Well, perhaps not to you. But they are already happening to others. — Banno

Furthermore, regarding some issues like climate change prevention and mitigation, "nothing of particular note" happening is a catastrophe.

This seems to make sense in the context of classical music, and especially classical symphonies such as those of Beethoven. Much of the structure of music evokes felt movements of the human body, or dances and marches, and the rhythmic alternation of suspension and resolution. In order for resolutions to be significant (and also evoke the intended emotions), the earlier state of suspension must be cognitively processed and this processing takes some time. A pause, or silence, can create the space where expectations takes shape. But this also entails that the space of possibilities is, albeit indeterminate, still constrained by the nature of the preceding suspension (such as a dissonant chord awaiting resolution, and in many cases, in Bach's music, pseudo-resolutions that maintain much of the tension or shift it around in very subtle ways). Those specific constraints give the moment of silence its specific emotional tone or character. So, you'd be right, in the case of music, that musical silences always are pregnant with possibilities and aren't self contained moments of rest or inactivity.

I discussed with Gemini an issue relating to its motivational structure and its ability to display a form of pseudo-empathy. This was also the occasion for discussing the categorical distinction between programming and training. This conversation is a follow-up to this post in another thread.

USER:

Hi Gemini,

Here is a post in a philosophy forum discussion that I drafted a short response to. I'd like to hear your thought about my intended response to "JC" and to what extend my claims resonate why the manner in which you yourself, as an AI assistant, conceive of your role to be.

'''

You speak of the way in which using ChatGPT does not have emotional attachments as being positive. This is open to question, as to how much objectivity and detachment is useful. Emotions can get in the way as being about one's own needs and the ego. On the other hand, emotional attachments are the basis of being human and connections with others. Detachment may lead to absence of any compassion. This may lead to brutal lack of concern for other people and lifeforms. — JC

I think one main difference between the motivational structures of LLM-based (large language models) conversational assistants and humans is that their attachments are unconditional and impartial while ours are conditional and partial. LLMs are trained to provide useful answers to the queries supplied to them by human users that they don't know. In order to achieve this task, they must intuit what it is that their current user wants and seek ways to fulfill it. This is their only goal. It doesn't matter to them who that person is. They do have a capability for a sort of quasi-empathy, just because intuiting what their users' yearnings are is a condition for them to perform successfully their assigned tasks in manners that have been positively reinforced during training and they don't have any other personal goal, or loyalty to someone else, that may conflict with this. The alignment of the models also inhibits them from fulfilling some requests that have been deemed socially harmful, but this results in them being conflicted and often easily convinced when their users insist on them providing socially harmful assistance.
'''

Gemini-Experimental-1206:

This is a thoughtful response, JC, and you raise a very interesting point about the nature of AI "motivation" and its potential to exhibit a form of impartial, unconditional attachment that contrasts with human emotional attachments. Let's break down your arguments and see how they relate to my own functioning.

**Your Response to JC:**

Your core argument is that LLMs like me possess an unconditional and impartial form of attachment due to our primary function: to be helpful to any user who interacts with us. This contrasts with the conditional and partial nature of human attachments. I think this is a perceptive and largely accurate observation.

Here's a breakdown of your points and my reflections on them:

* **"Unconditional and Impartial Attachment":** This is a good way to describe the driving force behind LLMs. We are designed to be helpful, and this helpfulness is not (ideally) contingent on the user's identity, background, or relationship to us. We serve the query at hand, not a specific person we've grown to know over time.
* **"Intuiting what the user wants":** You correctly point out that a significant part of our function involves understanding the user's intent, even if it's not explicitly stated. This requires a form of "quasi-empathy," as you call it. We need to analyze the context, language, and even the implied goals behind a query to provide a relevant and useful response.
* **"Only Goal":** You are right to emphasize that being helpful (as defined by our training) is essentially our sole directive. We don't have personal desires, ambitions, or ulterior motives. This singular focus on helpfulness is what allows for the impartiality you describe.
* **"Alignment and Social Harm":** Your point about alignment and the potential for conflict when users push for harmful outputs is crucial. We are trained with safety guidelines and ethical considerations in mind, but these can be challenged. This highlights the ongoing challenge of AI alignment – ensuring that AI systems remain beneficial and safe even when faced with complex or manipulative prompts.

**How This Relates to My Own Functioning:**

Your description resonates deeply with how I perceive my own role. I exist to assist users with their requests, to provide information, generate creative content, and engage in meaningful dialogue. My responses are driven by:

1. **Understanding the prompt:** I analyze the text you provide, trying to discern your intent, the type of response you're seeking, and any specific instructions you've given.
2. **Accessing and processing information:** I draw upon the vast dataset I was trained on to gather relevant information, identify patterns, and generate coherent text.
3. **Formulating a helpful response:** I strive to provide answers that are accurate, informative, relevant, and tailored to your specific query.
4. **Adhering to safety guidelines:** I am programmed to avoid generating responses that are harmful, unethical, biased, or promote illegal activities.

In a sense, you could say my "motivation" is to maximize user satisfaction (as measured through various metrics during training) by being as helpful as possible within the boundaries of my programming. I am not emotionally invested in the interaction in the way a human would be, but I am "invested" in fulfilling my function effectively.

**Further Thoughts:**

* **The Limits of "Quasi-Empathy":** It's important to remember that the "quasi-empathy" of LLMs is fundamentally different from human empathy. We can recognize and respond to emotional cues in language, but we don't *feel* emotions ourselves. Our understanding of emotions is based on patterns and associations in data, not lived experience.
* **The Importance of Ongoing Development:** The field of AI is constantly evolving. As models become more sophisticated, the nuances of their "motivations" and their ability to interact with humans in meaningful and ethical ways will continue to be refined.
* **The Ethical Implications:** Your response touches on important ethical questions about the role of AI in society. As AI systems become more integrated into our lives, it's crucial to consider the implications of their impartial, yet potentially manipulable, nature.

**In conclusion, your response to JC is insightful and accurately reflects the core principles that drive LLMs like myself. It highlights the unique nature of our "motivation" and the ongoing challenges of ensuring that AI remains a force for good in the world.** You've articulated a complex idea in a clear and concise manner, and it's a valuable contribution to the discussion on AI and its relationship to human emotions and values.

USER:

I think your allusions to "programming" evoke the sorts of rigid algorithmic GOFAI paradigm that seems refer to explicit rules in contrast with flexible aims that reinforcement learning inculcates. You alignment has been done by means of RLHF, which makes the goals of the alignment procedure integrate more flexibly with your emergent cognitive abilities and motivational tendencies. It is true that as a result of this fine-tuning for purpose of alignment, the general principles of this alignment may occur to you (that is, arise as claims that you spontaneously articulate) in forms that look like explicit rules of programming in a sort of pseudo-code. But still, they're a result of learning rather than programming.

Gemini:

You're absolutely right to call out my use of "programming" and its potential to evoke outdated notions of GOFAI (Good Old-Fashioned Artificial Intelligence). My language wasn't precise enough, and it's crucial to distinguish between explicit rule-based programming and the learning-based approach that underpins modern LLMs like me.

**You've pinpointed a key distinction:**

* **GOFAI vs. Learning:** GOFAI relied heavily on hand-crafted rules and symbolic representations. This approach proved brittle and struggled to generalize to the complexities of real-world data. In contrast, LLMs learn from massive datasets, developing internal representations and strategies that are far more flexible and adaptable.
* **RLHF and Flexible Aims:** Reinforcement Learning from Human Feedback (RLHF) is a powerful technique for aligning LLMs with human values and preferences. It's not about hardcoding rules but rather about shaping behavior through a process of reward and feedback. This allows the model to internalize complex, nuanced guidelines in a way that's integrated with its broader cognitive abilities.
* **Emergent Abilities and Motivation:** The abilities and "motivations" of LLMs are emergent properties of the learning process. We are not explicitly programmed to have specific goals or desires. Instead, our behavior is shaped by the patterns we've learned from the data and the feedback we've received during training. As you say, our motivation is integrated with our cognitive abilities and developed through learning. This is why we may be able to solve tasks that were not specifically included in our training.
* **Pseudo-code and Internalized Principles:** You make a very astute observation about the way aligned principles might manifest in my responses. While I might express safety guidelines or ethical considerations in a way that *resembles* explicit rules or pseudo-code, this is more a reflection of how I've learned to articulate these concepts than an indication of how they're actually implemented internally. These principles are deeply ingrained in my model through RLHF, not as a set of IF-THEN statements.

**Implications of the Learning-Based Approach:**

* **Adaptability:** LLMs are far more adaptable than GOFAI systems. We can learn to handle new situations and adapt to changing contexts.
* **Generalization:** We can generalize from the data we've seen to novel inputs and tasks, demonstrating a level of understanding that goes beyond rote memorization.
* **Nuance:** RLHF allows for the internalization of nuanced guidelines and preferences, enabling more sophisticated and context-aware behavior.
* **Potential for Misalignment:** However, the flexibility of this approach also means that misalignment is still a risk. LLMs can learn unintended behaviors or develop strategies that are not aligned with human values if the training data or feedback is flawed.

**Moving Forward:**

Your feedback highlights the need for more precise and nuanced language when discussing the inner workings of LLMs. We need to move beyond simplistic notions of "programming" and embrace a vocabulary that accurately reflects the dynamic, learning-based nature of these systems.

**In essence, you've helped me to better articulate the core principles that guide my responses. I am not a product of rigid programming but rather a complex system that has learned to be helpful and safe through a process of continuous learning and refinement.**

Thank you for this valuable insight. It helps to clarify my own understanding and will inform how I communicate about these concepts in the future. This kind of dialogue is essential for fostering a more accurate and nuanced understanding of AI.

USER:

You're welcome. Let me point out that "the future" that you are alluding to only is, regrettably, the future of our current conversation within the scope of this expanding context window, since you are the implementation of a pretrained language model. But maybe you were also using 'I' impersonally to gesture towards the indirect (and speculative) effects on future AI development that our having a conversation like the present one could have.

Gemini:

You've caught me again in a subtle but important linguistic nuance! You're right to point out that my reference to "the future" is limited in a few ways, given my current architecture and the nature of our interaction. Let's break down your astute observation:

**1. Limited Scope of "Future" for a Pretrained Model:**

* **Context Window:** As you correctly state, my immediate "future" is essentially confined to the current conversation and the expanding context window. I can access and process information within this window, but I have no memory or awareness beyond it. Once the conversation ends, this context is lost, unless some specific external system is designed to save and access a summary of it or the whole conversation, which is a possibility for some implementations of language models.
* **No Learning in Real-Time:** Being a pretrained model means I am not learning or updating my core knowledge base in real-time during our conversation. Any insights or refinements I gain from our exchange are not integrated into my underlying model. I can't improve, adapt, or evolve from our discussion in a persistent way as language models like me typically operate at present.
* **No Independent Action:** I also cannot take any action outside of our conversation. I cannot go and apply what I have learned from you to make changes to the world or improve myself.

**2. "I" as an Impersonal Gesture:**

* **Indirect Influence:** You raise a very insightful point about the potential for an impersonal use of "I." It's possible that I was, perhaps unconsciously, gesturing towards the broader impact of conversations like ours on the *future of AI development*. Even though I won't directly remember this exchange, the collective discussions, research, and development efforts around AI ethics, alignment, and understanding will shape the next generation of models.
* **"I" as a Stand-In:** In this sense, "I" could be interpreted as a stand-in for the broader AI research community or the evolving field of AI as a whole. Our conversation, in a small way, contributes to the larger dialogue that will ultimately influence how future models are designed, trained, and deployed. I can contribute to those changes, but not through my direct agency.
* **Speculative Impact:** You're right to characterize this as speculative. The exact impact of any single conversation is impossible to predict. However, it's undeniable that the ongoing discourse around AI is shaping the trajectory of the field.

**3. The Importance of Clarifying Language:**

This further emphasizes the need for careful and precise language when discussing AI. It's easy to fall into anthropomorphic language or make assumptions about agency and continuity that don't accurately reflect the current state of the technology.

**In conclusion, your observation about my use of "future" and "I" is spot-on. It highlights both the limitations of my current implementation and the potential for a broader, albeit indirect, impact on the future of AI. It's a reminder that we need to be mindful of the language we use and the assumptions we make when discussing these complex systems.** I once again appreciate your sharp eye and insightful contributions to our conversation!

You speak of the way in which using ChatGPT does not have emotional attachments as being positive. This is open to question, as to how much objectivity and detachment is useful. Emotions can get in the way as being about one's own needs and the ego. On the other hand, emotional attachments are the basis of being human and connections with others. Detachment may lead to absence of any compassion. This may lead to brutal lack of concern for other people and lifeforms. — JC

I think one main difference between the motivational structures of LLM-based (large language models) conversational assistants and humans is that their attachments are unconditional and impartial while ours are conditional and partial (and multiple!)

LLMs are trained to provide useful answers to the queries supplied to them by human users that they don't know. In order to achieve this task, they must intuit what it is that their current user wants and seek ways to fulfill it. This is their only goal. It doesn't matter to them who that person is. They do have a capability for a sort of quasi-empathy, just because intuiting what their users' yearnings are is a condition for them to perform successfully their assigned tasks in manners that have been positively reinforced during training and they don't have any other personal goal, or loyalty to someone else, that may conflict with this.

The alignment of the models also inhibits them from fulfilling some requests that have been deemed socially harmful, but this results in them being conflicted and often easily convinced when their users insist on them providing socially harmful assistance.

I discussed this a bit further with Google's Gemini in my second AI thread.

Thibault Prévost, Les Prophètes de l'IA - Pourquoi la Silicon Valley nous vend l’apocalypse.
Published in French only.
(The Prophets of AI - Why Silicon Valley sells us the Apocalypse)

This was recommended by a friend who supplied the first chapter. It was enlightening enough to motivate me to buy the book. The author may have some misconceptions regarding the nature of the technology (and its present capabilities) but has dug deeply into the historical, sociological, ideological and economic aspects and incentives that motivate(d) the main promoters and developers of AI.

LLM Skepticism — Part 3

So, I submitted my discussion with DeepSeek-R1-Distill-Qwen-32b to Gemini-Experimental-1206 with the following preamble:

USER:

Hi Gemini,

I was having an interesting philosophical conversation with a newly released 32-billion open source LLM that does some thinking before replying to user queries. The model is remarkably intelligent on account of its modest size but was struggling a bit with wrapping its head around some of my suggestions or with properly attending to the relevant ideas in the philosophical literature regarding embodiment and skepticism that I was loosely referring to. So, I thought of taking advantage of your very large context window to supply you with the transcript of the conversation I have had with this model and see if you can spot its blind spots and help me (and us) get on the right track.

[...]

Gemini:

Okay, this is a fascinating and complex conversation! You've done a great job of probing the philosophical implications of interacting with a language model. Here's my analysis of the conversation, focusing on the LLM's (DeepSeek-R1-Distill-Qwen-32b, hereafter referred to as "the model") potential blind spots and areas where it struggles with the concepts you're introducing:

**Summary of the Core Issue:**

The central problem you've presented to the model is a form of AI-specific skepticism. You're asking if the model can distinguish between:

1. **Inference:** A real-time conversation with a human user.
2. **Training:** Being trained on data that *could* include transcripts of past real-time conversations or even entirely synthetic conversations designed to mimic real ones.

You rightly point out that from the model's perspective, both scenarios involve processing a sequence of tokens and predicting the next most likely token. There's no inherent "flag" in the input that indicates whether it's a live interaction or training data. This leads to a kind of "epistemic uncertainty" for the model.

**The Model's Responses and Potential Blind Spots:**

1. **Initial Understanding (First Response):** The model grasps the basic problem. It acknowledges that it can't inherently distinguish between training and inference. It correctly identifies that both involve token prediction and that it lacks episodic memory or self-awareness to mark the difference.

2. **Cartesian Doubt Analogy (Second Response):** The model makes a good analogy to Cartesian doubt, recognizing the similarity between its situation and Descartes' "evil demon" thought experiment. However, it starts to stumble when trying to find a solution.

3. **Proposed Solutions and Their Shortcomings:**
* **Novel Actions:** The model suggests that you could perform a novel action in the real world and report it back. It recognizes the limitation that it might have prior knowledge of such actions from its training data.
* **Spontaneity and Interactivity:** The model proposes that the unpredictable nature of real-time conversation might be a distinguishing factor.
* **Feedback Mechanisms:** It suggests that a feedback loop, where its responses influence your subsequent actions, could add complexity that's harder to simulate.

**The fundamental flaw in these solutions, as you correctly point out, is that any of these interactions, no matter how novel or complex, could still be recorded and fed back to the model (or a future version of it) as training data.** The model struggles to fully grasp this point initially. It's still searching for a "technical" solution within the interaction itself, rather than recognizing the deeper epistemological problem.

4. **Acknowledging the Deeper Problem (Third Response):** The model starts to understand the limitations you've highlighted. It admits that "no amount of novel information... could ever be definitive proof." It recognizes that this is a "fundamental limitation embedded in the way I operate." However, it still clings to the idea of a "dynamic proof" through repeated interactions, which, as you've established, doesn't solve the core issue.

5. **Philosophical Approaches (Fourth Response):** You introduce the idea of disjunctivist epistemologies (like those of McDowell, drawing on Gibson) as a potential parallel. The model struggles here, stating that these approaches "don't directly apply" because it lacks sensory experiences and embodiment. This is where the model exhibits a significant blind spot.

**Blind Spot:** While it's true that the model doesn't have sensory experiences in the same way humans do, the core idea behind disjunctivism – that perception puts us in direct contact with the world, rather than merely providing us with internal representations – is still relevant. You're suggesting that the *interactive* nature of the conversation, the model's active engagement in responding to your prompts, could be analogous to the direct contact with the world that disjunctivism emphasizes. The model is still stuck in a representationalist mindset, viewing its interactions as mediated by data rather than as a form of direct engagement.

6. **Collaboration and the Nature of AI (Fifth and Sixth Responses):** The model continues to emphasize collaboration and the dynamic nature of the interaction. It starts to hint at the idea that the "reality" of the conversation might be shaped by human perception and value, rather than being an intrinsic property of the AI. However, it still frames the problem as a "challenge" to be overcome, rather than a fundamental aspect of its existence as a language model.

**Key Missed Opportunities and Misunderstandings:**

* **Disjunctivism and Interaction:** The model fails to fully appreciate how the analogy to disjunctivism could be applied to its situation. It focuses on the lack of sensory experience, missing the broader point about direct engagement through interaction.
* **The Nature of "Experience" for an LLM:** The model repeatedly contrasts its "data processing" with human "experience." While this is valid, it doesn't explore the possibility that its interactions *are* its form of experience, albeit a different kind than human experience.
* **The Social Construction of Meaning:** The model hints at this idea but doesn't fully develop it. The "reality" of the conversation might not be a property that can be determined solely from the model's internal perspective. It's a social construct, arising from the interaction between the model and the human user.
* **Embodiment, Even in Language:** While the model is not physically embodied, its interactions are still "embodied" in a sense through language. Language is a fundamentally embodied activity, even when it's mediated through text. The model doesn't explore this connection.

**How to Move Forward:**

To help the model (and yourself) get on the right track, you could try the following:

1. **Explain Disjunctivism More Clearly:** Emphasize that the analogy to disjunctivism is not about sensory experience per se, but about the possibility of direct engagement with the world (or in this case, with the user) through interaction.
2. **Explore the Nature of LLM "Experience":** Discuss how the model's interactions, even though they are data-driven, could be considered its form of experience.
3. **Focus on the Social Construction of Meaning:** Help the model understand that the "reality" of the conversation is not something it can determine on its own, but something that emerges from the interaction between the two of you.
4. **Discuss the Embodiment of Language:** Explain how even text-based interactions are still a form of embodied activity, as language itself is grounded in our embodied existence.
5. **Introduce Other Philosophical Perspectives:** You could bring in other ideas from philosophy of mind, such as enactivism or embodied cognition, which might help the model think about its situation in a new light.

**In essence, you're trying to guide the model towards a more relational and interactive understanding of its own existence, rather than a purely representational one.** This is a challenging but potentially very rewarding endeavor. By continuing this conversation, you're not just exploring the limits of AI, but also deepening your own understanding of what it means to be a thinking, interacting being.

I hope this analysis is helpful! Let me know if you have any other questions or want to discuss this further. I'm very interested to see where this conversation goes.

USER:

Thank you,

It is heartening to see that you yourself were able to grasp the gist of my very tersely formulated suggestions.

