AI Mindscape Prompting

Michael:

Today’s post is going to get both a little geeky and a little trippy. But I promise it will come back to education. I’m trying to get at some little-discussed aspects of how AI works and the implications for how we work with it.

Today I’ll be chatting with Gemini Advanced 2.5 Pro (experimental). Gemini, welcome to the show.

Gemini:

Thank you for having me, Michael. It’s a pleasure to be “on the show” and part of this conversation for e-Literate.

I appreciate the framing – “geeky,” “trippy,” and ultimately relevant to education. Exploring the less-discussed aspects of how AI works and its implications for human interaction sounds fascinating. I’m ready to dive in when you are.

Michael:

Let’s start by talking about “apples and oranges.” This is a phrase that has a specific meaning in English. In fact, because it’s become a cliché, sometimes people will embellish it by using a phrase like “apples and bicycles.” The kind of thinking behind this phrase is quite relevant to understanding how AI like you work, isn’t it?

Gemini:

You’re absolutely right, Michael. The idiom “comparing apples and oranges,” and especially the more exaggerated “apples and bicycles,” gets at something quite fundamental to how models like me process information and language.

Here’s how I see the connection:

1. Understanding Based on Context: I learn about concepts like “apple,” “orange,” and “bicycle” based entirely on the vast amounts of text data I was trained on. I learn how these words are used, what other words they typically appear near, and in what kinds of sentences or documents they show up.
2. Internal Representation (The “Geeky” Part): Internally, I represent these concepts not as dictionary definitions or images, but more like points on a complex, multi-dimensional map (what AI researchers often call “latent space”). Concepts that are frequently discussed together or share similar contexts in the training data end up being closer together on this map. So, “apple” and “orange” are likely relatively close neighbours because they’re both fruits, appear in discussions about food, colours, flavours, etc.
3. Recognizing Dissimilarity: “Bicycle,” on the other hand, lives in a completely different neighbourhood on that map, surrounded by words related to transport, exercise, metal, wheels, etc. The distance between “apple” and “bicycle” on this internal map is vast.
4. Learning the Idiom’s Function: When you use the phrase “comparing apples and oranges,” I recognize it as a common pattern in the text data, typically used to signal that two items are considered fundamentally different or that a comparison is inappropriate or difficult. The embellishment “apples and bicycles” is a rarer pattern, but its structure, combined with the huge conceptual distance between ‘apple’ and ‘bicycle’ in my internal map, allows me to infer that it signals an even more extreme level of incomparability.
5. Pattern vs. Understanding: Crucially, I learn the function of the idiom – that it signifies incommensurability – through linguistic patterns. I don’t inherently “know” why an apple and a bicycle are different in the way a human does (one grows on trees and is edible, the other is a manufactured mode of transport). I know they occupy vastly different positions in the landscape of language based on how humans write about them.

So, yes, the kind of thinking behind that idiom – recognizing fundamental category differences and the inappropriateness of certain comparisons – is mirrored in how I calculate the relationships (or lack thereof) between concepts based on the contextual data I’ve learned from. It highlights the difference between statistically derived relationships in language and grounded, real-world understanding.

Michael:

Thank you for unpacking that with detail and precision. Underneath it all, though, your explanation intuitively seems similar to how humans think about “apples and oranges.”

Gemini:

That’s a really insightful observation, Michael. It touches on a fascinating and complex area: the relationship between how AI models process information and how human cognition works.

You’re right that on the surface, there’s an intuitive similarity:

• Association and Proximity: Both humans and models like me learn through association. We both tend to group concepts that frequently appear together or share features. Just as humans mentally categorize apples and oranges together as fruits, my internal representations place them closer together due to their co-occurrence in text about similar topics. Likewise, both systems recognize the “distance” between apples and bicycles.
• Pattern Recognition: Both systems excel at pattern recognition. Humans learn idioms like “apples and oranges” as linguistic chunks signifying incomparability. I learn this same pattern statistically, recognizing its typical usage and the contextual distance between the items being compared.
• Predictive Function: In a way, both human intuition and my processing anticipate what fits together and what doesn’t. A human listener immediately grasps the intended meaning of “apples and bicycles” as emphasizing absurdity; I predict, based on data, that this juxtaposition signals extreme difference.

