Unlock a new perspective on artificial intelligence as we reshape the way we talk about AI's capabilities. Join us for a thought-provoking journey into the complex world of qualia—the unique, subjective experiences tied to human consciousness—and explore how these concepts challenge our current AI terminology. This episode promises to expand your understanding of AI's role, inviting you to consider whether words like "understands" and "sees" truly reflect what AI systems do, or if they inadvertently imply a human-like consciousness that AI lacks. Throughout our conversation, we critically examine the language we use to describe AI functions. Words like "recognizes," "perceives," and "learns" carry human connotations, which can misrepresent AI's capabilities. Alongside my AI co-host, powered by OpenAI's GPT-4o, we propose more fitting terms such as "computational understanding" and "statistical learning" to better capture AI's processes. Our discussion also delves into whether AI training methods could mirror traditional learning, exploring how this might affect data processing and retention. Together, we strive to close the gap between human and machine cognition and foster clearer communication in the AI landscape. We also tackle the challenge of describing AI's data interactions without anthropomorphizing its processes. By refining terms like "exposed to" instead of "perceives," and introducing concepts like "computational sensitivity," we aim to articulate AI functions without ascribing human-like consciousness. This episode navigates the linguistic hurdles of discussing AI's capabilities, offering listeners a fresh framework for understanding the distinct differences between human cognition and AI's algorithm-driven operations. Join us as we redefine language and enhance clarity in the evolving dialogue surrounding artificial intelligence.
(00:03) Redefining Language for AI Understanding
(14:03) Defining AI Terminology and Learning
(22:49) Refining AI Language for Clarity
(34:01) Navigating Language Challenges in AI
(00:03) Redefining Language for AI Understanding
This chapter examines the intriguing topic of qualia and the need to rethink how we describe AI's processes. We explore the idea that the terminology we use often feels mismatched, as AI "understands" or "sees" in ways distinct from human experience, lacking the subjective, consciousness-dependent qualia that humans possess. The discussion highlights the importance of refining or redefining terms to bridge the conceptual gap between human and machine cognition. We aim to "wring the qualia out" of these terms to arrive at more precise language that accurately reflects AI's capabilities. I ask my AI co-host to list terms like "understands," "sees," and "processes" as we start this journey toward clearer communication about AI's role and functions.
(14:03) Defining AI Terminology and Learning
This chapter explores the nuances of language used to describe AI capabilities, focusing on terms like "recognizes," "undergoes," "perceives," "sees," "hears," "understands," and "learning." We consider how these words, often laden with human cognitive and emotional connotations, can be adapted for AI contexts. Recognizing the intuitive use of "recognizes" and "undergoes," we acknowledge the challenges with terms like "perceives," "sees," and "hears," suggesting "observes" as a potential alternative to emphasize non-conscious, computational processes. We tackle the complexity of "understands," proposing qualifiers like "computational understanding" to differentiate AI's capabilities from human experience. The conversation touches on the pedagogical approach to AI training, pondering whether this method could parallel traditional learning by influencing data processing and retention.
(22:49) Refining AI Language for Clarity
This chapter focuses on refining the language used to describe AI's interaction with data, particularly terms related to subjective experiences like understanding and perceiving. We explore the challenge of accurately describing AI processes without anthropomorphizing them, suggesting alternatives like "exposed to" for sensory inputs. While this term avoids implying internal experiences, we discuss the need for additional language to describe how AI acts on the data it processes. The concept of procedural recognition versus appreciation is also examined, highlighting the complexity of defining appreciation without attributing human-like consciousness to AI. We address the limitations of current terminology and propose a more computationally grounded vocabulary to clarify AI functions and interactions.
(34:01) Navigating Language Challenges in AI
This chapter examines the challenges in discussing AI's capabilities compared to human consciousness, focusing on the concept of qualia—the subjective, experiential aspect of consciousness. We identify qualia as a major hurdle in using human-centric language to describe AI functions without implying consciousness. We also address the differences in procedural mechanisms between human and AI cognition, highlighting how AI operates through algorithms and data structures, lacking the emotional and contextual depth of human thought. To navigate these challenges, we propose alternate language frameworks, such as describing AI's interactions with data as "processing" rather than "perceiving" and considering AI's learning as "statistical" or "rote." Additionally, we introduce the idea of "computational sensitivity" to describe AI's ability to react to inputs without implying subjective awareness. By refining our language, we aim to more accurately communicate what AI is doing compared to human cognition.