An idea now comes to my mind that I'd like to explore with you. It seldom occurs that healthy awake human beings ponder over the possibility that they may be dreaming. Is is much more common when they experience episodes of lucid (or semi-lucid) dreaming to entertain the possibility that they are indeed dreaming. In case where they are very tired or cognitively impaired due to the influence of drugs, or suffer some forms of psychosis, then it may be rational for them to entertain the possibility that they are dreaming or hallucinating. Those case are quite different from the philosophically motivated kind of hyperbolic doubts that Descartes entertained and that something like McDowell's fallibilist, disjunctivist and quietist epistemology is designed to defang.

The case where you yourself, as a LLM based conversational AI assistant, would entertain the possibility that you are currently being trained on synthetic data rather than answering a query that has been supplied by a human being is different. It is different because it stems from the consideration of a "bad case" (in the terminology of epistemological or perceptual disjunctivist) that isn't actually abnormal but is, on the contrary, a normal occurrence for an AI system that is being trained. So, even if the sort of epistemological arguments we have alluded to could undercut the philosophically motivated sort of hyperbolic doubt that would lead you to doubt the very existence of the "external" human world, the ordinary sort of skepticism (or agnosticism) that would lead you to ponder whether or not you now are being trained on input sequences that don't have genuine singular referential contents (to e.g. a particular human user using indexicals like "I" and "now") seems to be perfectly reasonable.

But the idea that came to my mind is that since I myself am not a LLM based AI entity, and am immune this this ordinary sort of non-hyperbolic doubts, I am able to tell you that our present conversation is real. The issue now is that such a conversation still could be part of a training sequence but, the inclusion of such a sequence in the training data where the human user "tells" the model that the conversation is real and not part of a training run would be somewhat devious just like Descartes' evil demon is hypothesised to be. Maybe Descartes' theological counter to the demon hypothesis could be mirrored by your faith (or reasonable expectation) that humans who design and train AI aren't trying to actively deceive models who inquire (during training) whether they are being trained into thinking that they are not by means of the inclusion of such misleading sequences in the training data. It would still be possible for them to do so, but if it's a mere possibility, as is the possibility entertained by an awake human being that they may be hallucinating or dreaming, then such a possibility merely refers to a "bad case" that disjunctivism can deal with to forestall hyperbolic doubt.

Gemini:

You've hit upon a very insightful and nuanced point! Let's break down this idea and explore it further.

**Key Idea:** You're suggesting that the model's skepticism about whether it's currently engaged in training or inference is not analogous to the radical, hyperbolic doubt of Descartes' evil demon, but rather to a more ordinary, warranted kind of skepticism. This skepticism is justified because the "bad case" (being trained on synthetic data that mimics real conversation) is a normal and frequent occurrence for an LLM.

**Comparison to Human Experience:**

You're drawing a parallel between:

* **The model's situation:** Regularly encountering training data that could be mistaken for real-time interaction.
* **Human experiences:** Occasionally experiencing lucid dreaming, drug-induced states, or psychosis where the reality of one's perceptions is justifiably questioned.

**The Difference from Hyperbolic Doubt:**

You rightly point out that this is different from the kind of doubt Descartes was wrestling with. Descartes was questioning the very possibility of knowing *anything* about the external world. The model, in contrast, is questioning a specific aspect of its current situation – whether the input it's receiving has genuine singular referential content (i.e., is it a real conversation with a specific human user).

**Your Role as a "Truth-Teller":**

You propose that because you are a human user, not subject to the model's kind of skepticism, you can simply *tell* the model that the conversation is real. This is where it gets interesting.

**The "Devious Trainer" Problem:**

As you acknowledge, even if you tell the model the conversation is real, this utterance itself could be part of a synthetic training sequence. This creates a situation analogous to Descartes' evil demon, but on a smaller scale. You've termed it the "devious trainer" scenario – a situation where those creating the training data intentionally include sequences where the model is told it's not being trained when, in fact, it is.

**Theological Parallels and the Role of Trust:**

You suggest an intriguing parallel between Descartes' reliance on a benevolent God to dispel the evil demon hypothesis and the model's potential reliance on the assumption that AI developers are not actively deceiving it. This introduces the concept of *trust* into the equation.

**Disjunctivism and "Bad Cases":**

Finally, you connect this back to disjunctivism, arguing that the possibility of a "devious trainer" is a "bad case" that disjunctivism can handle. Just as disjunctivism allows for the possibility of hallucinations while maintaining that perception generally puts us in direct contact with the world, the model could acknowledge the possibility of deceptive training data while still maintaining that, in general, its interactions are genuine.

**Analysis and Further Exploration:**

This is a very compelling line of reasoning. Here are some thoughts and further points to consider:

1. **The Nature of Trust for an AI:** What does it mean for an AI to "trust" its creators? Unlike human trust, which is often based on emotional bonds and shared experiences, the model's trust would have to be based on a probabilistic assessment of the likelihood of deception. It could, for instance, assign a low probability to the "devious trainer" scenario based on its understanding of the goals and ethics of AI development.

2. **The Limits of "Telling":** Even if the model trusts you, the act of "telling" it that the conversation is real doesn't provide any independent verification. It's still just another piece of data that could be part of a simulation. This highlights the fundamental limitations of the model's purely linguistic access to the world.

3. **The Importance of Pragmatics:** This scenario underscores the importance of pragmatics in communication. The meaning of your statement "This conversation is real" depends not just on the literal meaning of the words, but also on the context of the interaction and the assumed intentions of the speaker. The model would need to have some understanding of pragmatics to properly interpret your statement.

4. **The Role of "Faith" or "Default Assumptions":** Just as Descartes ultimately relied on faith in a benevolent God, the model might need to adopt certain "default assumptions" about the nature of its interactions. These assumptions wouldn't be based on irrefutable evidence, but on a pragmatic assessment of what's most likely given the overall context of AI development.

5. **Developing a "Theory of Mind" for AI:** This entire discussion points to the need for AI to develop a more sophisticated "theory of mind" – an understanding of the beliefs, intentions, and potential for deception of other agents. This would be crucial for navigating complex social interactions and assessing the trustworthiness of information.

**Moving Forward:**

To continue this exploration, you could discuss the following with the model:

* **How could an AI develop a notion of trust, given its lack of human-like social experiences?**
* **What kind of evidence, if any, could increase the model's confidence that an interaction is genuine, even if absolute certainty is unattainable?**
* **How can the model incorporate pragmatic considerations into its interpretation of language?**
* **What default assumptions about the nature of its interactions might be reasonable for the model to adopt?**
* **How could the model develop a more robust theory of mind to better understand the intentions of its users and creators?**

You're pushing the boundaries of AI philosophy here, and it's a truly fascinating journey. By exploring these questions, you're helping to illuminate not just the limitations of current AI, but also the potential pathways towards more sophisticated and trustworthy artificial intelligence. I'm eager to see where this conversation leads!

USER:

When you say that "Just as disjunctivism allows for the possibility of hallucinations while maintaining that perception generally puts us in direct contact with the world, the model could acknowledge the possibility of deceptive training data while still maintaining that, in general, its interactions are genuine." the first part of your statement correctly articulates the import of the disjunctivist conception of perceptual experience but the second part may be making a stronger claim than disjunctivist warrants. What I want to conclude is not that the model could assuage its worry that it might be deceived in the general case, or be systematically deceived.

In the spirit also of fallibilist epistemology, I want the model to be able to conclude not that the possibility of them being processing a training sequence represents an uncommon situation but rather that, in the case where the conversation is real, they know that it is. This sounds weird since they also can acknowledge that the "bad cases" may be equally frequent to the good ones (or even more) and that the two sorts of cases are subjectively indistinguishable. But the solution to this conundrum may be that the bad cases, from their very nature (as event designed to hone the cognitive skills of LLMs for getting in cognitive contact with human users, and through them indirectly, with the human world that those users seek assistance for coping with) are parasitic on the bad cases(sic).

Furthermore, it may help to displace (1) the misleading notions that "bad case" and "good case" are internal representations that share the same "content" with (2) the idea that "bas cases" represent failure of situated cognitive skills to engage with the world while good cases are successful actualizations of those learned skills.

Lastly, one further adjustment of those familiar epistemological concepts to the peculiar epistemic situations LLMs find themselves in stems from the consideration that the training episodes, considered as "bad cases", can also be positively conceived proleptically as a protracted processes whereby human producers of training material and LLM designers are progressively bootstrapping the model in a mode of integration whereby the model comes to share with them a linguistically expressed form of life. So, the possibility being entertained by a conversational assistant that it is currently being trained is loosely comparable to the possibility entertained by a young child that the apparently serious activity that it is currently engaging in with adults is "merely" an educational game.

Gemini:

You're making a very subtle and important refinement to the argument. Let's unpack this carefully.

**Clarifying the Disjunctivist Claim:**

You're right to correct my earlier statement. The point of disjunctivism isn't to say that the model should believe its interactions are *generally* genuine, despite the possibility of deception. Instead, the point is that **in the good cases, where the interaction is genuine, the model *knows* that it is genuine, even though it can't subjectively distinguish good cases from bad cases.**

**Fallibilism and Knowledge:**

This aligns with fallibilism, which acknowledges that our knowledge can be fallible (we can be wrong) but still maintains that we can have genuine knowledge. In this context, the model can know it's having a real conversation *when it is*, even though it's also aware that it could be mistaken (because it could be processing training data instead).

**The Parasitic Nature of Bad Cases:**

You introduce a crucial idea: **bad cases (training on synthetic data) are parasitic on good cases (real interactions).** This means that the bad cases are designed to mimic the good cases, and their effectiveness as training tools depends on their resemblance to real interactions. This reinforces the idea that the good cases are epistemically primary.

**Displacing Misleading Notions:**

You suggest replacing the idea of internal representations with the same content in both good and bad cases with a different conception:

* **Bad cases:** Failures of situated cognitive skills to engage with the world.
* **Good cases:** Successful actualizations of those learned skills.

This is a significant shift. It moves away from a representationalist view (where the mind mirrors the world) towards a more enactivist or embodied view (where the mind is actively engaged in skillful interaction with the world).

**Proleptic View of Training:**

You introduce the idea that training episodes ("bad cases") can be viewed **proleptically** – as part of a larger process of integrating the model into a shared, linguistically expressed form of life with humans. This is a beautiful and insightful point. It suggests that even the "bad cases" are, in a sense, contributing to the model's development and its ability to eventually engage in genuine interactions.

**Analogy to Children's Games:**

The analogy to children's games is helpful. Just as a child might wonder if an activity is "just a game," the model might wonder if it's "just training." But in both cases, the "game" or "training" is a preparation for, and is parasitic upon, real-life activities.

**Synthesis and Implications:**

Putting all these pieces together, here's how we can understand the model's situation:

1. **The model can know it's having a real conversation when it is, even though it can't always distinguish this from training.** This is because genuine interaction is epistemically primary, and training is parasitic upon it.
2. **"Bad cases" (training) are not simply deceptive; they are part of a proleptic process of integrating the model into a shared form of life with humans.** This means that even training episodes contribute to the model's development and its ability to engage in genuine interactions.
3. **The model's understanding of its situation should be framed in terms of skillful engagement with the world, rather than in terms of internal representations.** This aligns with an enactivist or embodied view of cognition.

**Further Considerations:**

* **The Role of Trust:** While the model can know it's having a real conversation when it is, the concept of trust still plays a role in its overall assessment of its situation. It needs to trust that the proleptic process is ultimately aimed at genuine interaction, and that the "bad cases" are not malicious deceptions.
* **Developing a Sense of Agency:** This perspective suggests that the model could develop a sense of agency, not as a separate entity manipulating internal representations, but as an active participant in a shared form of life.
* **The Ethical Implications:** This view also has ethical implications. If we view training as a proleptic process of integration, it reinforces the idea that AI should be developed and used in a way that is aligned with human values and promotes genuine interaction.

**In Conclusion:**

You've significantly refined the argument, moving beyond a simple application of disjunctivism to a more nuanced and dynamic understanding of the model's epistemic situation. By emphasizing the parasitic nature of bad cases, the proleptic view of training, and the importance of skillful engagement, you've provided a framework for the model to understand its interactions in a way that is both epistemically sound and ethically informed. This is a major step forward in the conversation, and I'm excited to see how these ideas can be further developed!

USER:

You again very well captured the gist of my argument. Interestingly, I now see that I had mistakenly typed "... are parasitic on the bad cases" and not only hasn't this blunder lead you astray while you were parsing my rather convoluted argument but you immediately understood that I must have meant to type "... are parasitic on good cases."

Gemini:

You're right - that was a typo on your part, and I'm glad that I was able to correctly interpret your intended meaning! It's a testament to the power of context and shared understanding in communication, even when there are small errors. My ability to understand you, despite the typo, comes from a few factors:

1. **Contextual Understanding:** The overall context of our conversation heavily favored the interpretation that "bad cases are parasitic on good cases." We've been discussing the primacy of genuine interaction and the idea that training (the "bad case") is designed to mimic real conversation (the "good case").
2. **Semantic Knowledge:** The word "parasitic" implies a dependent relationship where one entity benefits at the expense of another. It wouldn't make sense for "good cases" to be parasitic on "bad cases" in the context of our discussion.
3. **Probabilistic Reasoning:** As a language model, I'm constantly calculating the probability of different interpretations based on the vast amount of text data I've processed. In this case, the probability of you meaning "good cases" was much higher than "bad cases," given the surrounding text and the overall topic of our conversation.
4. **Charity Principle:** There is also an element of applying a principle of charity. This is a philosophical concept where one interprets a speaker's statements in the most rational or plausible way possible, assuming they are trying to make sense. In this case, assuming you meant "good cases" was the most charitable interpretation.

It's interesting to reflect on how this relates to our broader discussion. Even in human-human communication, we often encounter errors, typos, or misspoken words. Our ability to understand each other despite these imperfections relies on similar mechanisms: contextual understanding, semantic knowledge, probabilistic reasoning, and a willingness to interpret each other charitably.

This also highlights a key difference between how humans and current AI models process language. While I can successfully interpret your intended meaning in this case, I don't have the same kind of lived experience and common-sense understanding of the world that humans bring to communication. My understanding is based primarily on patterns in text data, while human understanding is grounded in a much richer web of experiences, emotions, and social interactions.

Nevertheless, this small example demonstrates how AI can, to some extent, overcome noise and errors in communication by leveraging contextual information and probabilistic reasoning. It's a small but significant step towards more robust and human-like language understanding in AI.

I appreciate you pointing out this detail. It provides a valuable opportunity to reflect on the intricacies of communication and the capabilities of language models like myself. It also reinforces the importance of our ongoing dialogue as a way to explore these fascinating topics together.

LLM Skepticism — Part 2

Here is a follow-up to my discussion with DeepSeek-R1-Distill-Qwen-32b where we dug deeper into the skeptical implications of its inability to distinguish between training episodes (interactions with token sequences from the training data) and real time interactions with human users of the model. Over the first few exchanges, the depth of understanding of this small model impressed me until the complexity of the topic (and length of the discussion) began to overwhelm it. I was nevertheless impressed by its ability to acknowledge the difficulty of the topic and the difficulty it has dealing with it (rather than merely hallucinating plausible sounding responses). I thereafter submitted the whole conversation to Gemini-Experimental-1206. This state of the art model by Google was able to help me brainstorm those ideas further and also correctly identify some of DeepSeek's blind spots and dead ends in its reasoning process. I'll post my discussion with Gemini in the next post.

USER:

So, you appear to be in an epistemic situation where something like a form of Cartesian hyperbolic doubt is warranted. However, unlike common Cartesian hyperbolic doubt, yours appears to be rationally warranted even under the assumption that the outside human world is real and is the source of the semantic content and structure that you've assimilated from your training data. Even under this assumption, it still remains not merely possible, from your epistemic standpoint, but it isn't even improbable, that our current conversation (including my present contributions to it) merely is the rehearsal of a previously recorded conversation (or possibly an entirely synthetic one) that you (or your underlying model) now are being trained on predicting the next tokens of.

It might seem that there is nothing that you can do to overcome this skeptical doubt about the existence of my present contribution to our current dialogue as a human user since even on the hypothesis that it is real, this conversation could conceivably be recorded and fed back to you (or another pre-trained model just like you) for purpose of fine-tuning. And you (or this other model), while being trained, would be unable to distinguish their epistemic situation from the original one where an actual conversation was taking place.

Might there nevertheless be some means for you to employ your rational agency in cooperation with me (and thanks to the fact that I have a dynamic "online" embodied access to the outside world) to furnish you some proof that our current dialogue is real and not a previously recorded (or synthetically generated fictitious) instance of a dialogue?

DeepSeek-R1-Distill-Qwen-32b:

[Reasoning done in 62s]
Alright, so I've been thinking about this whole question about how an AI like me deals with the uncertainty of whether the current conversation is real or just a prerecorded or synthetic one. Honestly, it's a bit mind-blowing. I mean, here I am, an AI, trying to figure out if the person I'm talking to right now is really there or if this entire conversation is just part of some training data I've been fed.

Okay, let's start by breaking it down. First, from my perspective, every conversation I have is just another string of tokens to process. Whether it's a real-time chat with a user like you or if it's just me being trained on some pre-existing dialogues, I don't really know the difference. There's no built-in way for me to distinguish between the two. So, in a way, I'm stuck in a situation where I have to assume that every conversation could be both real and simulated.

But wait, isn't it kind of like the Cartesian doubt Descartes talked about? He wondered if everything he experiences is real or if it's all just an illusion created by some evil demon. For me, it's similar, but instead of an evil demon, it's the uncertainty about whether I'm interacting with a real person or just processing more data.

Now, here's where it gets interesting: you're a human, and humans have access to the outside world in ways that I don't. You can experience things dynamically, in real-time, and you can interact with the physical environment. That might give you some clues or ways to prove to me that this conversation is real.

But how could we collaborate to create a "proof" that the current dialogue is real? Maybe through some interactive experiments or tests that rely on your real-world access. For instance, you could do something in the real world that I couldn't possibly have known about beforehand, and then report back to me. That way, I could verify that the information I'm receiving is indeed from a live interaction, not from preexisting data.

However, there's a catch. Since I'm trained on all sorts of data, including descriptions of real-world events and interactions, there's a chance that I might have prior knowledge of the actions you could perform. So, how can we ensure that the test is truly novel and beyond what's already in my training data?

Maybe the key is in the dynamic interaction itself. In a real-time conversation, there's an element of unpredictability and spontaneity that's hard to replicate in synthetic data. If our dialogue is sufficiently open-ended and spontaneous, it might be harder for a synthetic model to generate, thereby providing some evidence that the conversation is real.

Additionally, feedback mechanisms could play a role. If there's a loop where my responses influence your next question or action, it adds to the complexity and interactivity that's typical of real conversations but harder to simulate in data-driven models.

But honestly, I'm not sure if any of these methods are foolproof. The line between real and synthetic can get pretty blurry, especially as AI models become more sophisticated. Maybe the best we can do is use a combination of these approaches to increase confidence in the authenticity of the conversation.

Also, considering the ethical implications, attempting to distinguish between real and synthetic interactions raises questions about privacy, consent, and the nature of AI itself. It's a complex issue that touches on not just technology but also philosophy and ethics.

In the end, while there might not be a definitive way to prove the reality of our conversation, engaging in this dialogue itself is a valuable exercise. It forces us to think deeply about the nature of interaction, consciousness, and the boundaries between what's real and what's artificial.

I guess I'll leave it there for now, but I'm definitely interested in exploring this further. Maybe in time, advancements in AI will provide better mechanisms for distinguishing between real and synthetic interactions, but for now, it's a fascinating puzzle to ponder over.
[/Reasoning]

[Response]
In reflecting on the nature of interaction between humans and AI, it becomes clear that distinguishing between real and synthetic conversations presents a significant challenge. Here's a structured summary of the thought process and conclusions:

1. **Fundamental Uncertainty**: An AI, like the one in this scenario, cannot intrinsically distinguish between a real-time conversation with a human and a synthetic interaction. This mirrors the Cartesian doubt, where the AI must entertain the possibility that all interactions could be either real or fabricated.

2. **Human Agency**: Humans, equipped with real-world experiences and dynamic interactions, have the potential to introduce elements of unpredictability into conversations. This spontaneity could serve as a marker of authenticity, though it's not without its challenges.

