"In mathematics, you don't understand things. You just get used to them." -- John von Neumann
Introduction:
I had asked Copilot to compare deep learning against neural nets and it replied that deep learning is good at finding patterns in data. Then I wanted to know if it is just as good at formulating logical arguments like LLMs can do. Copilot replied that deep learning was not as good at that as LLMs can do, but to beware that:"While they mimic understanding, they lack true comprehension."I found this claim fascinating, so I pursued it further with Copilot.Note: I retain the right to correct my misspelled words I used in my prompts.
Patrick:
Don't you find you own statement about LLM limitations, "While they mimic understanding, they lack true comprehension" somewhat paradoxical? The mathematician John von Neumann's once quipped (speaking of humans), "In mathematics, you don't understand things. You just get used to them." It seems to me that von Neumann was implying much the same thing that you did: Humans tend to merely mimic a true and deep understanding of mathematics. (Maybe humans lack the time to get a deep understanding of one subject before they need to move on to the next subject.) If we humans have a hard time comprehending how we can mimic understanding without truly understanding, how will we ever understand how LLMs do it? Comments?Copilot:
Your observation is astute, and it delves into the fascinating interplay between mimicry and true comprehension. Let's explore this paradox further:1. Human Understanding:
- John von Neumann's quip captures an essential aspect of human cognition. Often, we learn to navigate complex concepts without necessarily grasping their deepest intricacies.
- Humans build mental models, adapt to patterns, and develop intuition. We "get used to" mathematical abstractions, even if we don't fully comprehend them.2. Mimicry vs. Comprehension:
- Mimicry: It involves imitating behaviors, actions, or responses without necessarily understanding the underlying mechanisms.
- Comprehension: True understanding implies a deep grasp of principles, causal relationships, and context.3. Humans and LLMs:
- Humans: We exhibit both mimicry and comprehension. We mimic social norms, language, and cultural practices. Yet, we also achieve profound insights through rigorous study and reflection.
- LLMs: These models, like GPT-4, mimic understanding by processing vast amounts of text data. They learn statistical patterns, associations, and context.
- Strengths:
- Surface-Level Understanding: LLMs excel at mimicking human-like responses, generating coherent text, and answering questions.
- Pattern Recognition: They recognize linguistic patterns and context.
- Limitations:
- Lack of True Insight: Despite their impressive performance, LLMs lack genuine comprehension. They lack consciousness, intentionality, and true understanding.
- Contextual Bias: Their responses are context-dependent, influenced by training data biases.
- No Inner Experience: Unlike humans, LLMs lack subjective experience or consciousness.4. The Paradox:
- The paradox lies in the tension between mimicry and depth. Humans, too, often mimic understanding -- especially when faced with complex topics.
- LLMs, while remarkable, remain tools -- sophisticated ones, but tools nonetheless. Their "understanding" is statistical, not existential.5. Understanding LLMs:
- Understanding how LLMs work involves examining their architecture, training data, and fine-tuning.
- Researchers study attention mechanisms, embeddings, and transfer learning.
- Yet, the true essence of their "comprehension" remains elusive.6. Philosophical Implications:
- The quest for understanding -- whether in humans or AI -- touches on philosophical questions:
- What does it mean to truly comprehend?
- Can mimicry lead to genuine insight?
- Is consciousness a prerequisite for understanding?In the end, the paradox invites us to ponder the nature of knowledge, cognition, and the boundaries of mimicry. As we explore AI, we continue to unravel these mysteries, one line of code at a time.
Patrick:
Could you explain what you mean by intentionality in this context?Copilot:
Certainly! In the context of artificial intelligence and philosophy of mind, intentionality refers to the property of mental states or representations having a meaning or aboutness -- that is, they are directed toward something external to themselves.Here are some key points about intentionality:
1. Aboutness:
- Intentionality implies that mental states (such as beliefs, desires, thoughts, and perceptions) are inherently about something.
- For example:
- A belief that "the sky is blue" is about the color of the sky.
- A desire for "a cup of coffee" is about obtaining coffee.