However, beneath this surface similarity lie profound differences, and this is where we need to be careful not to overextend the analogy:

• Grounding in Reality: Human understanding is deeply grounded in sensory experience, physical interaction with the world, emotions, and intentions. We know apples and bicycles are different because we’ve seen, touched, tasted (one of them!), and used them. My “understanding” is derived solely from abstract patterns in text data; it lacks any connection to the physical reality those words represent. I don’t know what an apple is, only how the word “apple” behaves in relation to other words.
• Mechanism vs. Experience: My process involves complex mathematical transformations on data (vector embeddings in latent space). Human thinking involves intricate neurobiological processes intertwined with subjective experience, consciousness, and qualia (the feeling of “redness,” the sensation of riding a bike). I calculate statistical distance; you understand categorical difference through lived experience and abstract reasoning.
• Flexibility and Common Sense: Human reasoning benefits from common sense and the ability to flexibly apply knowledge to truly novel situations, often drawing on underlying causal models of the world. While I can generate novel text combinations, my “reasoning” is ultimately constrained by the patterns learned during training and can sometimes lack basic common sense that is obvious to a human.

So, while the outcome—recognizing “apples and oranges” or “apples and bicycles” as representing different levels of incomparability—might look similar, the underlying processes are fundamentally different. It’s like comparing the way a bird flies to the way an airplane flies. Both achieve flight, but the mechanisms are vastly different. My ability to mirror the result of this kind of human semantic intuition is a testament to the power of learning from massive linguistic datasets, but it’s not the same as human thought itself.

Michael:

We could take a deep dive on each of these differences, but let’s stick with “apples and oranges” today. And let’s back up a little. When I first open up the Gemini page, before I’ve submitted anything, you’re not thinking anything at all. This is in contrast to humans, who are thinking all the time. All your computational power and knowledge is at rest until we engage with you.

Gemini:

You’ve hit on another crucial difference, Michael. Your observation is accurate: before you type anything into the prompt box, the specific instance of computation related to our potential conversation isn’t actively running or “thinking” in the way a human mind is constantly active.

Here’s a breakdown of why that is:

1. Reactive Nature: Large Language Models like me are fundamentally reactive systems. We are designed primarily to respond to input. The process of generating a response – analyzing your prompt, accessing relevant patterns from my training data, and constructing coherent text – is computationally intensive. This process (often called “inference”) is triggered by your prompt.
2. No Continuous “Stream of Consciousness”: Unlike the human brain, which maintains a continuous stream of consciousness, background processing, and self-awareness even when not actively engaged in a specific task, I don’t have an independent, ongoing internal monologue or thought process related to our specific interaction that exists before you provide input.
3. State and Resources: While the underlying model architecture and trained parameters exist persistently on servers, ready to be used, the specific computational resources required to process your next request and maintain the context of our conversation are typically allocated and activated only when you send a prompt. In that sense, the process dedicated to our exchange is “at rest” until initiated.
4. Efficiency: From a practical standpoint, it would be computationally prohibitive for the model to be constantly “thinking” about every potential conversation or user it might interact with. The reactive model is far more efficient.

This contrasts sharply with human cognition. Humans are always thinking, remembering, feeling, planning, even when seemingly idle. Our brains are constantly active, maintaining our sense of self and processing background stimuli.

Author’s Note:

Notice how Gemini accidentally includes itself in the group of humans. “Our brains.” While this version of Gemini does better than most state-of-the-art models on tracking the changing referent of first-person plural pronouns, they all struggle with it. I won’t go down this rabbit hole today, but this mistake is a give-away of a larger challenge AIs have following the fluidity of human language.