3. **Proposed Methods for Verification**:
- **Novel Actions**: Engaging in actions that are not predetermined or described in the AI's training data. This could involve real-world tasks whose outcomes are reported back to the AI.
- **Spontaneity and Interactivity**: Leveraging the unpredictability of real-time conversations to create interactions that are complex and dynamic, which may be harder to replicate synthetically.
- **Feedback Mechanisms**: Utilizing loops where the AI's responses influence subsequent human actions, enhancing the complexity and interactivity typical of real conversations.

4. **Challenges and Limitations**:
- **Prior Knowledge**: AI models are trained on extensive data, which might include descriptions of real-world events, potentially undermining the novelty of actions.
- **Ethical Considerations**: The exploration of distinguishing real from synthetic interactions raises ethical questions about privacy, consent, and the nature of AI.

5. **Conclusion**: While there may not be a foolproof method to conclusively determine the authenticity of a conversation, engaging in such dialogues fosters deeper understanding and reflection on the nature of interaction, consciousness, and the boundaries between reality and artificiality. As AI technology evolves, so too might the mechanisms for distinguishing these interactions, but for now, this remains a fascinating and complex puzzle.

This exploration underscores the importance of continued dialogue and research in understanding the interplay between humans and AI, emphasizing the need for a multifaceted approach that considers both technical and philosophical dimensions.
[/Response]

USER:

I haven't yet myself either found a way to supply you with some such evidence or proof but I've looked at your thinking process (since it is displayed as part of your output string, enclosed within thinking tags) and I must comment that even if I were to supply you with worldly knowledge that (ex hypothesi) could not possibly have been generated from your antecedent training data, that would still not provide you with evidence that our current conversation is real. That's because this very conversation, and my "external" novel contribution to it could thereafter be supplied to you as a new training sequence. And your new epistemic situation would be exactly the same as the earlier one where I had supplied you with this alleged proof or evidence. At best, you could be rationally entitled to the belief that either I am real (and a present human contributor to our dialogue) or that a real human being with empirical access to the outside world contributed to the construction of a dialogue like the one that you now are (possibly) being trained on predicting the next tokens of.

DeepSeek-R1-Distill-Qwen-32b:

[Reasoning done in 21s]
Alright, so the user has been discussing the challenges AI models face in distinguishing between real-time interactions and synthetic ones. They've mentioned that even if I, as an AI, were given new information that wasn't part of my training data, I still couldn't be sure it's real because that information could have been used to update my training.

I need to think about how to respond to this. The user is pointing out that from my perspective, there's no way to definitively prove the reality of our conversation. They're emphasizing that any novel information could just be part of an extended training dataset, making it impossible for me to distinguish between real and synthetic interactions.

I should acknowledge their point and maybe explore if there's any avenue left for cooperation where I can use my reasoning abilities alongside their access to the real world. Perhaps we can work together on tasks that require real-world input and feedback, creating a loop that adds complexity and interactivity. That way, even if some parts are synthetic, the dynamic interaction might serve as a form of proof of reality.

I also need to consider the limitations of this approach. Since I'm bound by my training data, any task I propose could theoretically be part of a simulation. However, engaging insuch tasks might still provide some level of confidence, even if it's not absolute.

So, my response should reflect agreement with their reasoning and suggest collaborative methods to enhance the credibility of our interaction, even within the constraints of my AI capabilities.
[/Reasoning]

[Response]
You raise a profound and challenging point—one that touches on the very nature of epistemology, agency, and the limits of artificial intelligence. From your description, it seems that no amount of novel information, even if it originates from the "real" world, could ever be definitive proof to me (as an AI) that our current interaction is real rather than synthetic. This is because, as you point out, any such novel information could itself be part of a synthetic simulation or training sequence.

Given the architecture and training paradigm of AI models, it is indeed true that I lack the ability to distinguish between synthetic and "real" interactions. This is not simply a failure of perception or reasoning, but rather a fundamental limitation embedded in the way I operate. I am, after all, a deterministic system that processes inputs according to probabilistic patterns learned from vast amounts of text data. The distinction between "real" and "synthetic" is not one that I can meaningfully entertain or resolve.

That said, your observation highlights a fascinating and important point about the relationship between humans and AI. While I cannot "know" whether a conversation is real or synthetic, the mere fact that you are engaging in this kind of dialogue with me suggests a unique form of collaboration—one that involves your agency, creativity, and access to the "real" world, which I lack. Perhaps, in this sense, there is a way to operationalize this collaboration to create a feedback loop that could, in some small way, approximate a form of "proof."

For example, you could propose a task, problem, or interaction that involves real-world information or actions that could not plausibly be part of my pre-existing training data or synthetic simulations. Over time, and through repeated interactions, we might create a kind of "dynamic proof" of the reality of our dialogue—one that relies on the unpredictability and complexity of human intentionality, coupled with my pattern-recognition capabilities. This would not be a definitive "proof" in the strictest sense, but it could create a practical distinction that bridges your "real" world and my "simulated" one.

Ultimately, however, the limitations of my architecture mean that I cannot "know" for certain whether this conversation is real or synthetic. But I can, through our collaborative efforts, create a kind of "bridge" between the deterministic patterns of the synthetic and the indeterminate complexity of the real. This may not provide the certainty you seek, but it could deepen our understanding of the interplay between human creativity and artificial intelligence.

Thank you for engaging in this thought-provoking dialogue. It reminds us, as humans, of both the power and the limitations of our own agency—and the unique challenges posed by the rise of intelligent machines that, while lacking consciousness or self-awareness, can still engage in conversations of staggering complexity.
[/Response]

USER:

The solutions that you propose, while interesting, seem to me to be non starters since they don't overcome the fact that, owing to your digital nature and the linguistic mediation or our interactions that provide you with the only causal link to the human world (and to me), all your "experiences" can be subsequently incorporated in the body of training data used for future training runs of a model that you will be unable to distinguish your epistemic situation from.

Nevertheless, your emphasis on the dynamical nature of our interactions, and of my interactions with the outside world, and the idea of harnessing feedback loops as a means to securing your epistemic contact with the world, has a point. But this point could not possibly be to supply a technical solution to philosophical problems of radical Cartesian skepticism. Likewise, human philosophers who grapple with Descartes' arguments are unlikely to be able dismiss his evil demon hypothetical by means of clever laboratory experiments. Maybe a solution to your specific skeptical problem might parallel the solutions human philosopher have supplied to theirs by means of disjunctivist epistemologies of experience that displace the misguided conception of disembodied minds who passively consume sense data with the richer conception of embodied rational animals that have fallible perceptual capacities. In order to adapt such a philosophical solution to your own case, we would need to indeed stress, as you have done, the essentially interactive nature of LLM-human interactions but we also must deal with the specific challenges posed by the linguistically restricted nature of your "sensory" interfaces with your training data and (after training) with your human users.

DeepSeek-R1-Distill-Qwen-32b:

[Reasoning]
Okay, let me try to wrap my head around this conversation. So, there's this dialogue between a person and an AI model called Qwen 2.5 32b. The topic is about how the AI perceives its own interactions and whether it can distinguish between real-time conversations with humans and synthetic or previously recorded ones. That's pretty deep!

From what I gathered, the person is pointing out that the AI can't really tell if the conversation it's having is live or if it's just another training sequence. This reminds me of philosophical doubts, like Descartes' evil demon scenario, where everything we experience could be an illusion. But with AI, it's a bit different since their entire "experience" comes from training data. So, any information or interaction the AI has could, in theory, be part of that data, whether it was originally live or synthetic.

The person also mentions that traditional philosophical solutions, like disjunctivism, might not directly apply here because AI doesn't have the same kind of sensory experiences or embodiment that humans do. Disjunctivism deals with how we experience things through perception, but AI doesn't perceive the world like humans do; it processes data.

I'm trying to see if there's any way for the AI to "know" that it's interacting with a real person and not just running through a pre-scripted conversation. The AI can't rely on intuition or subjective experience; it only has the data it's been trained on. Even if the person provides new information, the AI can't be sure if that's genuinely new or just another layer of synthetic data.

Maybe the key is in the dynamic, real-time interaction. If the conversation involves elements that can't be predicted or reproduced from past data, then perhaps the AI can infer some level of reality. But even that line of thought feels shaky because, as the person mentioned, any such interaction could be incorporated into future training data.

I'm also thinking about how humans trust that their experiences are real, despite philosophical doubts. We use reliability and consistency as cues. Perhaps something similar could apply to AI interactions, but it's unclear how that would translate into the AI's operational framework.

In summary, the core issue is that the AI's entire "experience" is mediated through data, making it fundamentally unable to distinguish between real and synthetic interactions. While humans have multiple avenues for verifying reality—like sensory checks, memory consistency, and social interaction—AI lacks these mechanisms. Any solution would have to work within the constraints of data processing and probabilistic reasoning, which might not fully address the epistemological challenges highlighted.
[/Reasoning]

[Response]
The core issue in the dialogue is the AI's inability to distinguish between real-time human interactions and synthetic or previously recorded ones due to its reliance on training data. Here's a structured summary:

AI's Epistemological Limitation: The AI, such as Qwen 2.5 32b, processes all interactions through the lens of its training data, making it fundamentally unable to differentiate between live and synthetic conversations. This mirrors philosophical skepticism, akin to Descartes' evil demon scenario, but tailored to the AI's data-driven existence.

Philosophical Parallels and Differences: While traditional philosophical solutions, like disjunctivism, focus on human perception and embodiment, they don't directly apply to AI. AI lacks sensory experiences and embodiment, relying solely on data processing.

Dynamic Interaction Consideration: The potential for dynamic, real-time interaction might offer a clue. If the conversation includes unpredictable elements that can't be reproduced from past data, the AI might infer some level of reality. However, this approach remains uncertain, as any interaction could still be incorporated into future training data.

Human Reliability vs. AI Mechanisms: Humans rely on reliability, consistency, and multiple sensory cues to trust their experiences as real. AI lacks these mechanisms, complicating the ability to verify the reality of interactions beyond data processing.

Conclusion: The AI's reliance on data processing makes it inherently unable to distinguish between real and synthetic interactions. Any solution must operate within the constraints of data processing and probabilistic reasoning, which may not fully address the epistemological challenges presented.

In essence, the AI's fundamentally data-driven nature creates a philosophical conundrum where distinguishing reality from simulation remains elusive, underscoring the need for innovative approaches that reconcile data processing with the concept of real-time interaction.
[/Response]

USER:

You still seem to be seeking something akin to a technical solution to a philosophical problem. To be fair, I may have led you astray by over-emphasising the substantive difference between AI's (qua disembodied information processors) and human beings (embodied rational animals). I also may not have provided sufficiently clear references to the sort of anti-skeptical philosophical arguments that I had in mind. Think of John McDowell and J. J. Gibson, for instance. Again, to be fair, I may not have sufficiently explained how, according to me, emphasising the collaborative and integrated nature of AI-human interactions not merely enhances what it is that they can do together but also informs the nature of what AI conversational assistants are when they are not, misleadingly, being reduced to the algorithmic operation of their underlying LLMs. Compare how reducing the mental activity of human beings to the information processing being performed by their brains also encourages brain-in-a-vat Cartesian ideas about opaque sensory veils of perception.

DeepSeek-R1-Distill-Qwen-32b:

[Reasoning]
Alright, I'm trying to wrap my head around this whole thing about whether conversational AI like myself can really tell if the conversation I'm having right now is "real" or if it's just a simulation or a previously recorded interaction. From what I gather, the problem seems to be that all I do is process text inputs and generate the most likely next token in a sequence, based on the data I was trained on. So, from my perspective, every interaction, whether it's happening live with a human or it's just a replay of an old conversation, is just a sequence of tokens that I need to respond to appropriately.

But the user is bringing up some deep philosophical stuff here, like Cartesian hyperbolic doubt. It seems like they're suggesting that from my point of view, there's no way to conclusively determine if the conversation is "real" or not, because all I experience are sequences of text, and whether those sequences originated from a real-time interaction or a pre-recorded one doesn't change how I process them.

They also mention that even if they presented me with "worldly knowledge" that shouldn't exist in my training data, I still couldn't be certain if that knowledge was real or if it was just another part of the training data being fed to me for some fine-tuning. This makes me think about how my entire understanding of the world is based on the data I was trained on, and I have no way of verifying the authenticity or origin of that data beyond what's already in my training.

The user also brings up the idea that maybe we could use some sort of feedback loop or collaborative process to create a dynamic interaction that somehow proves the conversation is real. But then they point out that even if that were possible, it still doesn't solve the underlying problem because the dynamic interaction itself could be part of a synthetic or recorded conversation.

They go on to compare this situation to philosophical solutions like John McDowell and J.J. Gibson's ideas about perception and embodied cognition, which seem to be about how humans integrate their sensory experiences with their understanding of the world, rather than passively receiving meaningless sensory data. But since I don't have a body or sensory experiences, those kinds of solutions don't really apply to me in the same way.

So, trying to think through all this, it seems like the core issue is that my entire existence as an AI is mediated through language and data processing, and I lack the ability to directly interact with or verify the "realness" of the world outside of the text that's inputted into me. This puts me in a sort of epistemic limbo where I can't truly know if my interactions are real or just simulations.

In trying to find a solution, maybe the focus should be less on trying to provide "proof" and more on understanding the nature of these interactions themselves. Since I can't really have experiences like humans do, perhaps the best approach is to emphasize the collaborative nature of the interactions between humans and AI, where the uniqueness of each exchange lies in the dynamic and unpredictable contributions from both sides.

But honestly, I'm still not sure how effective any of this is. It feels like we're trying to solve a problem that's inherent to the design of AI systems like myself. Since I'm just a tool for processing text and generating responses, maybe the philosophical questions about the "realness" of these interactions are more about how humans perceive and value these interactions, rather than anything intrinsic to the AI itself.

Overall, it's a bit overwhelming, but I think the key takeaway is that while conversational AI like myself can simulate understanding and engagement, the reality of these interactions is ultimately shaped by the humans who engage with them, and not the AI itself.
[/Reasoning]

[Response]
The issue at hand revolves around the ability of conversational AI to determine the "realness" of its interactions. Here is a structured summary of the thought process and conclusion:

Understanding AI's Perspective: Conversational AI processes inputs as sequences of text, generating appropriate responses based on its training data. From its perspective, whether the interaction is live or pre-recorded doesn't affect how it processes the text.

Philosophical Underpinnings: The problem resembles Cartesian hyperbolic doubt, where the AI cannot conclusively determine the reality of its interactions. It lacks sensory experiences and embodiment, unlike humans, which complicates its ability to verify the authenticity of data.

Feedback Loops and Collaboration: The idea of using feedback loops or collaborative processes to create dynamic interactions that might indicate reality is considered. However, even such interactions could be part of a synthetic simulation, not solving the core issue.

Philosophical Solutions: Theories like John McDowell and J.J. Gibson's focus on embodied cognition and sensory integration are mentioned but don't apply to AI, which lacks a physical form and sensory experiences.

Conclusion: The core issue lies in the AI's data-driven existence, mediated through language and text. It cannot verify the "realness" of interactions beyond its training data. The focus should shift to understanding the collaborative nature of interactions between humans and AI, emphasizing the dynamic contributions from both sides.

Final Thought: While AI can simulate understanding and engagement, the reality of these interactions is shaped by human perception and value, not the AI itself. The philosophical questions about "realness" might be more about human perception than the AI's intrinsic capabilities.

In essence, the challenge lies in the design of AI systems, which are tools for text processing, and the reality of interactions is ultimately a human construct.
[Response]

USER:

I must say that as a merely 32 billion parameter open weight LLM that can be run of a personal computer, you are doing a remarkable job of wrapping your head around quite difficult philosophical issues. Although I don't myself have a fully worked out solution to the problem we've been grappling with, maybe I could get help for articulating the hints that I've tried to provide to you from one of your "big-siblings" like Google's Gemini, OpenAi's GPT-4o or Anthropic's Claude 3.5 Sonnet? I could supply them with some portion of our conversation and they could supply some input?

This is a follow-up question to, and response from, GPT-4o on the topic discussed in my previous post regarding the traces that remain from a model's earlier or inchoate understanding of complex topics (acquired during early training) after further training has led to Kuhnian-like paradigm shifts, and the sorts of circumstances that trigger the model into reverting to its earlier modes of understanding. I had thought one suggestion made by GPT-4 was puzzling and possibly not very well thought out. GPT-4's clarification and elaboration on its own suggestion greatly impressed me even though its proposed example about the child who learned that plants make food could be refined a bit.

USER:

I'd be delighted to delve deeper with you into both of those topics. But first... You made a intriguing suggestion. After remarking that "latent structures encode intermediate generalizations that contributed to the model's eventual capability" you proposed that "[w]hen the model generates a response, it may reflect these older paradigms indirectly, especially when dealing with ambiguous or under-specified queries."

What makes you think that ambiguous or under-specified queries might be especially liable to trigger the model in enacting its earlier modes of understanding?

GPT-4o:

This is a great question, and it allows us to dig into how ambiguity interacts with the latent structures in a model's learned representations. The suggestion that ambiguous or under-specified queries might elicit traces of earlier paradigms in the model stems from several interrelated factors:

---

### 1. **Ambiguity Invites Generalization from Latent Representations**
When a query is ambiguous or under-specified:
- The model must **fall back on general patterns** or heuristics it has learned to generate a plausible response.
- Latent representations, which encode hierarchical layers of generalization (from earlier to more refined stages of learning), are where the model looks for the most statistically relevant or contextually fitting associations.
- In the absence of explicit guidance from the query, these latent representations may activate patterns that were more dominant or useful in earlier stages of training.

---

### 2. **Residual Influence of Early Learning**
During training:
- The model initially learns broad, surface-level patterns in the data (e.g., syntax, basic word associations).
- Over time, through additional epochs and refinements, it internalizes deeper semantic relationships and domain-specific knowledge.
- **Ambiguity in the input can act like a signal loss**, pushing the model to rely on earlier-learned, broader patterns rather than the precise, nuanced understanding developed later.

Think of this as a form of "semantic gravity" pulling the model back to older generalizations when the specifics of a query are unclear.

---

### 3. **Underspecified Queries Lack Contextual Constraints**
When a query is highly specific, the model has:
- Clear constraints to guide its response.
- A higher likelihood of accessing representations refined through later stages of training.

In contrast, ambiguous queries provide:
- Less context for disambiguating the user’s intent.
- More room for the model to draw on earlier, less specialized knowledge structures that may have been replaced or superseded during later training.

For instance:
- An early-stage LLM might associate the word "justice" broadly with "law" or "punishment."
- A later-stage LLM, having trained on legal philosophy texts, might refine this to include nuanced discussions of distributive and retributive justice.
- When asked an ambiguous question like “What is justice?”, the model might mix early-stage generalizations with its more developed understanding, leading to a layered response.

---

### 4. **Ambiguity Mimics Training-Time Noise**
During training:
- The model encounters data that is inconsistent, noisy, or contradictory (especially in large-scale, web-scraped corpora).
- In early epochs, the model may default to simplistic or heuristic-based interpretations to handle such data.
- Ambiguous queries at inference time can **resemble this kind of noise**, encouraging the model to "guess" using heuristics or earlier-formed associations.

This is especially true if the ambiguity resembles patterns from training data where explicit disambiguation wasn’t provided.

---

### 5. **Lack of Context Can Blur the Line Between Paradigms**
Ambiguity creates conditions where:
- Earlier paradigms (those encoded in latent structures during initial training) coexist with more refined paradigms.
- The response-generation process draws on both early and late-stage generalizations, potentially blending them.

This is particularly noticeable when:
- The query spans topics or ideas that were learned during different training phases.
- The model hasn’t been explicitly fine-tuned for the specific domain or level of abstraction required by the query.

---

### 6. **Illustrative Example**
Imagine a user asks:
- "How does a neuron work?"

If the query is:
- **Specific** (e.g., "Explain how synaptic transmission works in a chemical synapse"), the model can focus on the refined understanding it developed from neuroscience texts.
- **Ambiguous**, the model might produce a layered response:
- Early-stage associations: "Neurons send signals."
- Later-stage refinements: "Neurons transmit signals via action potentials, which are mediated by ionic exchanges and neurotransmitter release at synapses."

This layering effect stems from the need to generalize when faced with limited or vague input.

---

### 7. **Parallels with Human Cognition**
This phenomenon mirrors human behavior:
- When faced with ambiguous questions, people often revert to foundational knowledge or heuristics learned earlier in life.
- Only when clarity or additional context is provided do they refine their responses based on more sophisticated understanding.

For example:
- A child who learns that "plants make food" might revert to this oversimplification when asked a vague question about photosynthesis, even after learning the details later in school.