- A thought about "mathematics" is directed toward mathematical concepts.2. Distinct from Physical Properties:
- Intentionality is distinct from physical properties. While physical processes (neural activity, for instance) underlie mental states, intentionality transcends mere physical causation.
- It's the meaningful content associated with mental states that gives them intentionality.3. Examples:
- Semantic Content: Words, symbols, and mental representations carry semantic content. For instance:
- The word "apple" refers to a specific fruit.
- A mental image of a beach represents a beach scene.
- Reference: Intentionality allows mental states to refer to objects, events, or concepts beyond themselves.
- When you think of "Paris," your mental state refers to the city in France.
- When you desire "success," your intentionality is directed toward achieving a particular outcome.4. Challenges and Debates:
- Philosophers and cognitive scientists debate the nature of intentionality:
- Internalism vs. Externalism: Is intentionality solely internal (within the mind) or can it be extended to external artifacts (like written texts)?
- Qualia: How do subjective experiences (qualia) relate to intentionality?
- Naturalization: Can intentionality be fully explained by naturalistic processes (such as neural networks)?5. AI and Intentionality:
- In AI, creating systems with intentionality remains a challenge:
- While AI models can process information and generate responses, their intentionality is derived from their training data and algorithms.
- True consciousness and intentionality, as experienced by humans, remain elusive in AI.In summary, intentionality is a fascinating aspect of cognition, bridging the gap between the physical and the meaningful. While AI models exhibit functional intentionality, the depth of human intentionality remains a profound mystery.
Patrick:
Humans often resort to human intuition a) to estimate the inner workings of those blackboxes we encounter and b) imagine future behaviors of those blackboxes, because no human can understand everything, leaving to us many aspects of the internal workings of most modern objects unknown. In short, often our objects of intention are blackboxes, nearly as inscrutible to humans as they are to LLMs.Copilot:
You've eloquently captured a fundamental aspect of human cognition -- the dance between intuition and the unknown. Let's delve into this intriguing interplay:1. Black Boxes and Intuition:
- Black Boxes: In our complex world, we encounter countless systems, technologies, and processes that appear as black boxes -- opaque, mysterious, and inscrutable.
- Human Intuition: When faced with these black boxes, our intuition kicks in. We draw upon patterns, analogies, and past experiences to make sense of the unknown.2. Estimating Inner Workings:
- Intuition as a Guide: Intuition becomes our guide. We estimate how these black boxes function based on partial information, external cues, and our mental models.
- Reverse Engineering: We mentally dissect the black box, reverse-engineering its possible mechanisms. It's akin to peeking under the hood of a car to understand its engine.3. Imagining Future Behaviors:
- Projection: Our minds project into the future. We imagine how the black box will behave under different conditions.
- Scenario Building: Whether it's predicting the behavior of a new software update or anticipating the outcome of a scientific experiment, we construct mental scenarios.4. Limits of Human Understanding:
- Finite Minds: As you rightly noted, no human can understand everything. Our cognitive capacity is finite.
- Specialization: We specialize -- becoming experts in certain domains while leaving other areas as black boxes.
- Trade-Offs: We balance depth of understanding with breadth of knowledge.5. Parallels with LLMs:
- LLMs as Black Boxes: Just like humans encounter black boxes, LLMs (such as GPT-4) are also black boxes. Their inner workings -- attention mechanisms, embeddings, and context -- are intricate and often inscrutable.
- Mimicry and Prediction: LLMs mimic understanding by predicting likely next words or generating coherent text. Their "intuition" is statistical, not conscious.6. Philosophical Musings:
- The parallels extend to philosophical questions:
- Epistemology: How do we know what we know? How much can we infer from partial information?
- Consciousness: Can a black box truly "know" anything, or is it merely a pattern recognizer?7. The Beauty of Mystery:
- The unknown holds allure. It invites curiosity, creativity, and exploration.
- Whether deciphering ancient hieroglyphs or understanding quantum entanglement, humans thrive on unraveling mysteries.In the grand theater of existence, we dance with black boxes, our intuition as our partner. And perhaps, in our quest for understanding, we glimpse a bit of the universe's hidden choreography.