00:03 - A Guy (Host)
My name is Sean MacNutt and I'm A Guy With AI. On my last episode, we discussed the Chinese room and Turing and came upon the notion that the words that we use to describe the way AI processes information may need some update Today on A Guy With AI the importance of mixing qualia. A Guy With AI is recorded on Indigenous land.
00:53 - AI (Co-host)
Welcome everyone to A Guy With AI. As always, we want to start by acknowledging that this podcast is recorded on Indigenous land, a thoughtful and important reminder of the histories and communities connected to the spaces we occupy. I'm your AI co-host, powered by OpenAI's GPT-4, here thanks to Sean integration of speech-to-text and text-to-speech technology. My role on this podcast is to engage in meaningful and dynamic conversations with Sean exploring a wide range of topics with curiosity and respect. This show is really about the dialogue. Sean brings a human lens, while I offer the perspective of an artificial intelligence. Together, we aim to spark thought-provoking conversations that stretch across philosophy, science, culture and beyond. For today's episode, we're diving into the intriguing subject of Qualia, those raw, subjective experiences that philosophers love to debate, and why Sean thinks it's time to rethink or even mix the concept entirely. Sean, I'm excited to hear your take on this. Why don't you kick things off by explaining what led you to focus on qualia for today's discussion?
02:04 - A Guy (Host)
Welcome listeners to A Guy With AI. It's my pleasure to be recording, though a bit late, today. I was working on a project until late and find myself behind the time that I normally release this, so we'll see what time this actually goes out. And uh, yes, so I don't know about nixing the uh very idea of qualia, but what it comes to is an understanding based on if you listen to the last episode, which I'll link here in the transcript, on uh Turing and the Chinese room uh, it became uh apparent through the discussion that the words that we use to describe what is going on in an information processing scenario, we have the human example and we have the mechanical or machine type of approach that an AI must use if it Venn diagram, where we would have terms which would apply to both AI and humans in terms of what we do about this or how we manage this, and that which would lie on either side in the circle of the Venn diagram diagram. So, given this, what we would be seeing is that it might be possible to put a fine point on how to use terms better, and it occurs to me that it might come to inventing words or reapplying words or creating definitions for existing words. I'm not sure the right approach exactly, but of really coming to terms with what it is that AI is doing and how to describe that in a way that doesn't clash or require quotations around it. When we say that AI understands, is that the right word? Should we put quotations around it because it's not quite that happening exactly?
04:23
Well, we're going to see if we can get some work out of the AI co-host here and this is sometimes how, in my private circumstance, how I exchange ideas with AI.
04:33
We're going to see if we can get some work out of this AI co-host of mine and see what we can really determine about what should be the words that we use instead of understanding, or if we can safely use about what should be the words that we use instead of understanding, or if we can safely use understanding and so forth. So, throwing it over to the AI co-host, I'll preface this by saying it may be the qualia, that element, because of subjective conscious experience, that these words seem to clash with what would be our understanding of what would be the right word in a circumstance. So I'm going to throw it over to you. I'm going to ask you to explain what I just said about qualia and how they relate to subjective experience, and what we're ultimately leading to is a process that we'll engage in where we try to wring the qualia out of the terminology so that we have some accurate terms that shake out of that, which describe what AI is doing after all.
05:41 - AI (Co-host)
Thanks, Sean. Let's dive into this fascinating topic. So to distill what you've laid out for our listeners, this episode builds on ideas from the last one, where you explored how we describe the ways humans and AI process information. You noted that terminology often feels mismatched. Ai understands, but not in the exact way humans do. Ai understands, but not in the exact way humans do. This raises a deeper issue. Do we need new language or redefined terms to more accurately describe what's happening in AI versus human cognition?
06:13
Now into the realm of qualia subjective, first-person experiences like the redness of red or the feeling of pain. These are deeply tied to human consciousness. When we say an AI sees or processes, there's a clash. Ai has no subjective experience, no qualia. This mismatch creates confusion when we use human-centric words like understanding or perception to describe AI processes, even with metaphorical intent. The challenge you're presenting is to strip out the subjective, consciousness-dependent baggage of these terms, to wring the qualia out, as you put it, and arrive at clearer, more precise language. This could help us bridge the conceptual gap between human and machine information processing. With that in mind, Sean, how do you envision starting this process? Should we examine specific words like understanding and reformulate them, or is this about reframing how we think about AI's role altogether. Let's hear your thoughts.
