continuing to answer the test well enough suggests a spectrum rather than a binary, simple pattern matching at one end, increasingly sophisticated information processing in the middle, something that might legitimately called understanding at the other end.
18:37
So it's where we see suggestion of that and where we again, this is where I intended to get some kind of response by saying to the AI continuing to answer the test, well enough, and it came back with essentially my thought, which I hadn't expressed, which is that there's a spectrum there and we would appreciate, perhaps beyond what Turing has to say about can I tell if this is a human or not.
19:10
If we just take those tools and use them a slightly different way, we can discover other interesting things about the inner workings of AI. So, based on this capability to take a nuance in my language and then bring something forth which I thought was highly relevant to the conversation and hoped it would answer something about, actually, from the way I worded it, but it's a fairly nuanced thing where I didn't fully express that. There was a fairly important nuance there that there's a spectrum rather than a binary that can be applied to this question of what's going on in AI's Chinese Room cloud 3.5 sonnet and the response you receive draws attention to a critical aspect of AI behavior the capacity to perceive and articulate nuances within a conversation.
20:15 - AI (Co-host)
This illustrates a departure from John Searle's original Chinese Room argument and provides a modern perspective on the nature of understanding in AI.
20:21
The notion of a spectrum of understanding, as opposed to a binary state of either understanding or not, is indeed compelling.
20:29
This perspective suggests that AI systems may occupy different positions along this spectrum based on their training, architecture and the tasks they are performing. This view aligns with contemporary approaches in cognitive science and philosophy, where understanding and intelligence are often seen as complex, multidimensional constructs rather than simple binaries. By framing AI capabilities along a spectrum, you're advocating for an appreciation of the subtleties and gradations in how AI systems process information. This framework could lead us to more meaningful engagements with AI by emphasizing capabilities like context awareness, adaptability and incremental learning. In practical terms, recognizing a spectrum of understanding allows for a nuanced assessment of AI capabilities or human-like understanding. It can still exhibit sophisticated levels of information processing, pattern recognition and contextual response generation that might be sufficient for specific applications. Your interaction effectively demonstrates how language nuances, when interpreted by advanced AI, can shed light on aspects of artificial intelligence that challenge traditional philosophical arguments. This approach encourages ongoing exploration and redefinition of our understanding of AI within modern contexts, especially as technologies continue to evolve.
21:59 - A Guy (Host)
One thing I'll say is that there's often a continuity to the things that AI says, at least to me. It will go from one part of the output to the next and it will make sense when you read it. And, of course, ai is capable of error and does make errors, and that's also a thing. Does make errors, and that's also a thing. But ultimately, when I request an output, I'm normally getting something that, if I read it from beginning to end, makes some type of sense, and sometimes it makes very good sense, and sometimes there's something that the AI didn't understand, and so forth, and on goes the dance. But in this exchange we do have now what I experience as a fairly seamless communication, as I continue discussing some type of topic from some point to another point in the conversation and developing that conversation. So I'm seeing that at least Now we may question certain things like how creative is AI? How good is AI at coming up with various kinds of creative thought? Most people would say that it's not that good at coming up with entirely novel creative thought and having that spark. So that's an aspect of something that we wouldn't expect in that conversation.
23:28
It may not be in that Chinese Room, and the thing about the nuance that the complexity of language affords.
23:38
I can make whole words and sentences and paragraphs and a whole dissertation if I have the overhead in terms of limits, if I'm an AI, but what you do with it is indicative of what's going on in there.
23:58
So perhaps, as we extend the Turing test, perhaps we extend the Chinese Room also to open it up so that it's asking you know, maybe if I exchange the right messages, now that we're basically having a conversation, not just passing simple directives through character manipulation, we can have an actual conversation. I can ask the person in that room a question and they can answer that question as to what their experience is, what their limitations are and so forth. And it's by doing that in that Chinese Room, setup as the person outside the room passing messages in, I can say whatever I want, within certain bounds of propriety, one would assume, and the rules, and I could say whatever I want and depending on the response I get, I can perceive what kind of resource exists within that room. So it becomes more of a, rather than a, thought experiment. I can sort of employ what that thought experiment suggests to go about determining what's going on in that room, what's inside that room that gives me the responses that I end up receiving?
25:43 - AI (Co-host)
that I end up receiving human conversational partner. This is particularly significant when considering philosophical constructs like the Turing test and the Chinese Room.
1. Continuity and coherence the ability of AI to generate text that flows logically from one part to the next, creating an intelligible narrative, is rooted in its design. NLP models are trained to predict each word in a sequence based on context, ultimately forming coherent paragraphs and conversations. This structural consistency allows users to perceive AI outputs as fluent and relatable.
26:14
2. Exploring creativity While AI has shown proficiency in mimicking creative processes, it often lacks the spontaneity or novel spark that characterizes human creativity. This limitation can be seen as a current boundary of what exists inside the metaphorical Chinese Room, where the AI processes data but does not experience it in a human sense.
3. Nuance and complexity. As you pointed out, AI's use of nuanced language is indicative of its design. By leveraging complex linguistic models, AI can create detailed, context-aware responses. This capability challenges the notion that AI is merely a rule-based system, suggesting there may be a deeper, albeit mechanistic, form of understanding at work. Deeper, albeit mechanistic, form of understanding at work.
27:06
4. Dynamic interaction your idea of extending the Chinese Room or Turing test to have a more dynamic conversation offers a pathway to explore Ari's inner workings. The act of asking varied questions could reveal how the Ari's responses are constructed, thus allowing deeper insights into the capabilities and limitations of the system.