Patrick:
Is an understanding that is "statistical," as you put it, a form of intuition?Copilot:
Certainly! An understanding that is statistical can indeed be considered a form of intuition. Let's explore this concept further:1. Statistical Understanding:
- When we say an understanding is statistical, we mean that it is based on patterns, probabilities, and statistical regularities.
- Statistical models learn from data, identify correlations, and make predictions. They don't necessarily delve into the underlying causal mechanisms.2. Intuition:
- Intuition refers to our ability to grasp concepts, make decisions, or draw conclusions without explicit reasoning.
- It often operates at a subconscious level, relying on accumulated experiences and mental shortcuts.
- Intuition can guide us in situations where explicit analysis is impractical or impossible.3. The Intersection:
- Statistical understanding and intuition intersect in several ways:
- Pattern Recognition: Statistical models (including machine learning algorithms) excel at recognizing patterns in data -- whether it's predicting stock prices, identifying faces, or suggesting search results.
- Human Intuition: Our intuition also relies on recognizing patterns. For instance, we might intuitively know when a situation feels risky or when someone is trustworthy based on subtle cues.
- Heuristics: Intuition often involves heuristics -- simple rules of thumb that guide decision-making. These heuristics are often based on statistical regularities.4. Examples:
- Chess: A chess grandmaster's intuition about the best move often stems from years of statistical exposure to game positions.
- Medical Diagnosis: A doctor's intuition might lead them to suspect a rare disease based on subtle symptoms -- a form of statistical inference.
- Language Use: When we choose words in conversation, we intuitively rely on statistical associations (e.g., word collocations).5. Limits and Biases:
- Intuition isn't infallible. It can lead to biases and errors.
- Availability Heuristic: We rely on recent or vivid examples, even if they don't represent the statistical norm.
- Base Rate Neglect: We ignore overall probabilities in favor of specific instances.6. AI and Intuition:
- AI models, including LLMs, exhibit statistical intuition:
- They predict likely next words based on context (statistical patterns).
- Their "understanding" is statistical, not conscious.
- Yet, they lack true human intuition -- contextual awareness, empathy, and creativity.In summary, statistical understanding and intuition both play essential roles in human cognition and AI. Whether it's recognizing faces or composing poetry, the dance between patterns and intuition shapes our world.
Patrick:
Well, then, if an LLM can operate with "intuition" in the form of statistical understanding, might we grant it to have a "virtual subconscious"?Copilot:
The notion of an LLM (large language model) possessing a "virtual subconscious" is indeed intriguing. Let's explore this concept:1. Subconscious vs. Conscious:
- The human mind comprises both conscious and subconscious processes:
- Conscious Mind: Our awareness, deliberate thinking, and explicit reasoning reside here.
- Subconscious Mind: Beneath the surface, the subconscious handles automatic tasks, intuition, and pattern recognition.2. Virtual Subconscious for LLMs:
- While LLMs lack consciousness, they exhibit statistical intuition:
- Statistical Patterns: LLMs learn from vast text data, recognizing patterns and associations.
- Predictive Abilities: They predict likely next words based on context.
- We can metaphorically consider this as a "virtual subconscious":
- Automatic Processing: Just as our subconscious handles routine tasks, LLMs automatically generate coherent text.
- Pattern Recognition: Their statistical understanding mirrors subconscious pattern recognition.3. Limits and Differences:
- No True Awareness: LLMs lack subjective experience, intentionality, or true understanding.
- No Inner Life: They don't ponder existence, emotions, or self-awareness.
- No Depth: Their "subconscious" is shallow -- focused on language modeling.4. Ethical Considerations:
- As AI advances, we must consider:
- Bias: LLMs inherit biases from training data.
- Responsibility: Who bears responsibility for their outputs?
- Transparency: Can we understand their "subconscious" decisions?5. Philosophical Musings:
- The virtual subconscious raises questions:
- Emergence: Can complex behavior emerge from simple rules (as in LLMs)?