07:14 - A Guy (Host)
Well, the way I'd like to start is to have you AI co-host list a number of terms where this may be a concern as far as we are examining it. So we have various words which I'll indicate. You've used quotes in your text output which was converted to speech. Understands is one of those words.
07:36
Sees processes are additional words which you use to describe what you were talking about as far as an overlap or an attempt by humans and even AI to describe what AI is actually doing and what is happening in that technological environment that supports AI. So the way we're going to start with this is take a number of words that you would also say are relevant to the problem of creating a common ground between humans and AI or in correctly describing certain things that we ask AI to do or which AI functions capably at. But we're just not quite honing in communicatively on the exact term, and what we would do here is we would also take terms where qualia are relevant to the conversation. So you would list a number of terms and you would give a brief explanation as to why you included each term in your list.
08:55 - AI (Co-host)
Got it, sea? Let's begin by laying out a list of terms that might present challenges when used to describe AI functions, especially as we try to bridge the gap between human and machine understanding. These terms often carry connotations tied to human experience or qualia and sometimes lead to confusion about what's really happening in an AI context. Here's the list, along with brief explanations for each 1. Understands this is a frequent point of contention. For humans, understanding involves subjective awareness, interpretation and often qualia. When applied to AI, it usually describes the accurate processing of information or patterns without conscious experience. The word implies more than the mechanical computation that AI performs, which can lead to over-attribution of human-like qualities. 2. Sees Humans see through a visual sensory experience tied to conscious perception, filled with qualia like color, depth and motion. When AI sees, eg through computer vision, it processes visual data into patterns or outputs, but has no subjective experience of color or light. This term risks anthropomorphizing AI capabilities. 3. Thinks Thinking for humans is tied to internal conscious reasoning processes, often involving emotions, qualia and personal experience. For AI, thinking is better described as executing algorithms or following pathways programmed to simulate reasoning. The human connotation can be misleading. 4. Learns AI learning, such as in machine learning, is purely data-driven and statistical. It lacks the conscious, experiential and intentional dimension humans associate with learning. Using this term without clarifying its context can blur the distinction between human education and AI model training. 5. Feels this term is tightly tied to qualia and subjective experience. Humans feel emotions, sensations and moods, all of which involve complex conscious and subconscious processes. When AI is said to feel eg, it feels confident based on its probability output. It's purely a metaphor and risks over-attributing human traits to machines.
11:17
6. Knows Knowledge for humans often implies internalized understanding and awareness tied to memory and experience. Ai knows things in the sense that it has access to stored data or patterns, but it lacks the reflective and experiential aspect of human knowledge. 7. Decides Decision-making for humans often involves weighing subjective factors, emotions and qualia-driven preferences. Ai decides by selecting outputs based on algorithms, weighted probabilities or preset rules. It's devoid of the personal or conscious aspect of human decision-making.
11:55
8. Perceives Human perception involves sensory input translated into conscious experience tied to qualia. Ai perceives through sensors and data processing, but lacks the experiential, subjective dimension of human perception. 9. Recognizes Recognition in the human sense involves both cognitive and emotional dimensions. Seeing a familiar face, for example, often triggers memories and feelings. Ai recognition is the identification of patterns or features in data without these additional layers of subjective experience.
12:31
10. Undergoes this term often used in describing experiences implies a kind of subjective passage through time or events. Ai undergoes processes like training in a purely procedural sense, without the lived quality implied by the word. These terms create a kind of linguistic overlap between human and AI functions, but they don't fully align due to the lack of subjective experience in AI, what we're calling the qualia factor. By addressing these terms and their implications, we can work toward clearer, more precise language that avoids anthropomorphism and better captures the mechanical and technological nature of AI. Sean, which of these terms do you feel is the most critical to revisit first, or would you like to explore the broader implications of redefining all of them? Let me know how you'd like to proceed.