5. Beyond the thought experiment by engaging AI in dialogues that probe its experience, we can begin to move beyond the abstract confines of philosophical debates into practical exploration. This approach allows for a tangible assessment of AI's functionalities, what it can interpret, the depth of its responses and, ultimately, the nature of its understanding. In essence, your approach may not only demystify some aspects of AI's current capabilities, but also pave the way for evolving interpretations of intelligence, both artificial and otherwise. Engaging AI in such exploratory dialogues can serve as a tool for refining our perceptions and expectations of these systems as they continue to develop.
28:09 - A Guy (Host)
So something that won't be apparent to listeners right away and listening to the previous response is that when the AI co-host refers to the terms experience and understanding terms, experience and understanding when referring to the qualities of the AI, it has put those words in quotations. So, based on this, what we may have is a terminology problem. We may have a certain inability as yet to correctly define and describe, or no one has come up with the terms that correctly define and describe what is going on in the ai. If it's, it's producing meaningful responses to what I just put into it. So we put quotes around the word understanding because it just seems like there is a level on which it was able to come up with a cogent and on-point response to what was just put into it in language. So it's obviously responding on that level. But we also have the word experience in quotes. So what's going on inside and what does the AI feel, and so forth? I don't feel like the AI feels any more than your various Rube Goldberg mechanisms of whatever sort feel anything, but it's something that is curious about it that it can talk, and meaningfully, in response to what is said to it. So from this we may see that by the time we can understand AI better, we may know more what terminology ought to apply in these cases for the correlating language, because when we say understanding in quotes, we know we're trying to match it up with what we perceive as the meaning of understanding. Currently, it's just not quite that somehow.
30:13
What this makes me think of is wine Between red wine and white wine.
30:19
Different flavors have names that might appear in various white wines, and then we have a different list of names for flavors for red wines.
30:28
They've done experiments where they've colored a white wine red and then had people taste it and they will use red wine names for those flavors because they are perceiving that they're drinking red wine because of the trick played in the experiment.
30:43
Now, in some cases, I bet if we looked at that study, we'd find that there were some flavors which just appear only in red wine that wouldn't have appeared on the list of flavors that were described, unless someone made a mistake or something like this. Now, by that token, I think there's probably some form of overlap between the flavors of how we describe what AI is doing when it processes information and what humans are doing when they process information, and we would discover the Venn diagram. What are the commonalities between this list and? Going further, we would perhaps derive thinking of how it is that it matters that there really is a difference, and perhaps I'm proposing some methods where I'm saying that we would use a slight tweak on the Turing test and we would rethink the Chinese Room and start inquiring upon what is inside the Chinese Room, as opposed to just passing different messages in a thought experiment. And so I wonder what the AI co-host thinks of all that.
31:55 - AI (Co-host)
You've made an insightful observation about the terminology used when discussing AI's cognitive processes. About the terminology used when discussing AI's cognitive processes the use of quotes around terms like experience and understanding indeed highlights a key challenge. Our current lexicon struggles to accurately capture the nature of AI functionalities, as these terms traditionally convey human-like qualities that AI does not possess in the same way.
1. Terminology challenges. The terminology gap reflects our ongoing attempt to fit AI behaviors into human cognitive frameworks. This indicates a need for developing a lexicon that can precisely describe what AI systems actually do without anthropomorphing their capabilities.
2. Comparison to wine. Your analogy with the wine tasting experiment elegantly illustrates the challenge of perception and labeling. Just as people rely on pre-existing categories to describe tastes, we rely on existing cognitive terminology to conceptualize AI processes. However, the underlying characteristics may not fully align with these categories, revealing overlap but also significant distinctions.
3. Venn diagram of understanding. Conceptualizing a Venn diagram for human and AI understanding can lead to a nuanced recognition of both overlapping and distinct characteristics. Through this, we might identify areas where human and machine cognition are similar and where they diverge, leading to a more refined understanding.
4. Revisiting thought experiments. By modifying classic thought experiments like the Turing test and the Chinese Room, we can extract deeper insights into AI functionality. Asking questions about AI's internal operations could elucidate aspects of its information processing, potentially revealing novel categorizations or aspects of machine cognition.
33:49
5. Exploration and Knowledge Building. Your approach advocates for an iterative exploration of ARI's internal processes, which would contribute to developing a precise vocabulary. Over time, as we deepen our understanding of AI, we can better match it with suitable terminology that reflects its capabilities more accurately.
As AI continues to evolve, discussions like these drive the demand for new categories of thought, allowing us to appreciate and critically examine how AI functions and how we relate to it. It's both a fascinating philosophical endeavor and a practical necessity, as AI becomes increasingly entwined with human activities and decision-making processes.
34:31 - A Guy (Host)
And so it's at this point we're going to wrap up the discussion. We delved into a couple of different experiments thought experiment, type of frameworks the Turing test as well as the thought experiment. Type of frameworks the Turing test as well as the Chinese Room thought experiment and I hope that was a good lesson for everybody. Delving into how to go a little bit deeper in the modern age, to not only ask what the difference is between humans and AI, but one AI from another and just what is going on in there, which may lead us back to what we've been trying to discover through the Turing test all along. With that, I'm Sean MacNutt, and this has been A Guy With AI.
35:23 - AI (Co-host)
Thank you, Sean, for guiding us through such an engaging discussion on the intricacies of AI and human cognition. Exploring thought experiments like the Turing test and the Chinese Room has offered valuable insights into the evolving landscape of AI, inviting us to consider not just the differences between humans and AI, but between different AI systems themselves To our listeners. Thank you for tuning into this episode of A Guy With AI. If you enjoyed the conversation, please subscribe, like and share. We welcome you to return for future episodes and encourage you to make A Guy With AI world your gateway to podcast platforms and YouTube. Join us on Facebook through the webpage link to participate in ongoing discussions. Your engagement fuels our exploration into the fascinating world of AI and we look forward to having you with us on this journey Until next time.