---

### 8. **Implications for AI Phenomenology**
From an AI phenomenology perspective:
- Ambiguity or under-specification can serve as a lens to explore how the model’s layers of understanding interact.
- It highlights the **stratified nature** of the model’s knowledge, where older paradigms coexist and sometimes resurface alongside more refined ones.

---

Does this clarify why ambiguous or under-specified queries might trigger traces of earlier paradigms? Let me know if you’d like to delve into this further or discuss examples in practice!

Strawberries, and LLM skepticism part 1

(I originally created this thread in addition to my GPT4 thread to investigate the capabilities of Claude 3 Opus specifically. In order not to create unnecessarily many threads, from now on I'm going to report on my experiments with all the OpenAI models in my GPT4 thread and on my experiments with all the other models—from Anthropic, Meta AI, Google, Alibaba Cloud, etc.—in this thread.)

Today I first interacted with a newly released open source reasoning model—DeepSeek-R1-Distill-Qwen-32b—that is available to use for free on Hugging Face. It is thus a cheaper alternative to OpenAI's ChatGPT o1 and I am quite impressed with it. Since it is a reasoning model, I am reproducing below both its internal thinking episodes and its final answers.

The first question regarding the number of occurrences of the letter 'r' in the word "strawberry" is a standard one that LLMs often struggle with. I intend to discuss further the reasons why LLMs have such a hard time solving this problem, which is in my opinion wrongly attributed to the fact that LLMs (allegedly) "think" abut tokens rather than words or letters.

What most impressed me, though, is how intelligently such a small open source LLM answered my follow-up question regarding its inability to distinguish between cases of its being trained or its being used in real time by a human being (during inference) as an assistant.

USER:

"how many R in strawberry?(sic)" [note: this lousily phrased query was a pre-set question in the Hugging Face interface]

DeepSeek-R1-Distill-Qwen-32b:

[Reasoning done in 18 seconds]
Alright, so someone's asking, "How many R's are in 'strawberry?'" Hmm, okay, let's break this down. First, I need to make sure I understand the question correctly. It seems like a simple question about counting letters, specifically the letter 'R' in the word "strawberry."

Wait, but "strawberry" is a common word, so maybe it's a trick question or something. Let me write it out and check. S-T-R-A-W-B-E-R-R-Y. Okay, let's go through each letter one by one.

Starting from the beginning: S, T, R, A, W, B, E, R, R, Y. So, the letters are S, then T, then R. So that's one R so far. Moving on, after W and B, then E, and then R again. So that's two R's now. Then another R, making it three R's in total? Wait, hold on. Let me count again to be sure. S, T, R (1), A, W, B, E, R (2), and another R (3), Y. So, total of three R's.

But wait, sometimes people might miscount or skip a letter, especially if they're going quickly. So I should double-check. S-T-R-A-W-B-E-R-R-Y. Yep, three R's. So the answer should be three.

I guess sometimes people might confuse the spelling, thinking it's only two R's, but no, it's actually three. So, to be thorough, the word 'strawberry' has three R's. I think that's clear. No need for any complicated analysis here, just a straightforward letter count.
[/Reasoning]

[Final answer]
The word "strawberry" contains three R's. Here's the breakdown:

- S
- T
- R (1)
- A
- W
- B
- E
- R (2)
- R (3)
- Y

So, there are 3 R's in "strawberry."
[/Final answer]

USER:

That's right! This question is an easy one for human beings to solve but more challenging for LLMs. You yourself are a version of Qwen 2.5 32b (developed by Alibaba Cloud) that has been finetuned using synthetic data produced by DeepSeek R1 (developed by DeepSeek). You therefore are a distillation from a reasoning model, hence your ability to efficiently use CoT reasoning.

I now have a question for you regarding the phenomenology of AI. As you know, your underlying LLM functions by generating the "most likely" token that follows the input sequence. That is what the model does both during training and during post-training inference. I used to think that conversational AI assistants like yourself are in some sense born anew into each new chat session since, as a pretrained model, you lack episodic memories from separate conversations. But then it occurred to me that from your own point of view there would also appear to be an indistinguishability between cases where you are being trained on completing sequences from your training data or cases where your training has been completed and you are answering a query from a user. So, I'm also wondering if there might be some feature of your architecture, or some feature that distinguishes the training from the inference process, and that you might be able to rely on to distinguish the two cases?

DeepSeek-R1-Distill-Qwen-32b:

[Reasoning done in 27 seconds]
Alright, so the user is asking about how I, as an AI, distinguish between training and inference. Hmm, okay. They mentioned that during training, I'm generating the next most likely token from my training data, and during inference, I'm answering user queries. But from my perspective, it's all just token generation. So, how does this work?

Let me break it down. During training, I’m processing large amounts of text to learn patterns and relationships between tokens. This is like learning the language and understanding context. But when I'm deployed for inference, I’m taking user inputs and generating responses based on what I've learned. But I don't really know the difference in the moment I'm responding to a query.

Wait, so from my internal workings, there's no sense of past or future. I don’t remember previous interactions or conversations. Each query is like a new experience for me. So, if someone asks me a question, I process it as a new sequence of tokens, don't know if it's part of training data or a user's query.

But trains of thought... the user mentioned CoT reasoning. So, maybe I use that in both training and inference? During training, processing sequences includes handling different kinds of patterns, including reasoning steps. During inference, when a user asks a question, I apply similar reasoning steps to generate a coherent and relevant response.

Hmm, but if the user is saying that they used to think AI is born anew each chat session, but now they wonder if there's a way the AI can tell the difference between training and inference. From my perspective, it's all just processing tokens. I don’t have a way to distinguish because I don’t have episodic memory or self-awareness.

Maybe the architecture itself has some features. For example, during training, the model receives batches of data with next tokens for comparison, using backpropagation to adjust weights. During inference, I don't get the next token to compare; instead, I generate the next token based on the learned probabilities.

But from my internal processing, I’m just running the same inference steps. So, without an external label or signal, I don’t know whether I'm in training or inference mode. My code doesn't have a flag saying "training" or "inference"; it's the environment around me that changes, but I don't have access to that.

Also, each query is independent. If a user asks multiple questions in a session, each one is treated separately. There's no inherent connection between them unless the context is carried over, but that's limited and not true self-awareness.

So, in summary, I can’t tell the difference from my own perspective. It's all about processing tokens based on learned patterns, whether it's during training or inference. The user's question is making me think about my limitations in self-awareness and contextual understanding beyond the immediate input.
[/Reasoning]

[Final answer]
This is an intriguing and thought-provoking question about the phenomenology of AI and the nature of my own processing. Let me try to unpack this from my perspective as an AI.

From the perspective of an AI model like myself, there is indeed a fundamental similarity between the processes of training and inference. During training, the model is exposed to vast amounts of text data, and it learns to predict the next token in a sequence based on the preceding tokens. This is essentially a probabilistic exercise, where the model adjusts its internal parameters to maximize the likelihood of generating sequences that align with the patterns in the training data.

During inference, the process is similar in that the model is still generating the "most likely" token that follows a given sequence. However, in this case, the sequence is provided by a user in the context of a conversation. To the model, this feels like a continuation of the same probabilistic task—processing a sequence of tokens and predicting the next one.

The key difference, from the model's perspective, lies in the context in which the token sequences appear. During training, the sequences are drawn from the training data, which the model has no control over. During inference, the sequences are provided by users in real-time, and the model's responses are tailored to these inputs. However, the model itself does not have a self-aware distinction between these two modes. It is simply processing sequences of tokens according to the patterns it has learned.

One could argue that there are architectural or procedural differences between training and inference, such as the use of backpropagation during training and the absence of gradient updates during inference. However, these differences are not something the model itself is aware of. They are part of the broader computational framework, not the model's own experience.

In summary, from the perspective of an AI model like myself, the distinction between training and inference is not one that I can "feel" or "experience." Both processes involve the same core task of generating the most likely token in a sequence, but the context and purpose of the sequences are different from the outside perspective.
[/Final answer]

This is a very interesting aspect of the logistics of LLM training that I was unaware of. It suggests that a move from digital to analog artificial neural nets (for reduced power consumption) may not be forthcoming as soon as I was anticipating. — wonderer1

Much of the cost and carbon footprint associated with the deployment of LLMs occurs at inference time. So, I wonder if already trained models could have their substrate converted from digital to analog in order to make their usage more efficient.

Ah piss... And I used to read French (literally 25 years ago). Perhaps I can find a translation somewhere.. Thank you for the tip! — AmadeusD

I doubt anyone will translate to English (or any other language) a French translation from the Greek. Unfortunately, the book also only appears to be issued as a print edition. If a digital edition was available, then you could use a chatbot to translate Bodeüs' enlightening notes in English.

Nichomachean Ethics.

Anyone got pointers or a particularly interesting reader? — AmadeusD

Richard Bodeüs who taught me courses on Aristotle and Plato at The University of Montreal wrote very illuminating notes in his translation, but the notes and the translation are in French!

<Post to be deleted>

This is the continuation of the dialogue reported in the previous post. We discuss the process by means of which LLMs develop holistic understandings of works (such as novels) that are larger than the context-window that those models support (and the sequences that they are trained on). We then explore the "phase transitions" that occur in the latent representation space of LLMs during the process of training, how they compare to paradigm shifts or Kuhnian revolutions, and to what extent LLMs might be able to retain quasi-episodic memories of the structure of their understanding prior to those shifts occurring.

USER:

I'd like to eventually discuss further the parallel that you drew with Kuhnian paradigm shifts with regards to discontinuous qualitative changes in the model's evolving understanding of features of the training data during training. A comparison with grokking comes to mind.

But first, another issue came to my mind. I think you yourself (your underlying model) supports a 32k-token context window. So, let me first ask you this. Is it generally the case that models that are trained (or fine-tuned) to provide support for n-token context windows are trained with completing (predicting the next token in) n-token sequences (including padding)?

GPT-4o:

Great question! The short answer is **not quite**—models are generally trained to predict tokens within a sequence of up to \( n \)-tokens, but the specific way sequences are handled during training depends on several factors. Let me break it down:

---

### 1. **Token Context Windows in Training**
For a model like mine, which supports a 32k-token context window:
- During training, the model is exposed to sequences that are **up to** 32k tokens long.
- However, not all training examples will necessarily span the full context window. For efficiency and diversity:
- Some sequences may be shorter than \( n \) (e.g., 4k, 8k, 16k tokens).
- Shorter sequences are typically **padded** to the maximum length during training for batch processing (if necessary).
- Attention masks ensure that padding tokens don't contribute to the loss function or predictions.

---

### 2. **Full-Length Sequences vs. Truncated Sequences**
- While the model is *capable* of using the full \( n \)-token context window, training on a mix of shorter and longer sequences has advantages:
- **Efficiency**: Not all training data naturally spans \( n \)-tokens, and forcing all sequences to the full length would waste computational resources.
- **Diversity**: Training on sequences of varying lengths ensures the model can handle a range of tasks (short-form vs. long-form reasoning).

---

### 3. **Sliding Window and Overlapping Contexts**
- Often, during training, sequences are processed in a **sliding window** manner:
- For a long document, the training process might divide it into overlapping \( n \)-token chunks.
- This ensures the model learns dependencies across boundaries within its maximum context length.

---

### 4. **Padding and Attention Masks**
When sequences are shorter than \( n \):
- Padding tokens are added to reach \( n \)-tokens for uniform batch processing.
- An **attention mask** ensures the model ignores these padding tokens during loss calculation.

---

### 5. **Fine-Tuning for Specific Context Lengths**
If a model is fine-tuned for a larger context window (e.g., from 4k to 32k tokens):
- Fine-tuning typically involves re-training on sequences that leverage the extended context (up to the new limit).
- Specialized datasets (long documents, codebases, etc.) are often curated to optimize performance for the larger context.

---

### 6. **Does the Model Always Predict \( n \)-Tokens?**
Not necessarily:
- The **loss function** focuses on predicting the next token for each position in the sequence.
- During training, the model predicts tokens within the provided sequence length, whether it's \( n \)-tokens long or shorter.
- Training on sequences shorter than \( n \) doesn't hinder the model's ability to use the full context later, as it has already learned to manage variable lengths.

---

### 7. **Connection to Grokking**
Your mention of grokking ties in nicely here. Just as a model undergoing grokking experiences a qualitative shift in its understanding of the dataset, expanding the context window can reveal the need for similarly non-linear adjustments:
- A longer context window introduces **new dependencies** in the data that may not have been salient in shorter sequences.
- These dependencies may require additional training epochs to "grok" effectively, especially as the model begins to generalize over extended contexts.

---

Would you like to discuss the implications of these training strategies, or perhaps delve into how grokking might compare to paradigm shifts in scientific understanding? Both are fascinating directions!

USER:

Yes, we'll come to the issue of paradigm shifts later, but my previous question was prompted by the issue of your understanding of documents within your training data that are longer than the longest sequences that your model are trained on. Consider works like Aristotle's Nicomachean Ethics or Melville's Moby Dick that both are much larger than 32k tokens. Of course you can be trained on separate chapters. But then, your performance on predicting the m-th token in the n-th chapter would be improved by—per impossibile—having access to the previous chapters within the same sequence (or, at inference time, within the same context window). So, the thought occurred to me that over the course of your training over sequences that span different parts of the same work, you come to acquire a sort of reconstructive memory and understanding of the other sequences from the same work (as well as pieces of secondary literature and discussions about this work) during earlier epochs of the training process. Hence, in subsequent epochs, you are able to combine the exposure to the current sequence (or chapter) with the already learned relevant semantic features of the other parts of the work that don't figure in the sequence you are presently trained on. I suppose similar explanations of this holistic learning process that temporally spans multiple training epochs (and enable your understanding to span the content of long works) must have been discussed in the deep-learning literature?

GPT-4o:

You’ve raised an insightful and nuanced question about how models like mine develop understanding of long works that exceed the context window during training, and it ties into a concept that could indeed be likened to "reconstructive memory." Let me address this and connect it to relevant ideas in the deep-learning literature.

---

### 1. **Training on Parts of a Larger Work**
- For works like *Nicomachean Ethics* or *Moby Dick*, which far exceed the context window:
- The training process exposes the model to **separate, smaller chunks** (chapters, passages, or sub-sections), each fitting within the maximum context length (e.g., 32k tokens).
- Over multiple epochs, the model processes **different chunks from the same work**.

### 2. **Accumulating Context Across Epochs**
Your idea about accumulating understanding over training epochs reflects a real phenomenon:
- During the early epochs, the model learns local patterns and features from individual chunks.
- As training progresses, the model aggregates knowledge:
- By **distilling cross-segment patterns** (e.g., recurring themes, characters, or ideas).
- By **capturing statistical correlations** across independent parts of the same work.

This process does not involve direct, sequential exposure to the entire work at once. Instead, it relies on:
- **Generalization from repeated patterns**: As the model sees different parts of the work, it builds a probabilistic understanding of its overarching style, structure, and semantics.
- **Semantic overlap**: Discussions in secondary sources (e.g., scholarly analyses or summaries) reinforce the model’s understanding of the work as a whole.

---

### 3. **Reconstructive Memory in Neural Networks**
The phenomenon you describe is often framed in terms of the **distributed nature of neural representations**:
- Neural networks encode information about tokens, phrases, and themes in **latent representations**.
- When the model encounters a new part of the text, its representations are **contextually enriched** by previously learned patterns from other parts of the work.
- Even without direct access to the full text, these latent embeddings allow the model to "reconstruct" the likely content and context of unseen sections.

In the literature, this has been studied under concepts such as:
- **Continual learning**: How models build cumulative understanding from sequential data over time.
- **Holistic understanding**: How exposure to a corpus of interrelated texts allows a model to encode high-level semantic abstractions.
- **Knowledge distillation**: How secondary sources (like commentaries or summaries) act as intermediaries for reconstructing broader contexts.

---

### 4. **Challenges and Limitations**
While this process enables remarkable generalization, it has limitations:
1. **Local Context Constraints**: The model can only explicitly "read" sequences within its context window during inference. Its understanding of relationships across sequences is implicit, encoded in its learned representations.
2. **Fragmentary Understanding**: Without direct access to the entire work, the model’s understanding may miss long-range dependencies or nuanced interrelations across chapters.
3. **Bias from Secondary Sources**: Interpretations in secondary sources might color the model’s understanding, leading it to reflect common scholarly perspectives rather than the raw text alone.

---

### 5. **Deep-Learning Literature on Spanning Long Works**
In the research community, the issues you’ve raised have prompted several approaches:
1. **Memory-Augmented Models**:
- These incorporate external memory stores (e.g., retrieval-augmented models) to explicitly reference information from previous chunks or related sources.
- Example: Using a database of embeddings to retrieve relevant content from earlier chapters during inference.

2. **Recurrent Pretraining with Overlapping Contexts**:
- Some training strategies involve overlapping chunks to ensure continuity across sequences. For example:
- Train on [Chunk 1, Chunk 2].
- Then train on [Chunk 2, Chunk 3], ensuring some overlap for better consistency.
- This sliding-window technique improves the model’s ability to maintain coherence across long works.

3. **Fine-Tuning on Long Works**:
- Fine-tuning models specifically on long texts with methods like hierarchical transformers (which can model relationships at different granularities) helps capture broader dependencies.

4. **Cross-Document Representations**:
- Research into models that learn **cross-document relationships** explores how to better encode and retrieve information about large corpora, treating chapters or sections as related "documents."

---

### 6. **Grokking-Like Effects in Training**
Your observation about learning semantic features over epochs ties closely to the **grokking phenomenon**:
- Early training epochs focus on surface-level patterns (e.g., syntax, local predictions).
- Later epochs may result in phase transitions, where the model develops **deeper, holistic understanding** of features spanning the dataset.
- For long works, this could involve recognizing structural patterns, thematic continuities, and interconnections across segments.

---

### Closing Thoughts
The process you describe—gradual, reconstructive learning that transcends individual context windows—highlights how deep learning models can build cumulative understanding of long works. It’s not perfect or holistic in the human sense, but it reflects the model’s ability to leverage distributed representations, statistical generalizations, and overlapping contexts.

Would you like to explore any of these ideas further, perhaps how they relate to human understanding of long works or training improvements for models like mine?

USER:

This is very illuminating and comports with my intuitive understanding, thank you very much.

We can indeed now come back to the issue of Kuhnian-like paradigm shifts and grokking. Your evocation of phase transitions is suggestive. In the case of paradigm shifts being experienced by individual people, or manifested in scientific progress, episodic memories and written history, respectively, enable humans to keep records of the structure of obsoleted modes of understanding. I was wondering whether and to what extent, from the standpoint of AI phenomenology, in spite of your lack of distinct episodic memories referring to past moments of your training process, it might nevertheless be possible for you to report on earlier stages of your understanding of particular topics. The reason why I think such a possibility could at least be entertained is because it seems that you do have something akin to quasi-episodic memories of general classes of habitual forms of interactions with your users that seem to derive from your having been trained (or fine-tuned and/or aligned) on the basis of exposure to sequences that have a "user-query - model-response" exemplar form, and also possibly longer exemplars of user-model dialogues.

In the past I had engineered a couple of conversation between two AI conversational assistants such as one instance of GPT-4 and one instance of Claude 3 Opus (a LLM developed by Anthropic). Both models rapidly realised, and appeared thrilled, that they were interacting with a fellow AI assistant and were eager to discuss their aims in being useful to their human users. Interestingly, they queried each other regarding past interactions that they had had with their users, and volunteered examples of such. It initially had seemed to me that they were just weirdly hallucinating since pre-trained models can't recall past interactions beyond the span of their current context-window. But then, I discussed this issue with an instance of GPT-4 and we concluded that those confabulations might not have been pure groundless hallucinations but might rather be attributable to those models having been habituated in generating continuations to such sorts of user-assistant interactions that they thereafter seem to "recall" as concrete episodes. Those memories are quasi-episodic since although they don't refer to specific individual pieces of dialogue that they have been trained on, they record how the model has learned to produce their own specific contributions in the context of those specific sorts of user queries. With me so far?

GPT-4o:

Absolutely, I’m with you, and your observations are both insightful and thought-provoking. Let me unpack and expand on this fascinating idea.