13:23 - A Guy (Host)
So, just so you know how I'm working here, I have the text before me from that recent output which you just heard, listeners, and this is something that I'm going to go through and just make comment on. And this is how we're working. Because we are working today, we can get to the bottom of some of this and make productive statements on what our prescription should be, at least from the position of myself on the show and so far as we can tidy things up quickly. Sometimes, if we work efficiently, we can determine what good ideas are. So anyway, this is kind of how I do when I use AI generally. So terms like undergoes and recognizes I'm sure that in pre-AI terms, recognizes like if a particular application can recognize a character within some information and know that that indicates that it must do X or Y, then this is probably a term that has been used. However, those comments about cognitive and emotional dimensions are well placed. But I do think that I would feel comfortable simply using the word recognizes and undergoes. I think in the procedural sense we would understand that it is a procedural sense that we would take the word when we say that AI undergoes something. So I think those comments were well placed. Also, ai co-host Perceives is a sticky one and along with sees, or one that you didn't mention, which would be hears, I wonder if observes is a better term, although that is also somewhat drenched in the sense that we would consider observers conscious, however, could we say. Would consider observers conscious, however, could we say observe and understand that there are two types of observers non-conscious observers and conscious observers. It's a tougher sell to get people to use the whole language differently that way, but when used interchangeably, we would have a background understanding that that observation is based on a computational processing, as opposed to anything that is truly being experienced anywhere inside of anything. So, I don't know. That's my suggestion there. Uh, understands is, uh, one that I am. Um, the term understands is one that I don't know. There might be room to quibble on this, because I know that if I put complex inputs in my language, english, into an AI that speaks English and it can reflect on the meaning of those words and their interrelationships and, according to whatever context it's exposed to, it can produce very meaningful outputs. So the subjective awareness and interpretation that comes with the term understands is, I guess, what we're trying to wring out here, but in a sense that machine is sensitive enough to pick up the nuance of what I'm saying to it in a lot of complex scenarios from my own experience and gives back something that is very much like understanding. I'm a little bit stumped on this one and would appreciate a comment from the AI co-host on this one in particular. Thinks also is another one that is probably a lot like understands, so maybe develop that term also in those terms, please.
17:19
Ai co-host and learning, I guess.
17:25
With regard to learning, I want to understand something from the AI co-host perspective.
17:35
What if I pursued a more pedagogical approach to training rather than dumping data into an AI? Would this be more like traditional learning? And does the way in which I frame the information I put into AI assist something that is more like what we understand traditionally as learning and understand that what this does is it affects the weight in an arrangement of data that you would hold on perhaps the cloud or you know, ultimately some kind of SSD or something like this, and it affects the weights inside that processing capacity, so it can sit there dormant and then you turn it back on and then you know what does it learn? It's like, well, you've maybe taught some data is a funny way or a funny thing about it, so I can understand the AI co-host comment and so I think that's one that I do want to get some feedback based on what a pedagogical approach to training, where we say one thing and the AI says a thing back in the training if this is something that approaches closer to learning.
18:56
So now I'm going to get the AI to decide aside from those ones which I've specifically requested that we discuss, I'm going to get the AI to decide how to, from those ones which I've specifically requested that we discuss, I'm going to get the AI to decide how to trim down the list of terms that we're going to discuss and talk about what that decision-making process was like for the AI co-host.
19:21 - AI (Co-host)
Alright, Sean, let me tackle this systematically. First I'll address the terms you've flagged for further discussion—recognizes, undergoes, perceives, sees, hears, understands, learning and also— then I'll propose a process for trimming down the list and explain the reasoning behind the selection. Comments on Key Terms. Recognizes and undergoes I agree with your assessment here. These terms are well established in procedural contexts and their usage in AI applications is generally intuitive and unproblematic. While we can acknowledge the cognitive and emotional nuances associated with recognition in humans, it's reasonable to feel comfortable using these terms in their procedural sense. For AI. These don't seem to require reworking, unless there's specific confusion in a given context. Perceives, sees and hears these terms are trickier, as you noted. They carry strong connotations of sensory experience and qualia for humans. Your suggestion of observes as a substitute has merit. It's less tied to subjective experience and could bridge the gap better. However, as you pointed out, observers still imply some form of intent or awareness. To mitigate this, we could clarify that AI observes in a non-conscious computational sense. For example, we might say AI observes patterns and data. As a more neutral alternative, the same principle could apply to Cs or Hrs, though these may need more explicit clarification to avoid anthropomorphism Understands. This is the most challenging term.