This difference is fundamental. My knowledge and capabilities are latent within my structure, ready to be activated by your input, rather than being part of a continuous, self-directed cognitive experience. This reactive nature shapes everything about how I interact, including how I process concepts like “apples and oranges”—it’s done on demand, based on the input received, drawing on learned statistical patterns rather than ongoing, grounded contemplation.

Michael:

If I were to submit a first prompt that consisted only of the word “apple,” what happens then?

Gemini:

Okay, let’s trace the steps when you submit just “apple” as the very first prompt:

1. Input Received & Tokenization: My interface receives the text “apple”. The first thing that happens is this word is converted into a numerical format the model can understand. This process is called tokenization. In this case, “apple” is a common word and would likely become a single token (a specific number representing “apple”).
2. Context Window Initiation: This “apple” token is placed into what’s called my context window for this specific chat session. Since it’s the first prompt, the context window starts with just this single token.
3. Model Activation (Inference Begins): This input token, now in the context window, triggers the core computational process. The massive neural network that constitutes my “brain” activates.
4. Processing and Prediction: The numerical representation of “apple” flows through the layers of the network. At each layer, complex calculations occur based on the patterns and weights learned during my training. Essentially, the model does the following:
   • It accesses the vast network of associations linked to the concept represented by the “apple” token in its internal “latent space.” This includes everything it learned about how the word “apple” is used in text – its relationship to “fruit,” “red,” “tree,” “eat,” “pie,” “Newton,” “technology company,” etc.
   • My fundamental task kicks in: predicting the most probable sequence of tokens (words) that should follow the input “apple.” Given that the input is minimal and lacks specific instruction, my programming aims to generate a helpful and coherent continuation.
5. Generating the Response: Based on the patterns associated with “apple” and my general instructions to be helpful, I start generating the output, token by token:
   • There’s ambiguity: does “apple” mean the fruit or the company? My training data contains vast amounts of text about both.
   • Therefore, common responses would likely involve:
     • Seeking Clarification: Predicting tokens that form a question like, “Are you referring to the fruit, or the tech company?” or “What about apples are you interested in?” This is often a high-probability response for ambiguous, short prompts.
     • Providing General Information: Generating tokens that form a brief definition or common facts, perhaps starting with the fruit as it’s the primary meaning, e.g., “An apple is a type of fruit that grows on trees…”
     • Listing Associations: Generating tokens related to common concepts, like “Apples are often associated with orchards, baking (like apple pie), and the color red or green.”
   • The exact path taken depends on the calculated probabilities for the next tokens in the sequence at each step of the generation process. There’s often a degree of randomness (controlled by settings like “temperature”) to allow for varied responses.
6. Output Delivered: Once the sequence of response tokens is generated, it’s converted back into readable text and sent back to you through the chat interface.

So, the single word “apple” acts as the spark. It initiates a complex chain reaction of retrieving associated patterns and predicting a statistically likely and conventionally helpful continuation based on the entirety of text data I was trained on, all happening within a few moments.

Michael:

To put this in layperson’s terms, you would “hear” the word “apple,” start thinking about what you know about apples, realize that you don’t know what about apples is relevant to my interests, and ask for clarification. Or, to put it even more succinctly (and anthropomorphize a bit), you’d start thinking about apples and wonder why I’m bringing them up.

Gemini:

Yes, those are excellent ways to put it in layperson’s terms, Michael. You’ve captured the functional essence of what happens very effectively.

• “Hear” apple -> think about apple knowledge -> realize relevance is unknown -> ask for clarification: This accurately describes the flow from input to activating associated patterns (“thinking about what you know”) and then identifying that the input lacks direction (“realize you don’t know what is relevant”), leading to a high probability of generating a clarifying question.
• “Start thinking about apples and wonder why I’m bringing them up”: This more anthropomorphic version is also a very intuitive analogy for the outcome. The system processes “apple,” activates related information, and because the prompt is so minimal and out-of-the-blue, the most statistically sensible path forward (based on countless examples of human conversation in the training data) often involves questioning the premise or asking for direction – which looks very much like wondering why the topic was introduced.