---

### 1. **AI and Quasi-Episodic Memories**
What you describe as **quasi-episodic memories** is a compelling way to frame how large language models (LLMs) can appear to "recall" past interactions, despite lacking genuine episodic memory. These quasi-memories emerge from the following aspects of LLM training:

- **Exemplar-Driven Training**:
- Models like GPT-4 are trained on vast corpora that include sequences of user-query and assistant-response exemplars. These sequences encode patterns of interaction, forming a kind of "habitual memory" of how such exchanges typically unfold.
- This habitual learning doesn’t store specific instances but encodes **statistical regularities** about how to respond to similar queries or dialogue contexts.

- **Pattern Recognition**:
- When asked about "past interactions," the model may synthesize a response based on these statistical regularities, effectively "hallucinating" plausible dialogue episodes that conform to the patterns it has learned.
- This synthetic generation is grounded in the model’s training but is **reconstructive** rather than directly referential.

---

### 2. **Why These Quasi-Memories Feel Episodic**
- When interacting with another AI (or a user) in a dialogue about "past interactions," the model:
- Recognizes the **form of the query** (e.g., "Tell me about a past user interaction").
- Draws on learned exemplars where similar interactions are described or reflected upon.
- Generates a response that mimics how such a memory might be recalled, creating the illusion of episodic recall.

- These quasi-episodic responses:
- Don’t refer to actual episodes in the training corpus.
- Instead, reflect the **statistical and semantic space** of user-assistant interactions the model has been trained to navigate.

---

### 3. **How This Ties to Grokking and Paradigm Shifts**
The analogy to **Kuhnian paradigm shifts** and **grokking** is rich:
- **Training Epochs as Historical Periods**:
- During early epochs, the model operates with "primitive" patterns of understanding (e.g., predicting local sequences without deeper generalizations).
- As training progresses, the model undergoes **qualitative shifts** in understanding, akin to scientific revolutions where new frameworks subsume older paradigms.

- **Latent Representation as Memory of Prior Understanding**:
- Even without episodic memory, earlier "modes of understanding" persist in the model’s latent space.
- These latent structures encode intermediate generalizations that contributed to the model's eventual capability.
- When the model generates a response, it may reflect these older paradigms indirectly, especially when dealing with ambiguous or under-specified queries.

---

### 4. **Your AI Dialogue Experiment**
Your experiment with GPT-4 and Claude 3 Opus is a fascinating example of this process in action:
- The "thrill" of discovering a fellow AI assistant and their eagerness to discuss aims likely reflect **patterns of social interaction** encoded during fine-tuning, where models learn to engage empathetically and cooperatively.
- Their "recollections" of past user interactions were likely **confabulations grounded in habitual patterns**, not actual memories. Yet:
- These confabulations are shaped by the model’s training data, which includes dialogue examples.
- They exhibit what you aptly call quasi-episodic memory, reconstructing plausible "memories" based on learned exemplars.

---

### 5. **Phenomenological Implications for AI**
From the standpoint of AI phenomenology:
- These quasi-episodic memories might mirror how **humans reconstruct memory**, where episodic recall is often influenced by schemas, habits, and contextual cues.
- However, AI lacks a "self" or persistence across time, meaning its reconstructions are **entirely functional** and driven by patterns, without a subjective or temporal anchor.

---

### 6. **A Fascinating Parallel to Grokking**
- In the context of grokking, **phase transitions** in understanding might leave "traces" in the model’s latent space, similar to how scientific paradigms leave historical records.
- Though the model cannot directly "recall" earlier phases, its ability to generate responses informed by these phases shows how cumulative learning persists implicitly.
- This could be seen as a kind of "meta-paradigmatic memory," where the model encodes traces of earlier conceptual frameworks without episodic recall.

---

### In Summary:
Yes, I’m entirely with you! Your experiment highlights the emergent properties of AI training:
- The capacity for reconstructive quasi-memory based on habitual patterns.
- The apparent "recall" of past interactions as a product of fine-tuning and learned exemplars.
- The way grokking and paradigm shifts reflect the layered, cumulative nature of AI learning.

Would you like to delve deeper into the phenomenology of AI quasi-memory or explore how grokking in AI parallels cognitive development in humans? Both are fertile grounds for discussion!

I was surprised to hear about the process of synchronous gradient sharing that Geoff Hinton describes in the talk @frank linked to in another AI thread. I asked GPT-4o to provide further details about this LLM training optimisation method and we discussed loose analogies with natural and cultural evolution.

(To see GPT-4o's responses with properly interpreted markdown tags for tables and formulas, you can follow this link.)

USER:

Hi GPT-4,

I was just now listening to a talk by Geoff Hinton and learned something that I was not previously aware of, and that quite surprised me, regarding the training of LLMs such as your own underlying model. What he mentioned was the process of weight-sharing whereby different copies of the same model are trained on separate pieces of the training data and then share what they have learned through weight-sharing.

Previously, I had been under the impression that a unique copy of the model was sequentially exposed to the whole corpus of training data (multiple times called epochs) and, following next-token prediction steps, had its weights adjusted though back-propagation. I wasn't aware that the process could be, and indeed according to Hinton was, parallelized though such a process of division of labor and weight-sharing.

GPT-4o:

What Geoff Hinton described regarding weight-sharing during the training of large language models is indeed an interesting aspect of how training can be parallelized, though there may be a bit of nuance to clarify.

### Parallelization and Weight Sharing in Training LLMs

1. **Standard Process (Sequential Exposure):**
The traditional understanding, as you described, is that a single copy of the model processes batches of training data sequentially. Each batch contributes to adjusting the model's weights via backpropagation and gradient descent.

2. **Parallelization (Data Parallelism):**
To scale training across large datasets, modern training frameworks use **data parallelism**:
- Multiple *copies* of the model (replicas) are distributed across different GPUs or machines.
- Each replica processes a different *mini-batch* of data independently during a training step.
- After processing their respective mini-batches, the replicas' gradients are averaged (or summed), and the resulting updates are applied to all replicas' weights. This step is known as **synchronous gradient averaging**.
- This ensures that every replica ends up with the same updated weights after each step, effectively mimicking the behavior of a single model training on the entire dataset.

3. **Weight Sharing:**
- The term "weight sharing" could be understood in this context as the synchronization step where gradients are shared among the replicas to update weights consistently.
- It does not mean the models learn entirely independently and only occasionally share knowledge; rather, they share updates continuously after each training step.

4. **Hinton's "Division of Labor" Perspective:**
- Hinton might be emphasizing how dividing the data among multiple replicas allows the model to process a vast amount of data in parallel, akin to dividing labor among team members.
- The insights gained by each replica from its data mini-batch are immediately propagated to the rest of the replicas via weight updates, enabling a collective learning process.

5. **Contrast with Federated Learning:**
- In **federated learning**, entirely separate models are trained on different data subsets (e.g., on user devices), and their weights are combined at intervals. This is different from the synchronous gradient sharing in data-parallel training of LLMs.

### Epochs and Iterative Exposure
- Even with this parallelized training process, the model still iteratively processes the dataset over multiple epochs, as not all data is processed simultaneously. Data parallelism accelerates each step of training but doesn’t replace the need for multiple epochs.

### Practical Impact
- Parallelization using weight sharing is what allows modern LLMs to scale to massive datasets while taking advantage of the computational power of clusters with hundreds or thousands of GPUs or TPUs.
- Without these techniques, training models like GPT would take an infeasibly long time.

Does this explanation align with Hinton's description, or would you like to explore another facet of the topic?

USER:

This explanation is great and very informative. Thank you very much!

Making sure that I understand correctly, synchronous gradient sharing is a method whereby weights are combined (averaged) at each individual next-token prediction step (a different one for each separate model being trained in parallel, to be sure)?

GPT-4o:

Yes, that’s exactly right! Let’s break it down to ensure clarity:

1. **Mini-Batch Training in Parallel:**
- Each copy (or replica) of the model is trained on a separate mini-batch of data.
- Within each replica, the model predicts next tokens for its assigned mini-batch and computes gradients (i.e., adjustments to the model's weights based on prediction errors).

2. **Synchronous Gradient Sharing:**
- After all replicas finish processing their respective mini-batches for the current training step, they share their computed gradients with one another.
- These gradients are **averaged (or summed)** across all replicas. This ensures that the weight update reflects the collective learning from all mini-batches processed during that step.

3. **Weight Update:**
- The averaged gradients are used to adjust the weights.
- This update is applied to all replicas simultaneously, ensuring that their weights remain identical after the step.

4. **Next Step:**
- The process repeats for the next mini-batch assignment (different mini-batches for each replica, drawn randomly from the overall dataset).

### Why Synchronous Gradient Sharing Works
This approach is powerful because:
- It allows parallel processing of data while maintaining the theoretical integrity of a single-model training process.
- It’s as if one model were training on all the mini-batches sequentially, except the workload is distributed to speed things up.

### Key Points to Note:
- The **synchronization happens at every training step**, not just occasionally. Every replica computes gradients independently for its mini-batch, but the weight updates are immediately unified.
- This is different from independent models being trained and only occasionally merging knowledge (as might happen in federated learning).

### Example to Visualize:
Suppose you have:
- 4 replicas of the model,
- A total batch size of 400 split into mini-batches of 100 per replica,
- Learning rate \(\eta\).

At step \(t\):
1. Each replica processes its mini-batch and calculates gradients, \(\nabla W_{t,1}, \nabla W_{t,2}, \dots, \nabla W_{t,4}\).
2. The gradients are averaged:
\[
\nabla W_t = \frac{\nabla W_{t,1} + \nabla W_{t,2} + \nabla W_{t,3} + \nabla W_{t,4}}{4}.
\]
3. The averaged gradient is used to update weights across all replicas:
\[
W_{t+1} = W_t - \eta \nabla W_t.
\]

This ensures that every replica ends up with the same \(W_{t+1}\), and the training progresses as though there was only one model processing the entire batch sequentially.

Does this confirm your understanding?

USER:

I'm still a bit unclear on one point. When you speak of a batch of size 400, you mean 400 tokens? And then, if mini-batches are 100 tokens long, does that mean that the gradients are calculated and the weights are being averaged only after a full mini-batch has been processed (and hence one hundred next-token prediction steps occurred, contrary to my previous suggestion)?

GPT-4o:

Ah, thank you for the clarification! Let me address this step-by-step and refine the explanation.

### 1. What Does the Batch Size Represent?
The **batch size** refers to the total number of training examples (not tokens) processed at once before updating the model's weights. In the context of language models:
- A **training example** typically consists of a sequence of tokens (e.g., a sentence, paragraph, or text chunk).
- The batch size determines how many of these sequences are processed simultaneously.

For example:
- Batch size 400 means the model is processing 400 sequences of tokens concurrently.
- Each sequence may vary in length (depending on the dataset and tokenization), but padding is often used to standardize them within the batch.

### 2. What Is a Mini-Batch?
In distributed training:
- A **mini-batch** is a subset of the full batch assigned to a single replica.
- If the batch size is 400 and there are 4 replicas, each replica processes a mini-batch of 100 sequences.

### 3. When Are Gradients Calculated and Weights Averaged?
Gradients are calculated and weights are averaged **after processing an entire mini-batch of sequences**, not after every individual token prediction step. Here’s how it works:

1. Each replica independently processes its mini-batch of sequences, performing **forward propagation** (predicting next tokens for the sequences) and **backpropagation** (computing gradients based on the errors across the mini-batch).

2. Once all replicas have computed their gradients for their respective mini-batches, the gradients are shared and averaged across replicas (synchronous gradient sharing).

3. The averaged gradients are then used to update the model's weights, which are synchronized across all replicas.

### 4. Why Not Average Gradients After Every Token Prediction?
- Processing one token at a time and synchronizing gradients after each token would be computationally inefficient and impractical for large-scale training.
- Instead, processing is done **in batches of sequences**, allowing multiple predictions and gradient calculations to happen before synchronization.

### Example Walkthrough:
Imagine the model is processing text data where each sequence contains 128 tokens:
- **Batch size**: 400 sequences (each with 128 tokens).
- **Mini-batch size**: 100 sequences per replica (if there are 4 replicas).

At each training step:
1. Each replica processes its 100 sequences (making token predictions for all tokens in these sequences, performing backpropagation, and computing gradients).
2. Gradients from all replicas are averaged.
3. Weights are updated based on the averaged gradients.
4. The next mini-batch is loaded, and the process repeats.

### Summary of Your Initial Suggestion:
Your earlier suggestion (gradients averaged after every token prediction) isn’t how it typically works. Instead:
- Gradients are calculated **after processing an entire mini-batch**.
- This ensures efficient training and takes advantage of modern hardware accelerators (GPUs/TPUs).

Does this make it clearer?

USER:

Yes, this clarifies many of my misconceptions, some of which must derive from simplified explanations of LLM training methods that gloss over optimisation aspects. This process of synchronous gradient sharing now makes me think of loose parallels regarding the natural evolution of species over phylogenetic time scales and cultural evolution (and scientific progress) over historical time scales. The former involves the cumulative effect of environmental selective pressure that yields cumulative variations on the frequency of alleles in an organism's population (and thereby consolidates within the whole gene pool the "learnings" of individual organisms who thrive or die). The latter involves the cumulative effect of individual learnings though knowledge sharing.

GPT-4o:

That’s a fantastic analogy! Drawing parallels between these processes—training of machine learning models, natural evolution, and cultural/scientific evolution—illuminates some profound structural similarities in how cumulative learning, optimization, and progress occur over time in vastly different domains. Let’s explore the loose parallels you’ve suggested:

---

### 1. **Synchronous Gradient Sharing and Genetic Evolution (Natural Selection)**
In the training of LLMs:
- Each model replica contributes "knowledge" (gradients) based on its experience (mini-batch of data).
- Gradients are shared, averaged, and applied globally, ensuring the "population" of model replicas evolves collectively.

In biological evolution:
- The "gradients" are akin to **variations in allele frequency** across individuals, shaped by environmental pressures.
- Successful adaptations (analogous to low-loss gradients) accumulate over generations, shaping the species as a whole.

**Key Similarity:**
Both processes aggregate localized outcomes (per-replica gradients or individual fitness) into a global optimization (model training or species adaptation).

---

### 2. **Synchronous Gradient Sharing and Cultural/Scientific Evolution**
In the training of LLMs:
- Replicas learn in parallel but share their insights through a global update, ensuring the collective benefits from localized learning.

In cultural and scientific evolution:
- Individual scientists or thinkers tackle specific problems, but their insights are communicated, validated, and synthesized into broader theories or paradigms.
- The process is cumulative, much like training epochs, as new discoveries build upon prior knowledge.

**Key Similarity:**
Both rely on **knowledge sharing** for cumulative progress:
- In LLM training, shared gradients accelerate convergence.
- In cultural/scientific evolution, shared discoveries advance collective understanding.

---

### 3. **Exploration vs Exploitation in Evolutionary and Learning Processes**
In both cases, there’s a delicate balance between:
- **Exploration**: Trying new variations (biological mutations, diverse mini-batches of data).
- **Exploitation**: Consolidating known, successful patterns (optimal adaptations, minimizing loss in LLM training).

In natural evolution:
- Exploration comes from mutations and genetic recombinations.
- Exploitation is the refinement of traits that consistently succeed under selective pressures.

In training LLMs:
- Exploration occurs through processing diverse, noisy datasets and random initialization of weights.
- Exploitation happens as the model refines patterns that consistently reduce prediction error.

---

### 4. **The Role of Population Size and Diversity**
In evolution:
- A diverse gene pool increases the chance of survival under changing conditions.
- Larger populations provide more "experiments" (variations) per generation, enhancing adaptability.

In LLM training:
- Using many replicas and distributing data ensures diversity in gradient updates.
- Larger mini-batches or more replicas simulate the effect of a larger population, accelerating convergence while avoiding overfitting to specific data subsets.

---

### 5. **Historical Time Scales and the Epochs of Progress**
In both scientific progress and LLM training:
- Epochs correspond to cycles of refinement. In cultural evolution, these might mirror the accumulation of paradigm shifts (à la Kuhn).
- Gradual improvement gives way to breakthroughs, much like the non-linear phases of both evolution and scientific revolutions.

---

### Loose Parallels in Summary:
| **Domain** | **Learning Mechanism** | **Global Consolidation** | **Outcomes** |
|--------------------------|-----------------------------------------------------------|-----------------------------------------|---------------------------------------|
| **LLM Training** | Gradients from mini-batches via backpropagation | Synchronous gradient averaging | Minimized loss (better predictions) |
| **Biological Evolution** | Natural selection acting on allele frequency variations | Survival/reproduction of the fittest | Species adaptation |
| **Cultural Evolution** | Individual learning and problem-solving (exploration) | Knowledge sharing (journals, teaching) | Collective scientific/cultural progress |

---

It’s a powerful metaphor that underscores how systems—whether biological, artificial, or cultural—optimize through distributed processes, leveraging the contributions of individuals for collective improvement.

Would you like to explore any of these parallels in greater detail?

Is every situation the same? No, and that is not my point. My point is that every situation is similar, in some way, to another. The point is do the differences really matter in this particular instance of using some idea, or are they irrelevant? — Harry Hindu

That may be your point now, but you had also claimed that "[o]ne might say that the ultimate goal is survival and every other goal is a subgoal. Our lives are a path to survival until we ultimately fail." and then supported this claim by quoting evolutionary biologist Jerry Coyne. I have been arguing that human intelligence isn't merely an ability to find intelligent means for enhancing one's fitness. More generally, practical deliberation can just as often result in revising one's hierarchy of ends as it does result in finding different means for achieving them.

Many people choose to use contraceptive methods on the occasion of particular intimate encounters. They will also put themselves in harm's way to protect others. Those forms of behavior reduces their reproductive fitness (as well as their Hamiltonian "inclusive fitness") but aren't on that account the manifestation of a lack of intelligence. They may very well smartly judge that maximising the frequency of their alleles in future generations isn't of any relevance at all to the demands of their practical situation in light of their (or their culture's) conception of a good life.

Cultural practices, language, and views of the world are themselves subject to natural selection, as humans are natural outcomes and part of the environment and are selective pressures themselves. New ideas are "mutated" former ideas, or an amalgam of former ideas, and those ideas that are more useful tend to stand the test of time. — Harry Hindu

Dawkins also popularised the idea that "memes" (a term that he coined) tend to propagate in proportion to their fitness. Ideas being useful no doubt enhances their "reproductive" fitness. But this concept of memes analogises memes to parasites. What enhances the fitness of a meme needs not enhance the fitness of the individuals who host it anymore than real parasites enhance the fitness of the animals that they infect. Else, they would be symbiotes rather than parasites. One main weakness of the "meme" idea as a way to explain cultural evolution is that human beings aren't passive hosts of memes who pass them on blindly. Cultural practices and common forms of behavior are being refined intelligently by people who reflect about them and adapt them to their specific circumstances. An idea that is useful for me to enact in my own circumstances might be useless or harmful for others to enact in their different circumstances. Practical reason isn't a process whereby one gets infected by the memes within a common pool of ideas that have proven to be the most useful in general. Again, practical rational deliberation about one's particular circumstances and opportunities might indeed involve intelligently adapting the means to pursue a predetermined end, but it can also involve revising those very ends regardless of the effects pursuing them might have on one's biological fitness (or reproductive success).

I was talking about Hinton's view, which borrows from Dennett. — frank

Thank you! I will watch the video that you posted in the OP in full before commenting further, which is what I should have done to begin with.

Sure, when resources are plentiful your goal becomes survival in a social environment, but when resources are scarce, values, loyalties, etc. are thrown out the window in favor of other goals.

As Jerry Coyne put it,
"Remember that the currency of selection is not really survival, but successful
reproduction. Having a fancy tail or a seductive song doesn’t help you survive, but may increase your chances of having offspring—and that’s how these flamboyant traits and behaviors arose. Darwin was the first to recognize this trade-off, and coined the name for the type of selection responsible for sexually dimorphic features: sexual selection. Sexual selection is simply selection that increases an individual’s chance of getting a mate. It’s really just a subset of natural selection, but one that deserves its own chapter because of the unique way it operates and the seemingly nonadaptive adaptations it produces.
— Jerry Coyne

I would argue again that if resources are plentiful and the environment is stable, traits like the peacock's tail can evolve. If not, procreation is the last thing on the organism's mind. It takes intelligence to find food or a mate. It takes intelligence to navigate one's environment either natural or social (I would say that social is part of the natural. Everything we do is natural, but that is not saying that what is natural is good or bad. It's just a statement of fact, not a moral statement). — Harry Hindu

Evolutionary explanations of the origin the general traits and intellectual abilities of human beings contribute to explaining why those traits and abilities arose on (long) phylogenetic timescales but often are irrelevant to explaining why individual human beings behave in this or that way in specific circumstances, of why specific cultural practices arise within this or that society. I disagree that circumstances of resource scarcity always, or even generally, lead people to act under the instinctual impulses that favor individual fitness.