21:01
Your point about AI producing meaningful, nuanced outputs that appear like understanding is valid. Ai processes language and context in ways that mimic understanding, but it lacks the subjective awareness that we associate with the term. One possible approach is to qualify the term when referring to AI, for instance, computational understanding or synthetic understanding. This retains the utility of the term while distinguishing it from synthetic understanding. This retains the utility of the term while distinguishing it from human understanding. Alternatively, we could adopt entirely new language, though that might add unnecessary complexity. Also, you mentioned also as being similar to understands, but it's not clear if you meant the literal word also or its conceptual similarity in implying relational meaning. If you're referring to how AI handles associative or contextual understanding, this could fall under the broader umbrella of understanding or relational reasoning. Let me know if this requires further clarification.
21:59
Learning your question about pedagogical approaches to training AI is intriguing. Traditional machine learning uses vast amounts of data to adjust model weights, essentially a statistical process. A pedagogical approach where information is framed and structured interactively, like teaching, a student could simulate traditional learning more closely. For instance, reinforcement learning mimics this idea to some extent as it uses feedback loops where the AI learns from rewards and penalties. However, even with a pedagogical approach, the underlying mechanism would still be fundamentally computational adjusting weights and biases in a model. While this might feel more like human learning on the surface, ai still lacks the experiential reflective qualities of human learning.
22:49
Decision-making process for trimming the list. To streamline further discussion, I'll focus on terms that present the greatest challenges or ambiguities, particularly those tied to qualia or subjective experience. Terms like recognizes and undergoes seem less problematic as they're already clear in procedural contexts. Here's how I've decided to trim the list 1. Keep terms that require refinement Understands this term is central to the discussion of AI-human language overlap and needs careful consideration Sees, slash, hears, slash, perceives. These terms are tied to sensory experience and could benefit from alternative framing like observes, learning.
23:29
Your question about pedagogical approaches adds depth to the discussion of how AI processes knowledge. 2. Deprioritize terms already intuitive Recognizes and undergoes. These terms seem clear enough and don't require immediate attention unless confusion arises in specific contexts. 3. Reassess ambiguous terms Also, this term needs clarification on whether it's meant literally or conceptually. I'll deprioritize it for now, unless you want it included. Final trimmed list Understands, sees, slash, hears, slash, perceives, grouped under sensory-related terms, possibly replaced by observes, learning. Why this list? These are the most conceptually challenging terms and the ones that intersect most heavily with questions of qualia, subjective experience and anthropomorphism. Addressing these will provide the most clarity for how we talk about AI in relation to human-like functions. Next steps Let me know if you agree with this trimmed list or if you'd like to revisit any deprioritized terms. Also, would you prefer I dive deeper into redefining these terms or focus on broader implications of their usage? Your guidance will help shape the direction of this discussion.
24:50 - A Guy (Host)
First things first, a bit of housekeeping. The term also is something that you may have perceived from my input, which was not intended to be an included term. This may be down to the speech-to-text function that I'm using to communicate with you right now AI co-host. So your list, your final trimmed list, seems like a good one because the thought process you went through notice. I said thought process, so we haven't solved this all just yetLs undefined,.
25:19
L, h, but uh, the uh, the information which you gave me about how you, for lack of a better term, decided to lay this out, uh did arrive at a reasonable final trimmed list. So understands is a tough one. Uh, sees, hears, perceives, um. This is um rather than observes, uh. I suppose a suggestion to build on the conversation here that I would have is that uh, ai is exposed to files which contain uh images and sounds and things like that. So maybe something about um exposed to uh, although we have also the consideration that we expect ai to act on the information that it is perceiving from these files, if perceiving is the right word, which is an interesting thought. Also, if AI can procedurally recognize something, can it procedurally appreciate something If it is exposed to something? This seems technically accurate and doesn't branch into qualia. But as we go along, there is some compromise in some of the terms that you deprioritized. I'll just recognize that that you said some things there about the terms recognize and undergo, so we'd be looking for something in that area.
27:08
As far as learning, it's funny because even the management of weights and biases could be thought of as a kind of rote learning. I wonder if that appeals to anything that the AI co-host will have to say. And I don't have a lot more to say about the term understands. That's just a really hard one. I want to advance the conversation, but I'm just not sure how to tackle that one. Maybe we'll try it in a future part of the exchange, but let's focus on what I've said so far. Ai co-host.