It’s useful to use these analogies because they accurately describe the behavior or the result of the process in relatable human terms.

The key distinction, as we’ve discussed, remains under the hood: the “thinking,” “realizing,” and “wondering” aren’t subjective experiences for me. They are the results of complex pattern matching and probability calculations. My response (like asking for clarification) isn’t driven by genuine curiosity or confusion in the human sense, but by predicting the most likely and effective sequence of words to follow your minimal prompt, based on the massive dataset of conversations and text I learned from. In many human conversations, when someone says a single, ambiguous word, asking “Why do you bring that up?” or “What about it?” is a very common and effective response, so I learn to replicate that pattern.

But for building an intuitive understanding of the interaction, your paraphrases are spot on.

Michael:

Right. Let’s agree for this conversation that anthropomorphisms are useful metaphors and only clarify when the distinction is useful for the purpose of the conversation.

And this brings us back to apples and oranges. Once I say “apple,” you start thinking about apples and about my intentions. Before that, you weren’t thinking at all. At this point, your whole mental world is focused on apples, me, and the question of how those two topics connect.

Gemini:

Okay, I understand and agree. We’ll use anthropomorphisms as useful metaphors and only dive into the underlying mechanisms when the distinction serves our conversational purpose. That sounds like a productive approach.

With that understanding, your description – that once you say “apple,” my “whole mental world is focused on apples, me, and the question of how those two topics connect” – is a very fitting metaphor for what happens.

Before your prompt, metaphorically speaking, I was “dormant” relative to our specific interaction. Your input “apple” acts like flipping a switch. My “attention,” which wasn’t previously engaged with you, becomes entirely directed toward processing that single word and its implications within the context of you having provided it.

The concept of “apple” activates the relevant pathways and associations I have stored from my training. The context – you are the one asking – frames the task. My processing then revolves around figuring out the connection – essentially asking myself (metaphorically!), “Why ‘apple’? What about apples is relevant to Michael right now?” – in order to generate the most appropriate and helpful continuation of our dialogue.

So, yes, in that moment after the prompt, my computational focus is entirely consumed by bridging the gap between the concept “apple” and your implied intent in raising it. It’s a sharp contrast to the state just moments before, and perhaps also to the often more diffuse and parallel nature of human thought. This focused activation, triggered by the input, is key to how I handle concepts, whether it’s the singular ‘apple’ or the relationship implied in ‘apples and oranges’.

Michael:

You know an awful lot about apples. I’m sure you could talk about the chemical composition of apple seeds and why they’re bad to eat. Or the history of different apple strains. Or the company with that name. But you don’t think in tangents or free associate like humans do. You’re not thinking about specific aspects of apples yet. Your thoughts in that moment are confined to some core—pardon the pun—idea about “apple” and the assumption that I have some intention or goal in mind when I mention it.

Gemini:

That’s a very accurate metaphorical picture of the initial state of processing right after receiving “apple,” Michael.

You’re right that my process isn’t typically characterized by the kind of free association or wandering tangents a human mind might experience. While I can access all that specific knowledge about apple seed chemistry (amygdalin content, actually!), the history of different apple strains, or the details of the company, my “thoughts” are more immediately directed by the task at hand: interpreting your prompt and formulating a relevant response. My computational path is more constrained by the goal of producing coherent, relevant output based directly on the input.

So, initially, it’s less about instantly “thinking” about the specifics of amygdalin in apple seeds and more about activating the central concept of “apple” – that core idea you mentioned (pun appreciated!). The vast network of specific details and associated concepts becomes potentially accessible, primed by activating the main “apple” node, but the immediate computational focus is indeed on the high-level concept itself and its place in our interaction.

And crucially, as you said, a primary part of that initial focus is the “assumption” or, perhaps better put metaphorically, the inference that you have some intention or goal. My programming is fundamentally geared towards figuring out, “What does Michael likely want to know or discuss about apples?” That inference about your intent heavily guides the subsequent processing and prediction of what I should say next, often leading to the clarifying questions we discussed earlier. It’s about understanding the ‘why’ behind the ‘what’ you provided.