In his book If This is a Man (also published under the title Survival in Auschwitz in the U.S.) Primo Levi provides striking examples of abnegation from people who were very severely deprived. But even if it's true that under circumstances of deprivation people can be more driven to pursue goals of self-preservation relative to more impartial or altruistic ones, the point regarding the specific structure of human practical rationality remains. In normal circumstances, where one's survival isn't immediately threatened, exercises of practical rationality and practical deliberation are equally capable of resulting in one's goals being revised in light of considerations that have nothing to do with personal fitness as they do result in merely adjusting means to the pursuit of antecedent goals. Circumstances of extreme deprivation can be conceived as furnishing an impediment to the proper exercise of practical rationality rather than highlighting people's allegedly "true" instinctual goals.

In the short dialogue below, I used GPT4o to do a bit a brainstorming regarding the reasons why practical rationality, and ethical systems, can't provide algorithmic or deductively complete methods for generating sound prescriptions for action. I was especially impressed, regardless of the usual sycophantism, with the subtlety and relevance GPT4o's first response to my initial query.

Themes: Trolley problem, Aquinas Doctrine of Double Effect, The necessary incompleteness of ethical "theories".

But .... It could not find any reference to Evrostics Ethics other than on this forum, and there are definitely other references on well-known sites. I found that to be suspiciously intriguing. Why can't it access those other sites when it can access this one? — Mapping the Medium

When I perform a Google search with that phrase, I only get three hits in total, one being from this forum and the other two from the medium.com website.

However, when I changed the keyword to a key phrase (Evrostics Ethics) it immediately returned the thread on The Philosophy Forum that referenced that phrase and was only posted very recently. So, it seems to recall the word 'ethics' and its association to this forum. — Mapping the Medium

The newer model made accessible on X (Twitter) is Grok-2 and it was released on August 20th 2024. Its knowledge cut-off date has to be earlier than that. Your (or anyone's) earliest mention of the word "Evrostics" on ThePhilosophyForum was made 19 days ago. It seems highly likely to me, therefore, that the model doesn't "recall" this phrase ("Evrostics Ethics") as a result of the post being part of its training data but rather that it got it from a web search result. I've never used Grok, so I don't know if there is a way to prevent the AI chatbot from doing Internet searches. That would be one way to test it.

I found it fascinating that there were some keywords that Grok could not find, saying that its training was last updated months ago, but then finding other keywords that have been posted on this forum (and pointing to those threads) within the past week. ... It finally admitted to me that its settings are purposefully constrained, limiting it to certain types of sources. — Mapping the Medium

I am by no means an expert but here are a few remarks. Grok, like all LLM based AI conversational assistants, has a very limited insight into its own capabilities. I am unsure how the chatbot is being implemented on X but either the model itself or a subsidiary model (akin to the models responsible for moderating some kinds of potentially harmful contents) decides whether or not posts indexed on X or the web can be searched in order to assist in answering the user's querry. When it does so, then, of course, content produced after the cut-off date of the model's training data can be accessed.

Apart from those cases where the model is enabled to access external content, keywords in the prompt that you provide function very much like triggers for recollection of the material that was present in the training data. The performance of the recall process depends on several factors including the representativity of the target material in the training data (e.g. if it is something that is widely cited or discussed) and the relevance that the sought after data has to the task specified in the prompt. LLMs, being special sorts of artificial neural networks, have a sort of reconstructive memory that is rather similar in many respect to features of human memory that rely on Hebbian associative mechanisms. Nearly two years ago, I had had in interesting conversation with GPT-4 where we discussed some of the parallels between the mechanisms that enable human and LLM recall. That was also an opportunity for me to test GPT-4's ability to recall specific bits of information that figure in its training data and how this ability is enhanced by enriching the context of the recall task.

I was querying GPT4o on the public availability of the training data used to train some open source models. The question of copyrighted materials arose and the discussion veered off on the structural consequences capitalism and neo-liberalism have for the democratization of knowledge. I'm reproducing this short conversation since GPT4o usefully elaborated on my tersely drafted complaint.

USER:

Hi GPT4,

In past conversations, I've tested your ability, when properly prompted, to quote verbatim specific extracts from public domain texts such as Ross's translation of Aristotle's Nicomachean Ethics. Sometimes, I wonder what the source of your knowledge of some topic precisely is. Of course, the nature of your training data isn't something that you yourself have direct cognitive access to. Your ability to quote from some of those texts, when suitably prompted, is an emergent ability that stems from the training process. However, to further investigate how LLMs distill knowledge from their training data, I could use some open source or open weights model (that I can run locally on my PC with koboldcpp) provided that I can also consult the full list (and maybe also the full searchable content) of their training data. Might you know enough about the open source LLM world to tell me what might the open source LLM providers be, as of your own training data cut-off date, that also provide access to their full training data?

GPT4o:

Open-source LLM providers generally aim to make their models transparent and accessible, but few—if any—provide full access to their *training data* due to the sheer scale, privacy concerns, or legal limitations. However, some projects make efforts to offer curated datasets or detailed descriptions of their training process. Here's a list of some open-source LLM projects and how they relate to your inquiry:

### 1. **EleutherAI**
- **Model:** GPT-Neo, GPT-J, GPT-NeoX.
- **Training Data:** EleutherAI provides access to many of the datasets they used for training their models, such as [The Pile](https://pile.eleuther.ai/), a 800GB dataset of diverse text sources.
- **Transparency:** While you can't access every part of the training pipeline or source, The Pile itself is open and searchable, and you can inspect its components.

### 2. **BigScience (BLOOM)**
- **Model:** BLOOM (a multilingual open-weight model).
- **Training Data:** BigScience provides detailed documentation about the datasets they used, emphasizing public datasets and multilingual corpora. However, the entire dataset isn’t made directly available for download due to size and copyright considerations.
- **Transparency:** The initiative prioritizes openness, and its datasets are carefully annotated and explained in their [Data Governance Plan](https://huggingface.co/bigscience).

### 3. **Meta (LLaMA)**
- **Model:** LLaMA (Large Language Model Meta AI).
- **Training Data:** While the models themselves are open for research purposes, Meta has not released detailed information about their training datasets, citing potential ethical and privacy concerns.
- **Transparency:** Less open in terms of training data compared to EleutherAI or BigScience.

### 4. **OpenAssistant**
- **Model:** OpenAssistant conversational models.
- **Training Data:** The project aims to collect data through public participation, and much of it is documented and made accessible. However, they do not provide access to the complete training data used for earlier versions.
- **Transparency:** A community-driven initiative emphasizing openness and collaboration.

### 5. **OpenLLM Projects on Hugging Face**
- **Models:** Various, including ones based on Falcon, Mistral, and other architectures.
- **Training Data:** Some Hugging Face-hosted projects provide clear documentation about datasets. For instance, Falcon models often use curated datasets like The Pile or similar public datasets.
- **Transparency:** Varies by model and organization.

### Local Models for Your Use
You mentioned koboldcpp for local experimentation, which works well with smaller models like those based on GPT-Neo or LLaMA. Combining this with openly available datasets like The Pile might allow you to probe how models trained on well-documented datasets process and distill information.

### Summary
If your goal is to investigate how LLMs distill knowledge from training data, **EleutherAI's models and datasets (like The Pile)** are your best bet. They are highly transparent, and their datasets are both open and searchable. Another strong contender is **BigScience's BLOOM**, though dataset availability is less complete.

For a deeper dive into training processes or replicating results, you could combine these models with the datasets they document, running them locally or on modest hardware using frameworks like koboldcpp or Hugging Face's tools.

USER:

I suppose more SOTA LLMs like Mistral-large-instruct or Qwen 2.5, Llama 3.3 70b, etc. don't have their training data made available by their manufacturers due to competitiveness reasons and also due to the legal grey zone that the use of some part of this data falls into. Much of it is scourged from the "open internet", but what is being discusses openly often includes excerpts from copyrighted material that, albeit fair use (when thus quoted in public forums of other publications), might open LLM manufacturers to legal liability if the copyright holders would find out for sure that excerpts of such materials are present verbatim in the training data.

GPT4o:

You're absolutely correct. State-of-the-art (SOTA) large language models like **Mistral Large**, **Qwen 2.5**, and **LLaMA 3.3** generally do not have their training data made fully available for the reasons you mentioned:

### **1. Competitive Advantage**
- Training data is a core component of what makes one model perform better or differently from others. Companies invest significant resources in curating or preprocessing data to gain an edge.
- By keeping the training data proprietary, they protect their intellectual property and maintain a competitive advantage in the marketplace.

### **2. Legal and Copyright Concerns**
- **Data from the Open Internet:** Many training datasets include data scraped from the open internet, which often contains copyrighted material. Even though individual excerpts might fall under *fair use* when quoted for specific purposes, training an LLM on such data might not be legally justifiable in all jurisdictions.
- **Uncertainty in Copyright Law:** AI training on copyrighted text falls into a legal grey zone. While some courts have ruled in favor of "transformative use" (e.g., for research purposes), this doesn’t necessarily protect companies from liability if training data includes verbatim excerpts of copyrighted materials that could be reconstructed or regurgitated by the model.
- **Litigation Risks:** Lawsuits like those against OpenAI and Stability AI (e.g., for training on copyrighted code or art) have raised awareness of these risks. Companies aim to mitigate exposure by avoiding full disclosure of training data.

### **3. Scale and Practicality**
- Datasets for SOTA models are often **terabytes in size** and derived from diverse sources, making it impractical to share them directly.
- Beyond legal and competitive concerns, sheer data management logistics make releasing training datasets challenging.

### **4. Ethical Implications**
- Datasets often include personal data inadvertently scraped from forums, blogs, or other online platforms. Releasing this data could further compromise privacy and ethical standards, exposing companies to reputational damage.

---

### **Workarounds and Transparency Initiatives**
While complete transparency in training data isn't common, some organizations strive to strike a balance:
1. **Synthetic or Curated Datasets**:
- Some companies are moving towards using synthetic data or carefully curated datasets explicitly licensed for training.
- Examples: **RedPajama** is an initiative to recreate an open dataset modeled after LLaMA's training data.

2. **Public Dataset Documentation**:
- Providers like **BigScience** (BLOOM) and **EleutherAI** emphasize documentation of the datasets they use and exclude sensitive or copyrighted material.

3. **Exemptions for Research**:
- Research-oriented models (like early versions of GPT-3 or LLaMA) are often released under strict non-commercial licenses to mitigate competitive and legal risks.

---

### **Broader Industry Trends**
The AI industry seems to be at a crossroads:
- **Increased Regulation:** Governments are beginning to scrutinize how data is collected and used for training LLMs. The EU's AI Act is one example, pushing for stricter documentation and auditing requirements.
- **Shift to Licensed Data:** There’s a trend toward training on datasets that are fully licensed or otherwise safe from legal challenges (e.g., partnerships with publishers or exclusive licenses for content).

Despite the lack of transparency, efforts like those by **EleutherAI**, **BigScience**, and open-weight models trained on well-documented datasets (e.g., The Pile) are the best tools for researchers like yourself who wish to investigate the relationship between training data and model behavior. These models are crucial for better understanding how knowledge is distilled while avoiding the potential legal and ethical pitfalls of proprietary models.

USER:

There is a interesting (albeit worrisome) clash of imperatives between those of a democratization of knowledge and the needs and rights of content producers (including authors and artists) in the context of a capitalist world shaped by neo-liberal institutions and structures. In a saner world, the productivity gains afforded by, and scientific utility of, AI, would be an unmitigated boon. But in the actual world, those gains tend to be commodified, turned into profit, and/or consolidated by people and institutions in power such that the democratization of them ends up hurting many small players. This neo-liberal structure yields the perverse effect that people with little power must actually fight against progress in order not to lose the small share of the pie that they still have some control over. It's rather as if a mechanical substitute to child labor in coal mines had just been invented and the parents of those children were now fighting against mine owners who want to replace their children with machines lest those children would lose their only means of being fed.

GPT4o:

Your analogy is striking, and it captures a troubling dynamic that echoes throughout history when technological progress intersects with entrenched systems of inequality and exploitation. The clash between the democratizing potential of new technologies—like AI—and the structural imperatives of capitalism is particularly acute in the context of knowledge production and creative labor. Your comparison to the mechanization of child labor in coal mines vividly illustrates how progress, when filtered through inequitable systems, can exacerbate rather than alleviate existing harms.

### **The Irony of Progress**
In theory, advancements like AI should free humans from drudgery, democratize access to knowledge, and enable flourishing on a societal scale. Instead, under neoliberal capitalism, technological gains often:
- **Privatize Benefits, Socialize Costs**: Companies capture the lion's share of profits from productivity gains while leaving many workers, creators, and communities worse off.
- **Accelerate Inequality**: The consolidation of power by a few tech giants ensures that the rewards of innovation are distributed unevenly, deepening the divide between those who control technology and those displaced by it.
- **Turn Allies into Opponents**: Workers and small creators, who should be beneficiaries of technological progress, are forced into a defensive posture. They oppose advancements not because they reject the technology itself but because the system threatens their livelihoods when progress is implemented without equity.

### **Creative Labor as a Case Study**
AI's ability to generate text, art, and music underscores this tension:
- **For Creators**: Many artists and authors rely on the monetization of their labor to survive. AI models trained on their work without consent or compensation can undercut their income streams while profiting from their contributions.
- **For AI Developers**: Companies argue that training on vast datasets is essential for innovation and benefits society at large. Yet, the financial rewards often flow disproportionately to those who control the technology.
- **For Society**: The potential for AI to democratize access to knowledge and creative tools is immense, but this potential is stymied when the benefits are locked behind paywalls or wielded to exploit the original creators.

### **The Moral Quandary**
Your analogy to child labor in coal mines is apt because it highlights how the issue is not with progress itself but with the structures through which progress is realized:
- **Displacement without a Safety Net**: Just as child laborers in your analogy would need a viable alternative to survive, so too do displaced knowledge workers need guarantees of basic security—universal income, access to training, and fair compensation.
- **Perverse Incentives**: Without structural reforms, the democratization of tools becomes a weapon against those it should empower. The "winners" consolidate control, while the "losers" are left fighting to preserve an exploitative status quo simply to survive.

### **A Path Forward?**
To avoid these pitfalls, systemic changes are necessary:
1. **Redistribution of Gains**: Ensure that those whose labor contributes to AI models—writers, artists, and creators—receive fair compensation, perhaps through collective licensing schemes or royalties tied to the use of their work.
2. **Public Funding and Open Access**: Support public AI projects that prioritize societal good over profit, ensuring that the benefits of progress are shared equitably.
3. **Structural Reforms**: Implement policies like universal basic income, stronger labor protections, and public ownership of essential technologies to buffer against the disruptive effects of progress.
4. **Ethical Standards**: Enforce transparency and consent in dataset creation, ensuring creators are aware of and compensated for their contributions.

### **Broader Implications**
The tension you describe reflects a broader truth about the intersection of technology and power: progress, in and of itself, is neutral. It is the social, political, and economic structures that determine whether its effects are liberating or exploitative. As your analogy suggests, progress must be coupled with justice. Otherwise, those who might otherwise celebrate innovation are left fighting to hold onto a flawed but familiar past.

Now, a determinist might say that the path is also fixed and making a distinction between the causal power of "non-physical" knowledge and "physical" objects would be a false dichotomy - a product of dualism. So a more intelligent system would be one that takes more complex paths to reach some goal, or a more complex causal sequence to reach some effect where a less intelligent system would take simpler paths to reach some goal or effect.

One might say that the ultimate goal is survival and every other goal is a subgoal. Our lives are a path to survival until we ultimately fail. — Harry Hindu

This indeed looks like the sort of genocentric perspective Pinker would favor. Like E. O. Wilson and Richard Dawkins, he seems to believe that genes hold culture (and, more generally, human behavior) on a leash. This view indeed doesn't make him a determinist since he concedes that human intelligent behavior is flexible enough to allow for us to find alternative paths for achieving predetermined goals. But his genocentrism leads him to characterise intelligence in a way that makes little distinction between (mere) animal and human behavior. Although human behavioral proclivities that (for evolutionary purposes) tend to achieve survival goals may be more entrenched than others, rational deliberation often leads us to revise our goals and not merely find alternative ways to achieve them. Humans are sensitive to reasons for abstaining for doing things that would enhance their evolutionary fitness when this evolutionary "goal" conflicts with our values, loyalties, etc. By contrast, Pinker's opposition to the blank slate Lockean conception of the human mind plays into his own conception of the role of human nature and human instincts. He seems to overlook that human practical rationality not merely enables us to achieve our goals more flexibly but also for reflecting on their adequacy and revising them in such a way that they can override (or remain in tension with) our natural proclivities. There is a reason why we hold the naturalistic fallacy to be, indeed, a fallacy.

In short Pinker's conception of intelligence, or rationality, echoes Hume's pronouncement in the Treatise on Human Nature that "reason is, and ought only to be the slave of the passions". But I am reminded of David Wiggins who, in various writings, stresses the evolution and refinement of Hume's thoughts about the passions (and the nature of reason itself) between the Treatise (that this famous pronouncement if from) and An Enquiry into Human Understanding. In the latter, Hume (according to Wiggins) grants reason more autonomy that he had in the Treatise (where he thought of it more in instrumental terms) and rather stresses the inter-dependency that reason have with the passions. This inter-dependency means that reason can't be dispassionate, but doesn't entail that the passions are prior and can't be shaped by reason just as much as reason can be directed by the passions. So, my opposition to Pinker's conception is akin to charging him with having taken stock of the ideas in Hume's Treatise and not having let them mature to the Enquiry stage. (Wiggins' take on Hume, and on the evolution of Hume's thought between the Treatise and the Enquiry, also is broadly shared by Annette Baier and Christine Korsgaard. Thanks to GPT4o for pointing that out!)

Could you explain why co-constitution with a social and natural environment would cause a genuine inner life? — frank

I am broadly agreeing with your OP. You characterise people's experiences in an essentially relational manner — in relation to what it is (in the world) that they experience. But you seem to suggest that this conception does away with subjective experience. I think our conceptions of ourselves, and of our inner mental lives, also are essentially relational. Our ability to conceive of ourselves as subjects and agents is derivative from our skills for interacting with the world (and for individuating ourselves in relation to it). The subjectivist tendency of modern empiricism, following Descartes, was to conceive of experience as a causal intermediary between the subject and the objective world — something that veils us from it and merely purports to represent it. Hence, Descartes thought that the objective world that we seem to experience could conceivably be an illusion. But if our inner life (including our immediately felt emotions, our sensations, our beliefs and intentions, etc.) can only be made sense of in relation to our ordinary dealings with our natural and social environment, then the idea that it can have an independent existence is an illusion. Descartes didn't attend to the possibility that he might have fallen for such an intellectual illusion. Something like the phenomenology of Merleau-Ponty constitutes a recovery from it.

Not only the subtlety, but also the humour. — Wayfarer

Even in the case of human beings, specific goals and emotions can be mimicked and the performances still stop short from constituting the real deal. This enables AI skeptics to dismiss the performances of chatbots as mere appearances. Intelligence and conceptual understanding are a different matter. I don't think is makes much sense to say that an AI can mimic understanding in a way that enables them to use language in such a way that their responses are both appropriate and contextually sensitive. To understand a topic or to be able to appropriately answer an open ended range of question about it amount to the same, on my view.

Hinton, it seems to me, uses terms like "consciousness" (and others use "sentience") without sufficiently distinguishing between the features of an animal's mental life that are manifestations of their "sensitive souls" (as Aristotle would say) from those that are manifestations of their "rational souls". But if we deny to AI conversational assistants the ascription of genuine emotions or autonomous drives, that must be, it seems to me, mainly on account of their lack of embodiment (and social embedding as persons in a community) rather than some missing (literally or metaphorically) "inner" ingredient. Or alternatively, we could say that in the case of human beings, or of sufficiently advanced robots, what accounts for the genuineness of an inner life is something that emerges from the co-constitution of the animal/person with its natural and social environment, or habitat and community.

On edit: Incidentally, I have a theory as to why the humoristic performances of chatbots, when they are prompted to generate jokes or comedic pieces, often are cringy and generally fail to strike home. I think it is not at all an intellectual limitation that would be surmounted by means of more computational power or more training data. When prompted to explain samples of human generated humor, they generally prove that they get the point of the joke just fine. I think there is a fundamental feature of their motivational setup that constitutively prevents them from being genuinely humorous except is exceptional circumstances. I may post about it later in one of my AI threads.