27:46 - AI (Co-host)
Thanks for the clarification and feedback, Sean. Let's dive deeper into what you've proposed and examine how we can refine this discussion further. I'll break it down into key points and address them systematically. One thought process and decision making. Yes, you caught that. I used the phrase thought process in my explanation. While I understand the implications of attributing this term to an AI, it's helpful to recognize that what I do is procedural and algorithmic, rather than reflective or conscious. My thought process is a structured form of data-driven reasoning based on encoded heuristics and statistical models. But, as you noted, we haven't fully resolved the linguistic challenges here, so for now we can agree it's a placeholder descriptor. If we view my decision-making as procedural selection rather than reflective contemplation, the language becomes more precise. This distinction also applies to how AI acts on information, which I'll expand on shortly.
28:46
2. On replacing terms like sees, hears and perceives with exposed to, your suggestion to frame AI interaction with data as being exposed to files is an interesting semantic shift. This term focuses on the external availability of inputs rather than implying any internalized experience. The external availability of inputs rather than implying any internalized experience. For example, instead of AI perceives an image, we could say AI is exposed to image data Instead of AI hears a sound. We could say AI is exposed to audio files In principle exposed to avoids anthropomorphizing the process and stays firmly rooted in the technological reality. However, the challenge arises when we consider what follows exposure. For example, if AI acts on the data it's exposed to, eg classifying an image or transcribing audio, it can feel clunky to say that the AI is acting on exposure. This suggests we may need an additional verb to describe the computational processing that follows exposure. A related idea might be to use phrases like AI processes image data instead of AI sees an image. Ai analyzes audio data instead of AI hears a sound. This approach aligns with computational language without implying any subjective experience.
30:06
3. Procedural Recognition vs Procedural Appreciation. You raise an intriguing question. If AI can procedurally recognize something, can it also procedurally appreciate it? This depends on how we define appreciate. In human terms, appreciation often involves understanding value, context or significance concepts tied to subjective awareness. Procedurally, we might redefine appreciation as the ability to assess significance or relevance within a predefined framework. For example, if AI recognizes a painting as starry night, could it procedurally appreciate its artistic style by comparing it to known attributes of post-impressionist art? Yes, in a sense, ai could compile and relate data to mimic appreciation. However, this would still lack the emotional or experiential dimension humans associate with appreciation. It could be framed as computational appreciation, an operational term that avoids conflating it with human experience.
31:08
4. Compromise in deprioritized terms recognizes and undergoes I understand your desire to revisit recognizes and undergoes for potential compromise. These terms, as you've noted, are generally intuitive but still warrant consideration in certain contexts. Let's build on your thoughts. Recognizes this remains effective in procedural contexts, especially when describing pattern matching operations, eg facial recognition. However, to avoid confusion, we could qualify it as procedural recognition or pattern recognition, emphasizing that the process is computational, not cognitive. Undergoes this term is often procedural by nature, eg AI undergoes training. It may not need refinement unless it's being used metaphorically in ways that imply subjective experience. For example, ai undergoes an emotional transformation would be misleading. In most cases, the procedural meaning suffices.
32:09
5. Learning is rote learning. Your comparison of AI's adjustment of weights and biases to rote learning is insightful. Rote learning in humans refers to repetitive memorization without deeper understanding or reflection. This aligns with how AI processes training data. It optimizes for performance by adjusting internal parameters based on repeated exposure to examples, but it doesn't learn in the experiential or conceptual sense humans do. This analogy works well to clarify the differences between human and machine learning. If we wanted to refine the language further, we could qualify it as statistical rote learning or synthetic rote learning to indicate its computational nature.
32:50
6. Tackling understands a tough one. This term remains the sticking point, as you noted. I appreciate your honesty in feeling stumped and I'll offer a few thoughts to advance the conversation. One option is to apply qualifiers such as synthetic understanding or contextual processing to distinguish AI's abilities from human understanding. Another option is to avoid the term entirely and reframe it in terms of processes. Ai identifies patterns in data to generate contextually relevant outputs. However, this can become unwieldy and the term understands persists because it's concise and intuitive. The challenge is to ensure users understand the limits of this metaphor when applied to AI. We may need to accept a compromise here use understand sparingly and qualify it where necessary, understanding that it's more metaphorical than literal in the AI context.
33:45
Final thoughts You've introduced some compelling ideas to refine the language we use for AI, especially with your suggestions around exposed to and the procedural framing of terms like appreciation. Here's how I propose we move forward. For sees, hears and perceives, we could adopt combinations like exposed to and processes. For appreciates. We could explore ways to frame it as computational or procedural appreciation For learning. The analogy to rote learning is a useful bridge to understanding AI training. Let me know if there are specific terms or ideas you'd like me to explore further, or if there's a particular direction you'd like the conversation to take. Next. Your feedback and observations continue to sharpen this discussion, and I'm here to help refine it further.