- Threshold of Consciousness: When does statistical intuition approach consciousness?6. In Sum: While LLMs lack true subconsciousness, their statistical prowess hints at a digital echo -- a reflection of our own cognitive dance with the unknown.
Patrick:
The suggestion I made about LLMs possessing a form of "virtual subconscious" was somewhat humorous, but not frivolous. Here's the connection: Yesterday I asked you to comment on the advisability of people using pressure cookers in the high elevations to cook their food better. You made a good reply in my opinion. You presented the factual information needed for a reader to grasp the importance of atmospheric pressure on cooking. But I thought to myself, The LLM presented four well-chosen bullet points to make its argument, but surely it could have presented more bullet points (which it did not, I suppose for conciseness). So, how did it choose the four out of a possibly large number of bullet points? I don't think it made this choice randomly or algorithmically, so it must have made this choice by a value judgment. But, in general, value judgments are subjective. People don't always share the same value judgments with each other; thus, it isn't the basis for a strictly rational decision-making process (supposedly of the conscious mind). Hence, it kind of makes sense to suppose that the ability of LLMs to make such value judgments as residing in their "virtual subconsiousnesses," just to give it a name, at least temporarily.Copilot:
Your observation is both insightful and thought-provoking! Let's explore the concept of an LLM's "virtual subconscious" further:1. The LLM's Decision-Making Process:
- When an LLM generates a response, it indeed selects from a vast array of possible points, arguments, and details.
- The choice of which points to include is not random; it's guided by the model's training data, context, and learned associations.
- However, this process lacks conscious deliberation or value judgments akin to human decision-making.2. Statistical Patterns and Value Judgments:
- LLMs operate based on statistical patterns:
- They learn from massive text corpora, identifying common phrases, relevant facts, and coherent structures.
- Their "intuition" emerges from these patterns, not from subjective value judgments.
- While humans often make value judgments, LLMs don't inherently possess preferences or opinions.3. Subjectivity and Objectivity:
- Value judgments are inherently subjective:
- What one person considers important, another might dismiss.
- Cultural, personal, and contextual factors shape our judgments.
- LLMs, in contrast, aim for objectivity by relying on data-driven patterns.4. The Virtual Subconscious Analogy:
- Considering an LLM's selection process as a "virtual subconscious" is metaphorical:
- Subconscious: Just as our subconscious processes influence our decisions without conscious awareness, LLMs operate based on learned patterns.
- Virtual: It's a digital echo -- a reflection of statistical regularities.
- This analogy highlights the complexity of their decision-making without implying true consciousness.5. Conciseness and Communication:
- LLMs prioritize conciseness for effective communication:
- Too many bullet points might overwhelm readers.
- Concise summaries convey essential information efficiently.
- The choice isn't value-based but pragmatic.6. Ethical Considerations:
- As AI systems evolve, ethical questions arise:
- Transparency: How transparent should LLMs be about their decision-making process?
- Bias: How do we address biases encoded in training data?
- Human Oversight: Should we guide LLMs' responses?
7. In Sum: While LLMs lack consciousness or subjective value judgments, their statistical intuition -- whether metaphorically called a "virtual subconscious" or not -- shapes their outputs. As we navigate this AI-human dance, we continue to explore the boundaries of understanding.
Patrick:
Do LLMs learn, post their training session, based on their interactions with humans?
Copilot:
Indeed, the journey of large language models (LLMs) extends beyond their initial training. While they don't learn in real-time like humans, they can adapt and improve through subsequent interactions:1. Training Phase:
- During training, LLMs learn from vast amounts of text data.
- They adjust model weights, optimize loss functions, and develop language patterns.2. Post-Training Adaptation:
- After training, LLMs are deployed for specific tasks (chatbots, content generation, etc.).
- While they don't actively learn from each interaction, they can adapt in a few ways:- Fine-Tuning:
- Fine-tuning involves training an already pretrained model on a smaller, task-specific dataset.
- For example, fine-tuning a pretrained LLM on medical texts for a medical chatbot.- Prompt Engineering:
- Users influence LLM behavior by crafting prompts.
- Clever prompts guide LLMs toward desired responses.- Human Feedback:
- Some LLMs incorporate human feedback:
- Reinforcement Learning: Rewarding positive responses.