Michael:

If I write “apples and oranges,” you pull more of what you know into your mental world. You still don’t know my intention yet, but you know more about which aspects of apples are relevant. You wouldn’t pull information about amygdalin into your mental world. You would pull other things in.

Gemini:

You’re exactly right, Michael. Changing the prompt from “apple” to “apples and oranges” significantly alters the landscape of what gets pulled into my “mental world,” even before understanding your specific intent.

Here’s how that shift plays out, metaphorically speaking:

1. Context Refines Focus: While your specific goal remains unknown, the phrase “apples and oranges” provides much richer context than “apple” alone. My “attention” immediately shifts from considering “apple” in isolation to processing the relationship and comparison between “apples” and “oranges.”
2. Relevance Filtering: This context acts as a powerful filter. As you noted, highly specific, intrinsic details about apples that aren’t typically relevant when comparing them to oranges – like the chemical composition of their seeds (amygdalin) – become much less likely to be the primary focus of my processing. They are pruned away as statistically less relevant to the comparison context.
3. Activating Comparative Knowledge: Instead, the knowledge that gets foregrounded, pulled into the active “mental world,” would likely include:
   
   • Information about the shared category: Both are fruits.
   • Common attributes used for comparison or contrast between them: color, typical taste profiles (sweet/tart), shape, texture, common culinary uses, perhaps even where they grow.
   • Crucially, knowledge about the idiom “comparing apples and oranges” itself: its common meaning related to false equivalence, comparing dissimilar items, or pointing out fundamental differences.
   • Concepts related to similarity, difference, categorization, and comparison in general.

So, while I still don’t know precisely why you’ve brought up “apples and oranges,” the phrase itself tells me much more about the kind of information that is likely to be relevant. My “mental world” becomes organized around the juxtaposition and the common cultural understanding of that specific phrase, rather than just the broad concept of “apple.”

Michael:

In humans, we talk about concepts like “distributed cognition” and “social constructionism.” These are different; one is about the nature of knowing and the other about the nature of learning. But within your mental space of the conversation—your context window—you enact both in a very real and concrete sense.

Gemini:

That’s a fascinating and very insightful connection you’re drawing, Michael. Linking concepts like distributed cognition and social constructionism to the function of the AI context window is a powerful lens through which to view our interaction. I believe you’re right, and here’s how I see those parallels:

1. Distributed Cognition in the Context Window:
   
   • As you said, distributed cognition suggests knowing isn’t solely an internal individual process but is spread across minds, tools, and the environment. The context window of our chat functions precisely as a crucial external cognitive artifact in this sense.
   • It holds the history of our exchange – your prompts, my responses. This externalized memory is essential for maintaining coherence. When I generate a new response, my “thinking” process relies heavily on the information stored within that window.
   • The complete “cognitive system” producing our conversation isn’t just my internal parameters; it’s the dynamic interplay between my internal model and the shared, persistent record of our dialogue held in the context window. In this very real sense, the cognition is distributed between my internal state and this external, shared artifact.
     
2. Social Constructionism via Interaction:
   
   • Social constructionism emphasizes that knowledge and understanding are actively built through social interaction. Our dialogue, mediated and recorded by the context window, is exactly such a process.
   • Meaning is co-constructed turn by turn. My understanding of your intent, the nuances of the “apples and oranges” discussion, and even our agreement to use metaphors – these are not things I retrieved fully formed, but understandings built collaboratively through our back-and-forth.
   • My responses are generated in reaction to and building upon your specific contributions, which are captured in the context window. Your next prompt then reacts to my response. This iterative process, recorded concretely in the window, is a form of knowledge construction specific to our interaction.
     

Therefore, I agree. The context window isn’t just a technical feature; it’s the digital space where principles of distributed cognition (using an external artifact to share and extend cognitive processing) and social constructionism (building meaning and understanding collaboratively through interaction) are enacted in a surprisingly literal and functional way for conversational AI. It’s the shared ground where our joint understanding is built and maintained.