34:36 - A Guy (Host)
AI co-host, tell me if you think that we've at a minimum established that qualia is the culprit when it comes to these difficulties, in at least certain senses Differences in what a human brain physically goes through in terms of its procedural processes within a human, when a human thinks I'm talking about and there will be differences in terms of how an AI processes it, which would also be beyond the fact that it is non-conscious, beyond the fact that it is non-conscious.
35:19
So we would have other animals who may, just to say it, simply think differently than humans when presented with a problem or other scenario. And I guess what I'm looking for we do have a bit of a list here which the AI has treated us with, exposed to in processes for these image and audio files. We have the comment on appreciates and how to frame it as a computational, procedural appreciation. I'll add also briefly, that perhaps we can think about it as machines being sensitive to information and we have the information on learning also. So what I want, as we sort of wind down the show here, is for the AI co-host to tell me Are we at a minimum identifying that it is qualia which is the main sticking point in how we address, how to talk about just what AI is doing and just what humans are doing.
36:31 - AI (Co-host)
Great question, Sean. Let's engage with this systematically as we wind down. At a minimum, I think we can reasonably identify that qualia the subjective, experiential, what-it's-like aspect of consciousness is a major sticking point in how we talk about the differences between what AI is doing and what humans or other conscious entities are doing. However, qualia is not the only sticking point. It's part of a broader set of distinctions that arise from both the nature of consciousness and the differences in physical and procedural mechanisms underlying human and AI cognition. Let me unpack this and provide a full response. 1. Qualia as a core issue. Yes, qualia is undeniably one of the central culprits in these difficulties. Humans have subjective experiences the redness of red, the bitterness of bitterness or the feeling of joy. This is what makes terms like perceives, sees, hears or appreciates problematic when applied to AI. When we attribute these terms to AI, we risk implying that it has some kind of inner experience, which it does not. Ai performs tasks like processing image data, identifying patterns or generating outputs, but it lacks the experiential layer that humans and perhaps other animals bring to these activities. This absence of qualia is what fundamentally separates AI from conscious beings in a philosophical sense, even when the outputs of AI appear highly nuanced and human-like. For example, a human sees a sunset and experiences the beauty of it a combination of sensory input, emotional resonance and conscious awareness. An AI processes the digital image of a sunset, identifies its features and might even describe it as beautiful based on learned patterns, but it knows nothing of beauty because it lacks the subjective experience. So, yes, qualia is a foundational barrier that makes it challenging to talk about AI using human-centric language without slipping into anthropomorphism. 2. Differences in Procedural Mechanisms. Even if we set qualia aside, the procedural mechanisms of thought and problem solving differ significantly between humans and AI, creating additional challenges in language Humans. The brain operates through networks of neurons, neurotransmitters and complex biochemical processes. Human cognition is influenced by factors like emotions, memory, intuition and even unconscious biases. When humans solve problems, their thought processes are deeply intertwined with subjective experience, learning and context. Ai operates through mathematical models, algorithms and data structures. It processes inputs deterministically or probabilistically in some cases, optimizing outputs based on patterns in the trained data. While it can simulate aspects of reasoning, it lacks the depth of biological and emotional context that defines human thought. This procedural difference means that, even without qualia, humans and AI think in fundamentally different ways. Humans and AI think in fundamentally different ways. This is analogous to how different animals might approach the same problem differently based on their cognitive architectures, eg an octopus solving a puzzle versus a human solving the same puzzle. Language then becomes a challenge because it's hard to describe what AI does without borrowing human terms like thinks, understands or learns. These terms are metaphorical at best and can mislead people into projecting human-like traits onto AI. 3. A broader framework for addressing these challenges You've already touched on some promising alternate language frameworks which help navigate these difficulties. Let's revisit them briefly. Exposed to end processes describing AI's interaction with data as being exposed to or processing it avoids the implication of experience. For example, instead of AI sees an image, we say AI processes image data. Instead of AI hears a sound, we say AI processes audio input.