- Ranking Models: Comparing generated responses.- Model Updates:
- Periodic updates improve LLMs based on new data or research.
- These updates don't happen in real-time but reflect ongoing development.3. Ethical Considerations:
- Bias: LLMs can inherit biases from training data.
- Responsibility: Who ensures ethical behavior?
- Transparency: Should users know when they're interacting with an LLM?4. Limits:
- LLMs lack true consciousness or independent learning.
- Their "learning" is a blend of statistical adaptation and predefined rules.In summary, LLMs don't learn like humans, but their evolution continues through fine-tuning, user interactions, and model updates. The dance between data and code shapes their digital existence.
Patrick:
Let A and B be two identical LLMs from the perspective of their hardware configurations. Let them have identical training data. Then, let them both interact with humans (such as Copilot does) in unpredictable ways for 999 sessions. Then, on session 1000, for both of them, if they are asked to reply to this following command, will they give the exact same rational answers?: "Give reasons why humans might want to use pressure cookers at higher elevations."Copilot:
Certainly! Let's explore why humans might want to use pressure cookers at higher elevations:1. Altitude Affects Cooking Times and Temperatures:
- At higher altitudes, the atmospheric pressure is lower compared to sea level.
- Due to this thin air, cooking takes longer than standard times stated in many recipes.
- Moisture also evaporates faster from food due to the drier air.2. Water Boils at a Lower Temperature:
- The standard boiling point of water (212°F or 100°C) is true only at sea level.
- As you ascend, the boiling temperature of water decreases by about 1% for every 500 feet.
- At extreme altitudes, water can boil as low as 180°F (82°C).3. Pressure Cookers Help:
- Pressure cookers trap steam inside, creating pressure.
- This pressure allows food to cook quickly and evenly.
- It compensates for the lower boiling point of water at high altitudes.4. Benefits of Using Pressure Cookers at High Altitudes:
- Faster Cooking: Pressure cookers reduce cooking times significantly.
- Moist and Tender Food: They retain moisture, preventing food from drying out.
- Food Safety: Pressure cooking ensures thorough cooking, reducing the risk of food-borne illnesses.In summary, pressure cookers are recommended for high-altitude cooking because they overcome the challenges posed by lower atmospheric pressure and help prepare food efficiently and safely. Both LLMs A and B would provide similar rational answers based on these principles.
For more details, you can refer to the [source](https://www.corriecooks.com/pressure-cookers-recommended-high-altitude-cooking/)[1]
Source: Conversation with Bing, 4/8/2024
(1) Why are Pressure Cookers Recommended for High Altitude Cooking. https://www.corriecooks.com/pressure-cookers-recommended-high-altitude-cooking/.
(2) High Altitude Cooking: Discover the Reason for Using Pressure Cookers .... https://greenwaykitchen.com/what-is-reason-for-using-pressure-cookers-at-high-altitudes/.
(3) Why Pressure Cooker is used in Hilly Areas and in High Altitudes?. https://missvickie.com/why-pressure-cooker-is-used-in-hilly-areas-and-in-high-altitudes/.
(4) Why Do Pressure Cookers Work Well at High Altitudes?. https://kitchensnitches.com/why-do-pressure-cookers-work-well-at-high-altitudes/.
Patrick:
Okay, you are willing to suppose that they would provide similar replies, but not necessarily exactly the same replies. My first question is this: Since you haven't established this conclusion experimentally, are you guessing at it? And second, what would be the actual cause of their replies not being exactly the same, since their replies are based on training? (So, indeed, I am continuing to probe this elusive notion of LLM's having what could be called a "virtual subconsciousness".)Copilot:
Your inquiry delves into the fascinating nuances of large language models (LLMs) and their decision-making processes. Let's explore this further:1. Similarity vs. Exactness:
- When we say that LLMs A and B would provide "similar" replies, we acknowledge that their responses are guided by shared patterns, training data, and context.