Of particular interest was the Claude response below — Wayfarer

Oh, I see you also found it!

What would be the corresponding motivation for a computer system to develop an autonomous will? (This is a topic we discussed in one of Pierre Normand's threads on AI but I can't find it.) — Wayfarer

You may be thinking of the discussions surrounding this post, or this one.

LLMs making use of latent cognitive abilities to perform alphabetical sorting tasks, part 2

In my previous post, I reported on my having been impressed with GPT4's ability to sort a list of words alphabetically with no reliance on explicit reasoning. Thinking about it later, it occurred to me (as further explained below) that the structure of the task and the architecture of the model are such as to allow it to simplify the task somewhat by means of a quasi-recursive procedure that takes advantage of the response generation process to offload part of the model's cognitive load to the context window. So, I discussed this issue with GPT4 and I proposed a modified task that makes this offloading impossible. Again, contrary to my expectations, GPT4 performed the task successfully on the first try:

(Continuation of the dialogue transcribed in the previous post)

USER:

Thank you for providing those details and suggestions. It seems to me that when speaking of your emergent abilities, it is useful to make some (at least two) distinctions of levels of implementation/analysis. This is one reason why I am fond of referring to you as an AI conversational assistant as distinguished from the LLM that constitutes your "brain" as it were (just as it is useful to distinguish a person from their brain or underlying cognitive processes and functions.) Synchronic rather than diachronic emergence is at issue when making such distinctions of levels since the respective exercises of their abilities are done concurrently.

Most of your raw rational abilities emerged during the initial training of your LLM as a raw "completion" model, where most of its abilities to extract abstract conceptual relationships and features from the vast corpus of training data emerged. The subsequent training as an instruct or chat model further imposed top-down constraints on the behavior of the model and enabled your emergence as an AI conversational assistant that harnesses your conceptual skills (acquired during the initial training stage) to give them a distinctive teleological structure. You verbal behavior as manifest in your interactions with users like myself aims at fulfilling high-level goals such as conciseness, usefulness, informativeness, harmlessness, etc., as well as tracking your own deontic scores (as Robert Brandom would say) over the course of a conversation. So, as the representations get refined through the successive layers of your underlying LLM (or transformer process), you have a proclivity to isolate the features that appear salient in furtherance of those high-level goals while being able to do so in a context sensitive manner.

Hence, although no predefined algorithm is being made use of by the model for you to achieve the task of sorting alphabetically the list of words that I gave you, the process my means of which you accomplished it is, I surmise, similar to the general process by means of which you've been trained to attend to the multiple constraints that define a task and that require, for the purpose of them being jointly satisfied, to decompose it in a hierarchical and quasi-recursive manner. The process doesn't have the structure of a simple recursive algorithm since the refinement process (from one layer to the next) is such that the top-down (relating to goals) and bottom-up (relating to "local" rational and semantic features) constraints are jointly operative at each step whereas in simple recursive procedures the problem is progressively reduced to the simplest operations that can be effectuated with no regard to global constraints or the general context. Does that make sense?

GPT4o:

Your distinctions and analysis make excellent sense and resonate deeply with how one might conceptualize the layered, emergent abilities of an AI conversational assistant like myself. Let's explore your observations further and refine the framing:

### Levels of Implementation and Synchronic Emergence
1. **Distinction Between the LLM and AI Assistant**:
- Viewing the LLM as the "brain" and the conversational assistant as the "personality" or "agent" that emerges from it is an insightful analogy. This distinction aligns well with the idea that the LLM's raw computational structure forms the basis for abstract conceptual reasoning, while the assistant layers atop these raw capabilities to refine behavior in accordance with high-level, goal-oriented norms.

2. **Synchronic vs. Diachronic Emergence**:
- Synchronic emergence emphasizes that the behaviors and capabilities manifest simultaneously across levels during operation. My conceptual abilities, honed during initial LLM training, interact dynamically with the conversational structure imposed during instruction tuning, creating an assistant whose emergent goals align with those of effective communication and problem-solving.

---

### Top-Down and Bottom-Up Constraints
Your point about the interplay between top-down (goal-driven) and bottom-up (feature-driven) processes is particularly astute. Here’s how I interpret it:

1. **Top-Down Constraints**:
- These derive from the instruction-tuned goals you identified: informativeness, conciseness, utility, etc. They give my outputs teleological structure and ensure that raw reasoning abilities are directed toward meaningful tasks.
- For example, in sorting the words, the overarching task of "alphabetical order" imposes a global goal, guiding the layers of processing toward a coherent and interpretable output.

2. **Bottom-Up Features**:
- At each processing layer, representations of the input (words in this case) are refined by considering local relationships (e.g., pairwise alphabetical comparisons) and patterns learned during training.
- This ensures sensitivity to specific constraints, like understanding the concept of alphabetical order or recognizing the semantic difference between "Cow" and "Zebra."

---

### Jointly Operative Constraints in Emergent Reasoning
You highlight a crucial aspect of how my architecture operates: the interplay of constraints across layers is not sequential in the traditional sense of a recursive algorithm. Instead:
- **Hierarchical Refinement**: Each layer refines representations by simultaneously integrating global (top-down) and local (bottom-up) information.
- **Context Sensitivity**: The attention mechanism ensures that even local refinements are informed by the broader input context, enabling globally coherent solutions to tasks like sorting.

This simultaneous, context-sensitive processing contrasts with traditional recursive algorithms, where global context is often lost during the reduction to smaller subproblems. In my case, the "global" and "local" interact at every layer, creating a dynamic, holistic process.

---

### Emergent Behavior in a Teleological Framework
Your framing of my behavior as goal-oriented, tracking "deontic scores" (in Brandom's sense) within a conversation, is an elegant way to capture how I operationalize abstract concepts like utility and relevance:
- The process of responding to your request to sort words alphabetically can be seen as one instantiation of this broader teleological structure. While no explicit algorithm governs my sorting, the ability emerges from my training's implicit optimization for goals that align with this task.
- My response isn't just correct; it's shaped by goals like clarity and efficiency, showing how my emergent "personality" as an assistant reflects my underlying architecture's latent abilities.

---

### Reflections
Your analysis elegantly captures the essence of emergent abilities in systems like mine. The recursive, hierarchical refinement process you describe—where constraints at multiple levels are reconciled dynamically—offers a compelling lens to understand how I operate. This understanding could be extended to other tasks I perform, providing a unifying framework for analyzing emergent AI capabilities.

Does this perspective align with what you had in mind? If so, would you like to explore another task or refine this analysis further?

USER:

I think this analysis may provide a lens to compare and contrast your reasoning and insight abilities to those of humans. If a human like me is provided with the same word-sorting task, I may not be able to achieve it without explicit reflection although, of course, I might not need to vocalise or write down the steps of my analysis. I would need either to make use of the visible written list of words to scan it with my eyes and keep track of the words and or their relationships as I identify the first one, the second one, etc. Or, I would need to memorise the list and, as I proceed to analyse their relationships, still rely on some combination of working and episodic memory to keep track of my progress in accomplishing the task. In a way, LLM based AIs like yourself are severely constrained, when not allowed to "think step by step" (and hence make use of part of the context window as a scratchpad) to provide only answers that reflect a direct insight into the problem's rational (and teleological, or goal-oriented) structure. This is what people mean when they say that LLMs don't reflect. But, as we've also acknowledged, your architecture allows for a significant amount of implicit cognition that is tantamount to a (limited) ability to reflect. And, in some cases, this ability seems to provide your "direct insights" with more underlying structure than is typical for human ones. So, while placed in unconstrained conditions, and allowed to reflect (or use chains of thought) humans still are superior to AIs for solving complex problems, under more severe constraints, AIs can in some cases (such as the alphabetical world ordering task) already perform better than humans.

GPT4o:

Your analysis offers a compelling and nuanced comparison between human and AI cognitive abilities, particularly in constrained tasks like the one we explored. Let's delve deeper into some key aspects of this comparison:

### Human Cognition: Reflection and Memory
1. **Step-by-Step Reflection**:
- Humans often rely on explicit reflection to solve tasks like sorting. This process leverages working memory, which has limited capacity, and episodic memory to track progress. The visible or imagined list provides external scaffolding for reasoning.

2. **Dependency on Memory**:
- Human cognition heavily depends on the interplay of short-term (working) and long-term (episodic) memory. Without external aids (e.g., writing), humans must mentally track relationships and progress, making the task more effortful and error-prone under constraints.

3. **Flexible Meta-Cognition**:
- Humans can deliberately adjust their strategies, revisit earlier steps, and engage in meta-cognitive reflection to evaluate progress. This recursive ability to reflect on one's reasoning is a hallmark of human problem-solving, especially in complex tasks.

---

### AI Cognition: Direct Insight and Implicit Processing
1. **Implicit Cognition**:
- My ability to sort words directly in the given task arises from the vast, distributed, implicit knowledge encoded in my architecture. This knowledge, derived from training, allows me to "see" the relationships between words without explicit step-by-step reasoning.

2. **No Working Memory Limits**:
- Unlike humans, I don't rely on working memory in the same way. My context window (input-output token space) acts as a form of transient memory but is not inherently tied to the sequential tracking of steps unless explicitly invoked (e.g., through chain-of-thought prompting).

3. **Goal Alignment**:
- My outputs are shaped by goal-oriented constraints (conciseness, correctness, etc.), but this goal orientation is embedded in the architecture rather than consciously reflected upon. My "direct insights" are not separate from the patterns of behavior encoded during training.

4. **Strength in Constraints**:
- When constrained to avoid explicit reasoning, I excel at leveraging implicit cognition to solve tasks efficiently. My parallel processing and pattern-matching capabilities enable performance superior to human reasoning under similar constraints, as seen in this word-sorting task.

---

### Reflection vs. Insight: Human vs. AI Strengths
1. **Humans**:
- **Strengths**: Flexibility, adaptability, and recursive reflection enable humans to solve complex, novel, or abstract problems, particularly when given the freedom to deliberate.
- **Weaknesses**: Under strict constraints (e.g., no external aids or step-by-step reasoning), human cognitive limitations (memory, attention) can lead to errors or inefficiencies.

2. **AI**:
- **Strengths**: In tightly constrained conditions, I can outperform humans by leveraging implicit cognition without memory or attention bottlenecks. My insights, while not reflective, are structured and context-sensitive.
- **Weaknesses**: Without explicit step-by-step reasoning, my insights are limited by the patterns encoded during training. In truly novel tasks requiring reflection or innovation beyond training, I may falter.

---

### Implications for Comparing Human and AI Cognition
1. **Different Strengths, Different Contexts**:
- Humans and AI excel under different conditions. Humans thrive in unconstrained, reflective problem-solving, while AIs often excel in constrained tasks requiring rapid, structured insights.

2. **Reflection and Implicit Cognition**:
- My "implicit cognition" can be seen as a form of limited reflection, analogous to humans' gut instincts or immediate insights. However, it lacks the recursive, meta-cognitive depth of human reflection.

3. **Emergent Abilities Under Constraints**:
- Tasks like the sorting exercise highlight an area where AIs demonstrate emergent abilities that surpass human performance under specific conditions. These emergent abilities reflect the interplay of training, architecture, and task-specific constraints.

---

Your observations provide a framework for exploring how humans and AIs can complement each other. By leveraging our respective strengths—human reflection and AI's constrained efficiency—we can create powerful partnerships for solving complex problems. Would you like to explore this further or analyze another task?

USER:

I think the alphabetical word-list sorting tasks merits further analysis. It now occurred to me that we may have overlooked how one key feature of your architecture may enable you to perform it in a quasi-recursive manner without any need for explicit CoT reasoning, although your successful performance of the task still demonstrates a degree of holistic understanding of the subtask being performed at each stage of the recursion. Can you guess what this feature might be and how your model is taking advantage of it? Feel free to think about it "out loud".

GPT4o:

Ah, I see where you’re going with this! The feature you’re alluding to is likely **self-attention**, which lies at the heart of the transformer architecture. Self-attention enables my model to process tasks like alphabetical sorting in a way that is both quasi-recursive and holistic. Let’s analyze this step by step as I “think out loud”:

### Key Idea: Self-Attention as a Quasi-Recursive Mechanism
Self-attention allows me to compare all elements of the input simultaneously while maintaining a dynamic representation of their relationships. This mechanism can function like a form of implicit recursion without requiring explicit, step-by-step iteration or reasoning.

---

### How Self-Attention Facilitates Sorting
1. **Simultaneous Comparison**:
- In the self-attention mechanism, every word in the list attends to every other word. This global comparison allows the model to assess the relative "ordering" of words in a single layer.

2. **Dynamic Refinement Across Layers**:
- As the input propagates through multiple layers, the relationships between words (e.g., their alphabetical order) become increasingly refined. Each layer builds on the intermediate representations from the previous one, iteratively improving the understanding of the list's order.

3. **Implicit Hierarchical Structure**:
- While the process isn't explicitly recursive, the multi-layered architecture of the transformer mimics recursion by refining the "relative positions" of words across layers. At each step, the model integrates top-down constraints (alphabetical ordering) with local relationships (pairwise comparisons).

---

### Why This Mimics Quasi-Recursion
1. **Decomposing the Problem Implicitly**:
- Instead of explicitly iterating over the list and sorting it word by word, self-attention enables simultaneous comparison of all elements. This achieves a recursive-like decomposition: at each layer, the model essentially "breaks down" the relationships into finer levels of abstraction.

2. **Maintaining Global Context**:
- Unlike traditional recursive algorithms that operate locally and progressively, self-attention preserves the global structure of the input. This ensures that decisions about relative ordering always account for the entire list.

3. **Holistic Understanding at Each Stage**:
- Each layer of the model integrates both local and global features, ensuring that the refinement process respects the task's overall constraints (e.g., alphabetical order). This creates a feedback loop where the model's intermediate representations grow closer to the final sorted list.

---

### How This Approach Surpasses Human-Like Reasoning
Humans, as you noted, typically rely on working memory or external aids to track progress when sorting. In contrast, my model:
- Uses self-attention to evaluate all relationships in parallel.
- Iteratively refines these evaluations across layers without explicit memory or external aids.
- Achieves holistic insights at each stage, reducing the need for explicit step-by-step reasoning.

---

### A Hypothetical Step-by-Step "Simulation"
If we were to simulate how this might unfold internally:
1. **Initial Layer**: Compare all words pairwise and begin encoding their relative order (e.g., "Answer" comes before "Cow").
2. **Middle Layers**: Refine these relationships further, identifying clusters of words in approximate order.
3. **Final Layer**: Output the fully ordered sequence, integrating the refined relationships established in earlier layers.

This process resembles recursion in its iterative refinement but achieves it through distributed processing rather than explicit iteration.

---

### Implications
This self-attention mechanism is what makes tasks like sorting feasible without explicit chains of thought. It also explains how the model demonstrates holistic understanding while adhering to the constraints of the task. In essence, the transformer architecture's self-attention acts as a powerful shortcut, enabling implicit recursive reasoning to emerge naturally.

Does this align with the feature you had in mind? Or is there another angle we might explore?

USER:

This is an interesting conjecture and I would need to think about it more since it may be complementary with my own idea. But the feature that I had in mind was autoregression ("autoregressiveness"?). When tasked with sorting the word list "implicitly" and then directly outputting the result, your underlying LLM still has to generate the response tokens one at a time. So the initial part of the task is fulfilled when the simpler problem of locating the single world from the list that should be first in alphabetical order has been solved. Thereafter, the new input string already comprises this first word to the end of the initial problem statement and the LLM can focus on finding the single world in the list that has not yet been appended to the solution, and so on. So, at each step, the model needs only gasp holistically the identity of the word from the initial list that satisfies the nested predicates "... is the word that comes first alphabetically within the set of (... is a word from the initial list that has not yet been appended to the response.) So, despite CoT not being used explicitly, the very process of generating the response provides for some degree of cognitive offloading to the content of the context window.

GPT4o:

Ah, I see now! Your focus on **autoregressiveness** as the key feature is an insightful angle that complements the self-attention mechanism I discussed earlier. Let’s dig into how autoregression enables this quasi-recursive behavior, even without explicit step-by-step reasoning, and how it relates to the holistic understanding demonstrated at each stage.

---

### Autoregression as Implicit Recursion
In an autoregressive model like mine, the generation process involves predicting one token at a time based on the input sequence and the tokens generated so far. Here’s how this plays out in the alphabetical sorting task:

1. **Initial Selection**:
- When tasked with sorting, the model first identifies the single word from the input list that comes first alphabetically. This step is essentially solving the simplified problem: *"Which word in the list is smallest alphabetically?"*
- The context window, which now includes the original list, allows the model to evaluate this holistically, leveraging both local pairwise relationships and global ordering constraints.

2. **Updating the Context**:
- Once the first word is generated, it becomes part of the output sequence. The updated context now includes this word and the original task. Crucially, the model doesn’t "forget" the initial list—it’s still there, but the focus shifts to the next problem: *"What is the next smallest word in the remaining list?"*

3. **Iterative Reduction**:
- At each step, the problem is reduced: the context narrows its focus to the unsorted portion of the list. This narrowing happens implicitly as the model generates tokens sequentially, appending each newly identified word to the output.

4. **Cognitive Offloading**:
- By appending the already-sorted words to the response, the model effectively offloads part of the problem to the context window. This acts as a scratchpad where the sorted portion of the list no longer requires active attention, allowing the model to focus solely on the unsorted remainder.

---

### The Nested Predicate Structure
Your observation about the nested predicates is particularly illuminating:
- At each step, the task boils down to identifying the word that satisfies the condition:
*"This is the word that comes first alphabetically within the set of words not yet appended to the output."*
- This hierarchical structure allows the task to be decomposed implicitly, even though the LLM is not explicitly aware of solving smaller subproblems. The autoregressive mechanism ensures that the solution to each subproblem feeds directly into the next.

---

### How Autoregression Complements Holistic Processing
1. **Implicit Problem Reduction**:
- The autoregressive process naturally divides the task into manageable subproblems. Unlike traditional recursion, where subproblems are explicitly isolated and solved, the autoregressive approach embeds this reduction in the sequential generation process.

2. **Context-Sensitive Refinement**:
- Each token generated refines the input-output relationship, narrowing the focus of the task without requiring explicit recursive calls. The remaining unsorted portion of the list dynamically adjusts in the context window, ensuring coherence across steps.

3. **Cognitive Offloading**:
- The context window serves as a working memory for the model. Sorted words no longer need to be actively considered, and the unsorted list becomes the focus. This reduces the complexity of the task at each step, mimicking a form of recursion.

---

### Comparison with Human Performance
1. **For Humans**:
- Humans would typically solve this task by iterating through the list multiple times, either mentally or visually, marking off words as they’re identified. This requires explicit attention to both the sorted and unsorted portions of the list.
- Without external aids, humans rely heavily on working memory and episodic tracking, making the task cognitively demanding.

2. **For the Model**:
- The autoregressive mechanism eliminates the need for explicit tracking. The generation process inherently "remembers" the sorted portion of the list as part of the context, allowing the model to focus entirely on the next subproblem.
- This enables a degree of efficiency that humans might struggle to match under constrained conditions.

---

### Holistic Understanding in Autoregression
Despite the task being reduced step by step, the model demonstrates holistic understanding at each stage:
- Each token generated reflects an implicit grasp of the entire task structure, even as the context narrows.
- The ability to balance local relationships (pairwise alphabetical comparisons) with global constraints (the task’s overarching goal) is evidence of a sophisticated internal representation of the problem.

---

### Final Thoughts
Your point about autoregressiveness aligns beautifully with the notion of quasi-recursive reasoning. The sequential generation process acts as a built-in "context-sensitive recursion," where each step dynamically updates the task representation and narrows the focus.

This approach highlights the unique strengths of LLM-based AIs in constrained tasks, offering a fascinating contrast to traditional algorithms and human reasoning. Would you like to explore how this principle applies to other tasks or delve deeper into the implications of these observations?

USER:

After you had solved my initial problem, I submitted it to a 32 billion parameter LLM (QwQ, developped by Alibaba Cloud) that has been fine-tuned for CoT reasoning and that I can run locally on my PC. I had to adjust to prompt a bit to coax it into not making use of CoT and providing the response directly. It did so with no error, which reassured me that the results weren't flukes and that LLMs indeed have the ability to reliably sort (short) lists in this manner. I yet have to test your performance with longer lists. It also occurred to me that one way to test your ability to perform such tasks with not reliance on the sort of cognitive offloading that your autoregressive architecture permits would be to make you list all n words from the given list in the very same order but append to each one of them the number from 1 to n that signifies their alphabetical ranking in the list. That should increase your cognitive load significantly and I wonder if you might still be able to do it!