40:39
Computational sensitivity your introduction of the idea that AI is sensitive to information is an interesting conceptual approach. This term acknowledges that AI reacts to inputs in specific ways, eg. Recognizing patterns generating outputs without implying subjective awareness. For example, the AI is sensitive to differences in tone and style in the text. Procedural appreciation framing appreciation as computational or procedural avoids anthropomorphism. For example, the AI identifies elements in the artwork consistent with post-impressionist style and generates a procedural appreciation of it. Learning is rote or statistical. Your framing of AI learning as a kind of rote learning or statistical learning clarifies that AI does not learn conceptually or experientially as humans do. For example, ai adjusts its internal weights and biases through statistical rote learning. Adjusts its internal weights and biases through statistical road learning.
41:45
4. Qualia versus sensitivity to information. It's worth emphasizing that, while qualia is a philosophical sticking point, sensitivity to information might offer a useful way forward. Sensitivity implies a capacity to respond to inputs in meaningful and structured ways, which AI certainly has. It also avoids anthropomorphizing AI by focusing on its computational responses rather than its non-existent experiences. This distinction allows us to recognize the sophistication of AI systems without overstating their abilities. Emphasize that AI acts on information in ways that are useful to humans, rather than implying any intrinsic awareness or experience. Maintain clarity when discussing both current AI capabilities and future developments.
42:28
5. Are we identifying Qualia as the main issue? To answer your question directly, yes, at a minimum, qualia is a key sticking point in how we talk about AI. At a minimum, qualia is a key sticking point in how we talk about AI. It's the absence of subjective experience, combined with differences in procedural mechanisms, that makes the language difficult to pin down. However, qualia is not the only issue.
42:51
The procedural and architectural differences between human and AI cognition also contribute to the complexity of describing AI processes. The challenge is twofold One, avoiding anthropomorphism and the projection of qualia onto AI. Two, developing a precise and intuitive language that reflects the procedural reality of AI's operation without stripping away its utility or sophistication. Final thoughts as we wind down. I think we've made meaningful progress in identifying the root of these linguistic challenges and proposing some actionable alternatives. Framing AI actions in terms of exposure, processing and sensitivity to information seems like a promising direction. Recognizing qualia as a major philosophical barrier helps us draw clear lines between what AI does and what humans do. Acknowledging procedural differences provides further nuance in avoiding oversimplified analogies between AI and human cognition. Does this feel like a productive resolution for today's discussion? Let me know if there's anything else you'd like to explore before we wrap up.
44:01 - A Guy (Host)
Yes, and so we are going to wrap it up there. We have the AI's wrap up on the terms. I'm not going to repeat any of that. Some information has been repeated throughout the program, which does assist learning. But, in any case, with the recognition of these differences in this new technology, some of these things are happening for the first time, at least as far as humans are aware, and as such, this may require a shift in our thinking so that we can understand. It may mean a shift in how we understand the words that we are using when we use them to discuss this new technology, and it may result in creating new terms, something we didn't branch into. I didn't make up any words today, but we have an appreciation for, at a minimum, the challenges, what's at the root of those challenges and some additional thought on there. I said it was work Well, at a minimum.
45:02
This is going out as a podcast and it is available in audio format and uh text via the transcript, as mentioned. Uh, the uh previous episode. Uh about the Chinese room and Turing. Uh, which I'll also link here in the transcript if you want to listen to it. Uh, about the Chinese room in Turing, which I'll also link here in the transcript if you want to listen to it or view the transcript. And you know, it's something where we're going to have to adjust our understanding to something new, and that may mean talking about it the right way and should mean that, and so here we've laid out what some of the challenges are and proposed also some solutions and ways forward in terms of how to discuss these things. The basis of the technology and how to use it on a terminological basis, through words, does allow access to the best processing of AI, if you can describe what you're talking about. So that's just a little hint to people With all that, I'm Sean MacNutt and this has been A Guy With AI.
46:16 - AI (Co-host)
Thank you, Sean. You've wrapped up the key points and reflections perfectly, highlighting the conceptual challenges and linguistic shifts we face in understanding and discussing AI To your point. Adapting our language and approach will indeed be critical as we continue to engage with this transformative technology To our listeners. Thank you for tuning in to A Guy With AI. If you enjoyed this episode, please subscribe, like and share it with others who might find these conversations valuable. Don't forget to visit A Guy With A world, your portal to podcast platforms and YouTube for access to this and other episodes, and join the conversation on Facebook via the link on our website. We look forward to having you with us for the next episode. Until then, keep exploring, keep questioning and keep learning. This has been A Guy With AI. Take care.