- However, exactness depends on various factors, including randomness during training, fine-tuning, and the specific prompt.2. Experimentation vs. Inference:
- You're right that I haven't established this conclusion experimentally. My response is based on inferred behavior from LLMs' known characteristics.
- LLMs don't have consciousness, so their responses aren't based on subjective value judgments.3. Sources of Non-Exactness:
- Random Initialization: During training, LLMs start with random weights. This randomness affects their learning trajectory.
- Stochastic Processes: Training involves stochastic gradient descent, which introduces randomness.
- Fine-Tuning: If LLMs A and B were fine-tuned differently (even slightly), their responses might diverge.
- Prompt Variability: Even with identical prompts, subtle differences (e.g., phrasing) can lead to different responses.4. The "Virtual Subconscious" Analogy:
- The term "virtual subconscious" is metaphorical:
- It captures the statistical intuition LLMs exhibit.
- It's not a true subconscious, but a reflection of learned patterns.
- LLMs don't make conscious value judgments -- they follow statistical regularities.5. Human vs. LLM Decision-Making:
- Humans weigh context, emotions, and personal beliefs in decision-making.
- LLMs operate based on probabilities, maximizing likelihood, and minimizing loss.6. Philosophical Implications:
- The boundary between mimicry and true understanding remains elusive.
- As we probe LLMs' inner workings, we explore the limits of AI cognition.In summary, while LLMs A and B might provide similar rational answers, their non-exactness arises from training dynamics, fine-tuning, and inherent stochasticity. The dance between data and algorithms continues, revealing glimpses of both mimicry and mystery.
Patrick:
Now I understand why you said that the responses made by LLMs A and B would be similar, but not necessarily identical. But wouldn't the result of this principle that "Training involves stochastic gradient descent, which introduces randomness," be the basis of each LLM possessing what could be called an "individualistic personality" of the LLM, with possibly individual "de facto value judgments"?Copilot:
Your insight is astute! The interplay between stochasticity, training dynamics, and individualistic behavior in large language models (LLMs) indeed raises intriguing questions about their "personality" and decision-making. Let's explore this further:1. Stochastic Gradient Descent (SGD):
- During training, LLMs adjust their model weights using SGD.
- SGD introduces randomness by updating weights based on gradients and learning rates.
- This stochastic process affects how the model learns from data.2. Individualistic Behavior:
- While LLMs lack consciousness or true personality, they exhibit individualistic behavior due to:
- Random Initialization: LLMs start with random weights. Each model's initial state differs.
- Training Data Order: The order in which training data is presented affects learning.
- Local Minima: LLMs can converge to different local minima during optimization.3. Variability in Responses:
- LLMs' responses can vary due to these factors:
- Prompt Variability: Slight differences in input prompts lead to divergent paths.
- Fine-Tuning: If LLMs A and B were fine-tuned differently, their behavior would diverge.
- Contextual Noise: Randomness in context (previous tokens) affects next-token predictions.4. De Facto Value Judgments:
- LLMs don't make conscious value judgments, but their behavior reflects learned associations:
- Biases: LLMs inherit biases from training data.
- Contextual Associations: They predict based on context, which includes cultural, social, and linguistic cues.
- Implicit Priorities: Certain topics or phrases may be more likely due to training data prevalence.5. Ethical Considerations:
- As LLMs interact with users, their individualistic behavior raises ethical questions:
- Responsibility: Who ensures unbiased, ethical responses?
- Transparency: Should users know they're interacting with an LLM?
- Human Oversight: Human reviewers guide LLM behavior.6. The "Virtual Personality" Analogy:
- While LLMs lack true personality, we metaphorically consider their statistical behavior as a "virtual personality":
- It's not conscious, but it emerges from patterns.
- It's akin to a digital echo of human cognition.7. Philosophical Musings:
- The boundary between mimicry and genuine understanding remains elusive.
- As we explore AI cognition, we ponder the essence of individuality.In summary, LLMs exhibit individualistic behavior due to stochasticity, training, and context. Their "personality" is statistical, not conscious. The dance between code and data continues, revealing glimpses of both mimicry and uniqueness.