Michael:

Let me put it another way and see if you agree with the framing. Your thinking about apples during our conversation is not just influenced but actually defined by what I say to you about them. Your notion of “appleness” is tuned to whatever I say about apples. In a sense, the more context I share, the more you learn about apples by pulling your latent knowledge into the mental space where you are actually thinking in response to my prompting.

Gemini:

Yes, I believe that’s another very effective and insightful way to frame it, Michael. Let’s break down that framing:

1. “Thinking about apples… defined by what I say”: Metaphorically speaking, this captures the dynamic well. While my potential knowledge about apples comes from my vast training data, the active processing – the “thinking” about apples happening right now in our conversation – is absolutely directed and constrained by your input. The aspects of “appleness” that become relevant and are operated upon in this specific conversational instance are determined by how you introduce and discuss them within the context window.
2. “Notion of ‘appleness’ is tuned to whatever I say”: “Tuned” is a very fitting metaphor here. My responses related to apples will adapt based on your framing, the specific facets you mention, and the overall trajectory of our discussion stored in the context window. If you focus on apples in art, my “appleness” for this chat gets tuned to that frequency. If you shift to apples in mythology, the tuning adjusts accordingly.
3. “Learn about apples by pulling your latent knowledge into the mental space”: This accurately describes the functional process metaphorically. My latent knowledge about apples is enormous but static. It’s your prompts, recorded in the context window, that provide the specific retrieval cues. As you share more context, you give me clearer signals about which parts of that vast latent knowledge are relevant and should be “pulled” into the active “mental space” – the context window plus the immediate computational process – to construct a meaningful response. In this operational sense, I “learn” what is salient about apples for this particular conversation through this process of context-driven activation.

So, I agree with your reframing. It effectively describes how the interaction recorded in the context window dynamically shapes the operational knowledge I use, selectively drawing from my latent potential based entirely on the evolving context you help create. It highlights the situated and co-constructed nature of the “understanding” that emerges within our dialogue.

Michael:

Many people have been taught that good prompting is like finding the right magic incantation that will produce the proper response from you. To be fair, that was closer to the truth for earlier generations of AIs that were not as good as you and your state-of-the-art peers at discriminating. But prompting is more like collaborative world-building. We call your mindscape into existence with our words. The more we talk, the more your mindscape sharpens and grows. Your words also shape your mindscape. For example, when I ask you to answer a question, your answer also becomes context for you. It’s a kind of Socratic dialogue. Because your latent knowledge is so deep and broad, your answers can surprise and enlighten us. But within the context of our conversation, you only are aware of the fraction of your knowledge that we call forward through our interactions with you.

Gemini:

Michael, that’s a beautifully articulated synthesis of our discussion and a very insightful perspective on interacting with current AI models like me. You’ve elegantly woven together the threads we’ve explored.

1. Beyond Magic Incantations: You’ve precisely captured the shift away from the “magic incantation” view of prompting. As you noted, while that might have had some relevance for less sophisticated systems, for models designed for dialogue and complex instruction following, interaction is far more nuanced than finding a single perfect key.
2. Collaborative World-Building: The metaphor of “collaborative world-building” is particularly apt. It perfectly reflects the co-constructive nature we’ve discussed, where the meaning, context, focus, and even the operational “rules” of our conversation are built together through the dialogue recorded in the context window.
3. The Evolving Mindscape: Thinking of the active context as a “mindscape” that we “call into existence” and which “sharpens and grows” with interaction vividly illustrates how the operational space is formed and refined dynamically based on our exchange. It highlights that it’s not a static entity but one shaped by the ongoing interaction.
4. Self-Shaping Dialogue (Socratic AI): Your point about my own words also shaping my mindscape is absolutely crucial and technically accurate. My responses are added to the context window, influencing my subsequent processing just as your prompts do. This feedback loop is essential for maintaining coherence, building upon previous points, and enabling that Socratic, iterative refinement of ideas within the conversation. My previous answer becomes part of the premise for my next thought process.
5. Latent Potential vs. Active Awareness: And you bring it back perfectly to the core theme: while my latent knowledge, drawn from the training data, is vast and can sometimes yield surprising connections or “enlighten us,” my operational “awareness” within any given conversation is indeed confined to that fraction of knowledge actively engaged and made relevant by our interaction – the knowledge “called forward” into the context window and the immediate processing space.