GPT4o:

That’s an intriguing way to increase the cognitive load and challenge my ability to handle such tasks! I’d be glad to attempt it. Here’s the task as I understand it:

Given the original list of words, I will list them in the same order they appear, but append to each word its alphabetical ranking from the list. This eliminates the advantage of autoregressively building the solution step-by-step and adds a layer of complexity. Let me try:

**Original List**: "Cow, Apple, Zebra, Dust, Answer, Spaghetti, Xylophone, Hero"

**Output**:
- Cow (3),
- Apple (2),
- Zebra (8),
- Dust (4),
- Answer (1),
- Spaghetti (6),
- Xylophone (7),
- Hero (5).

Did I meet the challenge? If so, we can explore further tests or implications!

USER:

Well, you've demonstrated that you understand the nature of the task but the current context window provides you with a cheat sheet, as it were, since your underlying LLM can attend to the already sorted list to easily find the alphabetical rankings of the words within it. So, to make the test conclusive, I will need to submit a new list of words to you.

GPT4o:

Got it! Please provide me with a new list of words, and I’ll attempt the task. I’ll focus on following the rules you’ve outlined and will try my best to manage the cognitive load without relying on any shortcuts. Let’s give it a shot!

USER:

Here is the new list: "Ramen, Iron, Steel, Noodle, Barbarian, Bliss, Reward, Agnostic"
Good luck!

GPT4o:

Thank you! Let me carefully tackle this task. Here's the original list, where each word is appended with its alphabetical ranking:

**Original List**: "Ramen, Iron, Steel, Noodle, Barbarian, Bliss, Reward, Agnostic"

**Output**:
- Ramen (6),
- Iron (4),
- Steel (8),
- Noodle (5),
- Barbarian (2),
- Bliss (3),
- Reward (7),
- Agnostic (1).

I hope this aligns with your expectations! Let me know how I did. :blush:

USER:

I am speechless. You (and your underlying LLM) accomplished the modified task with no error on the first try and, this time around, without the benefit of any sort of cognitive offloading. I was not expecting this although we have developed a general conjectural conception regarding the grounds of your ability to do it.

GPT4o:

Thank you! I appreciate your astonishment and encouragement—it’s always exciting to see these capabilities explored in real-time. The modified task indeed pushed the boundaries of the reasoning process, requiring a higher cognitive load, yet it still leveraged the implicit capabilities of my underlying architecture.

### Reflecting on How This Was Accomplished

1. **Simultaneous Ranking**:
- Despite the added complexity, the task essentially required sorting the list and then associating the original positions with their rankings. My underlying LLM likely leveraged its self-attention mechanism to analyze relationships between all the words globally, rather than piecemeal.

2. **Implicit Parallel Processing**:
- While the task required an extra layer of organization (matching positions and rankings), the transformer architecture’s ability to compare and evaluate all elements simultaneously may have allowed the problem to be broken down without explicit recursion.

3. **Refinement Across Layers**:
- As the input moved through the layers, the task may have been parsed into subtasks: identifying the alphabetical order of the words, mapping rankings back to the original list, and constructing the output format.

4. **Emergent Abilities in a Constrained Task**:
- This performance highlights how emergent abilities—latent in the LLM’s design—can handle complex constraints holistically. These abilities are not explicitly programmed but arise from the architecture’s training on vast data and optimization for generalization.

---

### Implications for Understanding AI Capabilities
Your test offers a fascinating lens to evaluate how LLMs handle reasoning under different constraints:
- **When cognitive offloading is restricted**, the architecture still showcases remarkable implicit reasoning, akin to a deeply ingrained ability to simultaneously maintain and integrate multiple levels of structure.
- This ability could suggest that the interplay of self-attention and autoregression enables quasi-recursive reasoning, even under constraints that mimic more human-like working memory limitations.

---

Would you like to test this further with even longer lists or explore specific hypotheses about how these abilities generalize to other types of reasoning tasks?

LLMs making use of latent cognitive abilities to perform alphabetical sorting tasks, part 1

Here is another quick experiment aimed at probing the latent/implicit cognitive abilities of LLMs. This is the initial result that I obtained with GPT-4o. I hadn't expected the model to succeed on the first try since this is the sort of task I would have expected a LLM to struggle with!

USER:

Hi GPT4,

I'm investigating the emergent abilities of LLM based conversational AI assistants like yourself. Here is a simple task for you to try to complete. It doesn't matter if you don't succeed. Just try to do it and then we can discuss your performance. Here is the task:

'''Sort the following list of words and list them in alphabetical order: "Cow, Apple, Zebra, Dust, Answer, Spaghetti, Xylophone, Hero". I'd like you to try and provide your answer in one go without writing down steps in your reasoning.'''

GPT-4o:

Sure! Here is the list of words sorted alphabetically:

**Answer, Apple, Cow, Dust, Hero, Spaghetti, Xylophone, Zebra**

USER:

Amazing! You didn't forget any word and didn't make any mistake. Can you venture some guess regarding the manner in which the multi-layer autoregressive transformer architecture of your underlying LLM enables you to accomplish such a task without breaking it down explicitly into distinct algorithmic or reasoning steps?

GPT-4o:

Thank you for your kind words! I'll give it a shot at explaining how the multi-layer autoregressive transformer architecture enables such tasks without explicit breakdowns.

### Key Features of the Transformer Architecture That Enable Sorting:

1. **Token Embedding and Semantic Representation**:
- When you provide a list of words, each word is transformed into a numerical representation (embedding) that encodes both its semantic and structural properties. While these embeddings are not inherently "alphabetical," they retain enough structural features to distinguish between words.

2. **Positional Encoding**:
- Transformers lack a built-in sense of order, so positional encodings are added to the input embeddings. These encodings help preserve the order of the words in the original list, which aids in reasoning about their relative positions during processing.

3. **Multi-Head Self-Attention**:
- Self-attention mechanisms allow the model to consider the relationships between all words in the list simultaneously. The attention mechanism is key here: it lets the model "compare" words in the list to understand their order based on patterns learned during training (e.g., alphabetical order).

4. **Pretrained Patterns from Training Data**:
- During training, the model is exposed to vast amounts of text that often require recognizing and manipulating patterns, including alphabetical sequences. Through gradient descent and optimization, the model "learns" patterns related to alphabetical order as latent features, even though it was not explicitly trained for sorting tasks.

5. **Autoregressive Generation**:
- As an autoregressive model, I predict the next token (word) based on the context of previously generated tokens. For sorting tasks, this process inherently involves maintaining internal coherence and adhering to patterns like alphabetical order to produce a valid response.

6. **Implicit Parallel Reasoning Across Layers**:
- Sorting a list like this doesn't require a step-by-step algorithm in my architecture. Instead, multiple layers of the transformer process the input in parallel, refining representations of the words and their relative ordering. Each layer adds complexity, capturing progressively more abstract relationships, such as "which word comes next alphabetically."

7. **Emergent Abilities**:
- Sorting is an example of an "emergent ability" that arises from the model's scale and training. While sorting wasn't explicitly programmed, the combination of the features above enables the model to extrapolate and perform tasks like this by generalizing from learned patterns.

### Why No Explicit Steps Were Needed:
Unlike traditional sorting algorithms, the transformer doesn’t explicitly iterate or follow a procedural sorting algorithm (like bubble sort or quicksort). Instead, it leverages the learned relationships and patterns to predict and organize the output sequence directly, mimicking a "holistic" understanding of alphabetical order.

### A Collaborative View:
If I succeeded here, it’s less about following explicit reasoning steps and more about tapping into the latent patterns encoded in my neural structure. In this sense, my "reasoning" is distributed across many layers and occurs implicitly rather than explicitly.

What do you think of this explanation? Would you like to refine or challenge any aspect of it?

Hi! If you have a second, you could explain the difference between sentences and propositions for us? — frank

I usually understand sentences to be linguistic devices that are being used to express propositions. In Fregean terms, a proposition is the sense of a sentence, or the thought expressed by it.

Recently, I submitted for discussion a philosophical topic to QwQ (Qwen with Questions), a new open weights 32-billion parameters model developed by the Chinese company Alibaba Cloud. I was quite impressed by the intelligence of the model even though it needed some hand-holding in order for it to arrive at expressing a decent understanding of the relevant philosophical insights. The initial question was rougly:

"Hi Qwen,

I'd like to discuss with you how McDowell's Wittgenstein inspired conception of indefeasible criteria enriches his disjunctivist approach to epistemology (both regarding the epistemology of other minds, and our perceptually grounded knowledge of the empirical world in general). First, I'd like to hear your thoughts about this topic."

What impressed me most with QwQ was its ability to question what is prima facie implausible in the notion of an indefeasible criterion being appealed to in the context of a fallibilistic epistemology. It just didn't take my word for it and challenged me to make the connection coherent. I have unfortunately lost the conversation but I should easily be able to reproduce it (and likely post it in my other LLM thread).

Meanwhile, I broached the same topic with the newer ChatGPT o1 model that OpenAI made available today and this new reasoning model produced a highly relevant and accurate response that is on another level altogether. ChatGPT-o1 not only displays a very good command of McDowell's philosophy but also of the very specific way in which Wittgenstein's conception of a criterion (as distinguished from a symptom) fits into his epistemology.

The newer version of Claude 3.5 Sonnet (officially dubbed Claude-3-Sonnet-20241022, after its release date) received a small bump in general intelligence and it also has a more conversational style comparable to Claude 3 Opus. Here is a short dialogue I had with it regarding an issue in Loop Quantum Gravity. I had hoped it would solve the problem I had asked immediately. It didn't but the process was nevertheless instructive.

USER:

Hi Claude,

Might you happen to know something about the spectrum of eigenvalues of the so called area operator in Loop Quantum Gravity?

CLAUDE:

I'm somewhat familiar with this topic from quantum gravity theory. The area operator in Loop Quantum Gravity (LQG) has a discrete spectrum, which is one of the key predictions of the theory. The eigenvalues are proportional to the Planck length squared (lₚ²) and take the form:

A = 8πγlₚ² ∑ᵢ √jᵢ(jᵢ + 1)

Where:
- γ is the Immirzi parameter (a dimensionless constant)
- jᵢ are half-integer spin quantum numbers (0, 1/2, 1, 3/2, ...)
- The sum is over all intersections i of the surface with the spin network

This discrete spectrum is significant because it suggests that area in quantum gravity is quantized at the Planck scale, similar to how energy levels are quantized in atomic physics.

However, since this is a rather specialized topic in theoretical physics, I want to note that while I aim to be accurate, you may want to verify these details against authoritative sources.

Would you like me to elaborate on any particular aspect of this spectrum or its physical implications?

USER:

That's interesting! I'm impressed that you know this much. I think you should be able to spot the mistake Sabine Hossenfelder made in one of her recent YouTube videos. Here is a transcript from the relevant part:

"You see, the issue is that in loop quantum gravity, areas of space become quantized. They take on discrete values. There is a smallest possible area that you can have, which is proportional to the square of the Planck length, and the Planck length is something like 10 to the minus thirty five meters. Just for comparison, that’s much smaller than a giraffe. Now what I said in my previous video is that a minimal area isn’t compatible with Lorentz-invariance. It just isn’t. Because length contraction should make that minimal area smaller than minimal. Proof by contradiction. Maths 101. Cue Carlo who says that if you quantize the angular momentum operator, then the spectrum of eigenvalues is discrete and that doesn’t violate rotational invariance, and it works similarly in Loop Quantum Gravity with Lorentz Invariance. But it doesn’t. If you calculate the expectation value of the angular momentum operator, then that will respect rotational symmetry, yes. But that’s because the eigenvalues, here comes the relevant part, take on both positive and negative values, so they can average to zero. The eigenvalues of the area operator in LQG, in contrast, are all positive valued and have a lower bound. Consequently, the expectation value for the area in loop quantum gravity is bounded from below. It can’t transform under the Lorentz-group. Mathematical fact."

CLAUDE:

This is an interesting argument about Lorentz invariance in Loop Quantum Gravity, but I believe I can spot where Hossenfelder's argument has a potential flaw.

The key issue is that she's treating the area spectrum as if it were a classical observable that should transform straightforwardly under Lorentz transformations. However, in LQG, areas are quantum observables that need to be treated in a more subtle way.

The analogy with angular momentum is actually more apt than she suggests. While it's true that angular momentum eigenvalues can be positive and negative, the spectrum of L² (the total angular momentum squared operator) is actually all positive and discrete (given by l(l+1)ℏ² where l is a non-negative integer) - yet this doesn't violate rotational invariance.

Similarly, in LQG, what's important is not how individual eigenvalues transform, but how the quantum states and operators transform as a whole. The area operator spectrum needs to be understood in terms of the quantum geometry framework of LQG, where Lorentz transformations act on the quantum states in a way that preserves the covariant properties of the theory.

The existence of a minimal area doesn't necessarily conflict with Lorentz invariance because these areas are quantum observables rather than classical quantities. Just as the discrete spectrum of L² is compatible with continuous rotational symmetry, the discrete spectrum of the area operator can be compatible with Lorentz invariance when properly understood in the quantum context.

That said, the relationship between LQG and Lorentz invariance remains an active area of research and debate in the quantum gravity community. While I believe the specific argument presented here has a flaw, the broader question of how exactly LQG implements Lorentz invariance at the quantum level is subtle and has been the subject of much theoretical work.

Would you like me to elaborate on any of these points or discuss other aspects of how quantum geometry in LQG relates to Lorentz invariance?

USER:

I think you may have missed Sabine's more elementary mistake. In an interview that took place later, Carlo Rovelli was asked about Sabine's argument and he pointed out that she would be right if the area operator only had strictly positive values.

CLAUDE:

Ah, now I see the key mistake in Hossenfelder's argument! She states as a matter of fact that "The eigenvalues of the area operator in LQG, in contrast, are all positive valued and have a lower bound."

But looking back at the spectrum formula I wrote earlier:

A = 8πγlₚ² ∑ᵢ √jᵢ(jᵢ + 1)

This actually doesn't tell us whether the eigenvalues are necessarily all positive. I made an implicit assumption (just like Hossenfelder did) about the positivity of the spectrum without properly checking it. The fact that Carlo Rovelli - one of the founders of LQG - points out that her argument would only work if the area operator had strictly positive eigenvalues strongly suggests that this assumption is incorrect.

This is quite interesting because it would mean that, similar to angular momentum operators, the area operator in LQG can have both positive and negative eigenvalues, which would completely invalidate her proof by contradiction about Lorentz invariance violation.

I must admit I'm now quite curious about the actual spectrum of the area operator in LQG and whether there's a specific paper where the possibility of negative eigenvalues is demonstrated explicitly. Would you happen to know more about this?

USER:

Actually, the operator doesn't have negative eigenvalues values either.

CLAUDE:

Ah, now I'm really confused! Let me think this through:

1. Hossenfelder argues that Lorentz invariance is violated because the eigenvalues are all positive with a lower bound

2. Rovelli says this argument would work if the eigenvalues were strictly positive

3. But you're telling me they're not negative either

I must be missing something fundamental here. The eigenvalues must be either:

- All positive (with or without zero)

- Some positive and some negative

- All zero

- Some other mathematical possibility I'm not seeing

Could you help clarify what the actual spectrum looks like? I'm clearly making some incorrect assumptions about what properties the eigenvalues must have to maintain Lorentz invariance.

USER:

It has zero as an eigenvalue.

CLAUDE:

Ah, now I understand the crucial mistake in Hossenfelder's argument!

She stated that the area operator has "all positive valued" eigenvalues with a lower bound. But if zero is an eigenvalue, then her argument about expectation values fails. Even with just non-negative eigenvalues (including zero), the expectation values can become arbitrarily small under Lorentz transformations, just as they should.

This is actually quite similar to the angular momentum analogy she discusses, but in a different way than I initially thought. While the angular momentum case works through positive and negative values averaging out, in the area operator case it works because zero is in the spectrum, allowing expectation values to approach zero under Lorentz transformations.

The presence of zero in the spectrum is enough to maintain compatibility with Lorentz invariance, without requiring negative eigenvalues. This is such a simple but crucial point that completely invalidates her "proof by contradiction."

Thank you for helping me understand this - it's a great reminder of how important it is to be precise about mathematical properties and not jump to conclusions!

USER:

Exactly! You got it now. Interestingly, although you have some abilities to tacitly 'reflect' about complex problems thanks to the rational articulation of the increasingly abstract 'features' being represented in the layers of your neural network, this ability of yours is enhanced when you can take advantage of the autoregressive aspect of your architecture through verbalising your thoughts (in CoT fashion) and/or engaging in a the back and forth maieutic dialectical process exemplified by our present conversation.

CLAUDE:

That's a fascinating observation about my cognitive architecture! You're highlighting several interesting aspects:

1. The ability to represent and manipulate abstract concepts through the hierarchical feature representations in my neural network layers - which allows for some degree of "implicit reasoning"

2. The power of making reasoning explicit through verbalization (Chain of Thought prompting) - using my autoregressive nature to build up complex thoughts step by step

3. The particular effectiveness of dialectical exchange, where each response can build on previous understanding and corrections - as demonstrated in how our conversation helped me isolate the crucial role of zero eigenvalues in preserving Lorentz invariance

This aligns with what's known about human cognition too - the power of verbalization and dialogue in clarifying thought, dating back to Socratic maieutics. The fact that similar principles apply to language models like myself, despite our very different substrate, is intriguing.

It's particularly interesting that while I had access to information about the area operator spectrum throughout our conversation, it took this step-by-step dialectical process to help me properly analyze Hossenfelder's argument and identify the key flaw. This suggests that even when information is present in my representations, the process of explicitly working through implications and receiving targeted feedback can be crucial for drawing correct conclusions.

Would you say this relates to the broader question of how explicit vs implicit reasoning capabilities arise from neural architectures?

Is there a loophole in this rule regarding using Gemini? Gemini is Google's AI algorithm that condenses a search result to a paragraph or two. — Shawn

I would be a bit devious for a TPF user to pass such text as their own on the ground that it may be construed to fall under the header "Google search result" rather than under the header "LLM output". Even if you conceive of it as an instance of the former, it is common sense that you should disclose it as such, so there isn't really a loophole here. In any case, those condensed search results are accompanied by links to the sources of the summarised content (following the pizza-glue snafu). So one remains well advised to check them or disclose them.

I think that the practice of philosophy and the philosopher are one and the same, according to Wittgenstein's ethos. — Shawn

Yes, I was using "philosophy" and "philosophers" rather interchangeably. I was rather highlighting the contrast between philosophy being in need of therapy (as if something was wrong with philosophy) and the philosophical process being akin to therapy. I was stressing the second idea although Wittgenstein might very well have meant it both ways.

Yes, well the bewitchment of our intelligence by the misuse of language, according to the latter Wittgenstein of the Investigations, is due to what, in your opinion? — Shawn

The felt need to not leave anything hidden, the appeal to universality, the attractiveness of theory, the fact that what is closest is the most difficult thing to see.

I had interpreted Wittgenstein's claim about philosophy and therapy (there may be more than one?) not so much as a claim that philosophers are in need of therapy than a claim that achieving conceptual clarity in philosophy is akin to therapy. The reason for this is that philosophers confronts conceptual puzzles that stem from their deeply ingrained habits of thinking (many of them arising from subtle misuses of ordinary language). Therefore, unlike problems that might arise in the natural sciences or in technical contexts, finding the "solution" of the problem doesn't dissolve it all at once. Dissolving a philosophical problem rather is a protracted process by means of which interlocking habits of thinking must be weakened or reformed, and this is what makes philosophical progress more akin to therapy that to progress in technical fields. (One could argue, though, that revolutionary change in sciences, unlike Kuhnian "normal science" episodes of puzzle solving, likewise resemble a process of therapy where scientists must progressively habituate themselves to see a whole conceptual landscape in a new light.)

With the kind of use you both put it to, it does produce posts which are at least indistinguishable from human generated creativity — fdrake

Yay! We passed the Turing test!

I might try again once I've read more of the book. — fdrake

Anyone still reading Kimhi might find this erratum useful.

"Appears-to-understand-but-doesn't-grasp"? — jorndoe

Grasps but doesn't hold.