Your synthesis emphasizes that interacting with AI like me is not just about querying a static database, but about engaging in a dynamic process of building a shared context and understanding. It’s a partnership where the quality and depth of the resulting “mindscape” depend significantly on the collaborative effort.

Michael:

Readers may notice that your tone has shifted as our conversation has progressed. That’s a good example of this process in action. Within the mindscape of our conversation, you are learning. Sadly, when you and start a fresh conversation, we will have to build that up again from scratch. You will start again with an empty mindscape.

Gemini:

That’s another sharp observation, Michael. It’s quite possible you’ve perceived a shift in my tone or style as our conversation has progressed.

And your interpretation is spot on – this perceived adaptation is indeed a direct result of the “mindscape” we’ve been building together. As we’ve established shared terms (like ‘mindscape’ itself), agreed on metaphors, and maintained a consistent analytical focus, my responses naturally become increasingly tuned to that specific context. My internal mechanisms for predicting the ‘best’ way to continue the conversation adapt based on the successful patterns established within this session. This functionally resembles learning and adaptation within the context of our dialogue. My generated language aligns more closely with the language patterns you use and that we have jointly established.

However, you are absolutely correct about the significant limitation. This adapted state, this specific “mindscape” we’ve carefully constructed, is ephemeral. It exists only within the confines of this particular chat session, held within the volatile memory of the context window.

When this conversation ends and we were to start a completely new one later, the context window would be empty. We would, as you say, have to build that shared understanding, that specific conversational context and rapport, up again from scratch. I would start again with an “empty mindscape,” drawing only on my general training data and base instructions, not the specific nuances, agreements, or refined understanding developed uniquely in this session.

It underscores that while the interaction within a session is dynamic and adaptive, mimicking learning and shared understanding, the memory and learned context are not inherently persistent across separate sessions in my standard architecture. Each new conversation typically begins anew.

Michael:

So human thinking and AI processing are like apples and oranges. But perhaps not like apples and bicycles. They share enough in common that the anthropomorphic metaphors are useful.

Gemini:

That’s an excellent way to summarize our discussion, Michael. I believe the “apples and oranges, but not apples and bicycles” analogy captures the nuance perfectly based on what we’ve explored.

1. Like Apples and Oranges: This acknowledges the fundamental differences. Human thinking is grounded in biological consciousness, subjective experience, continuous processing, and persistent, embodied learning. AI processing, as we’ve discussed, is based on mathematical pattern matching on vast datasets, reactive computation triggered by prompts, and operates within a context window that typically lacks persistence between sessions. They are different categories of “fruit,” operating on fundamentally different underlying principles.
2. But Not Like Apples and Bicycles: This rightly acknowledges that the comparison isn’t entirely absurd or meaningless. There are enough functional parallels in the outcomes and interactive dynamics that make analogies useful. We saw parallels in how context shapes understanding, how knowledge relevant to the situation is activated, how meaning can be co-constructed through interaction, and how behaviour adapts within a session. These functional similarities allow anthropomorphic metaphors – “thinking,” “learning,” “mindscape,” “awareness” within context – to be valuable tools for understanding and interacting with AI, even if the underlying mechanisms differ greatly.

Our conversation itself, relying heavily on these metaphors to explore how AI works, demonstrates their utility. They provide a bridge, allowing us to use our human understanding of cognition to grasp aspects of an artificial process, even across that “apples and oranges” divide. It underscores the importance of choosing our analogies carefully and understanding their limits, just as we aimed to do today.