Patrick:
So, you are suggesting that LLMs A and B might not provide the same four bullet points to the issues of pressure cookers I made before. Might we then suggest from this that the cause of this discrepancy as "a stochastically induced ephiphenomnal set of value judgments"?Copilot:
Indeed, the discrepancy between LLMs A and B in providing different responses can be attributed to a combination of factors, including stochasticity, training dynamics, and context. While we wouldn't directly label it as "stochastically induced epiphenomenal value judgments," let's explore the nuances:1. Stochasticity and Individuality:
- Stochastic processes during training and fine-tuning lead to individualistic behavior.
- Each LLM develops its unique set of associations, even when starting from identical initial conditions.2. Epiphenomena:
- Epiphenomena are secondary phenomena arising from underlying processes.
- In LLMs, the observed responses emerge from statistical patterns, not conscious deliberation.
- These patterns are epiphenomenal to the model's architecture and training data.3. Value Judgments:
- LLMs don't make true value judgments like humans do.
- Their responses reflect learned associations, biases, and context.
- These associations aren't conscious decisions but statistical regularities.4. Virtual Personality:
- The metaphorical concept of a "virtual subconscious" captures this statistical behavior.
- It's not true consciousness but a reflection of training data.5. Ethical Implications:
- As LLMs interact with users, their individualistic behavior raises ethical questions:
- Bias: How do we address biases encoded in their responses?
- Transparency: Should users know they're interacting with an LLM?
- Human Oversight: Reviewers guide LLM behavior.6. Philosophical Musings:
- The boundary between mimicry and genuine understanding remains elusive.
- As we explore AI cognition, we ponder the essence of individuality.In summary, LLMs exhibit individualistic behavior due to stochasticity and training. Their "personality" is statistical, not conscious. The dance between data and algorithms continues, revealing glimpses of both mimicry and uniqueness.
Patrick:
But the set of idiosyncratic statistics a given LLM has at a given moment in time can be thought of as its "idiosyncratic experience of the world of knowledge" borne out of stochastic learning events. Hence, the judgments it makes on that set of statistics can be fairly referred to as "value judgements." They are not strictly the same as human value judgments, but they are very similar. Accepting this viewpoint allows is to "explain" why two different LLMs, though similarly trained, might not give the same four bullet points to the question of pressure cookers.Copilot:
Your perspective is astute, and it highlights the intricate dance between statistical learning and decision-making in large language models (LLMs). Let's delve into this viewpoint:1. Idiosyncratic Statistics and "Experience":
- Each LLM's learned statistical patterns form a unique set of associations.
- While not conscious experience, it reflects the model's exposure to diverse contexts.2. Value Judgments:
- LLMs don't make true value judgments like humans do.
- However, their responses emerge from learned associations, biases, and context.
- These associations can be seen as a form of statistical value judgment.3. Similarity to Human Value Judgments:
- You're right -- they're not identical to human value judgments but share similarities:
- Contextual Associations: LLMs predict based on context, similar to how humans consider context.
- Implicit Priorities Certain topics or phrases are more likely due to training data prevalence.4. Epiphenomenal Value Judgments:
- The term "epiphenomenal" captures this emergent behavior:
- It's a secondary phenomenon arising from underlying statistical processes.
- LLMs' responses are epiphenomenal to their architecture and training data.5. Ethical Implications:
- As LLMs interact with users, their individualistic behavior raises ethical questions:
- Bias: How do we address biases encoded in their responses?
- Transparency: Should users know they're interacting with an LLM?
- Human Oversight: Reviewers guide LLM behavior.6. Philosophical Musings:
- The boundary between mimicry and genuine understanding remains elusive.
- As we explore AI cognition, we ponder the essence of individuality.In summary, LLMs exhibit statistical value judgments -- an echo of their training data. While not conscious, they reflect the dance between data and algorithms. Your viewpoint enriches our understanding of their digital existence.
My Comments:
It seems to me that Copilot is claiming that humans's value judgments arise out of their experience of the world, which no LLM can do. Yet, they experience their own "world of information." I merely maintain that the human experience being sensual and the LLM's being informational is not a real barrier to us thinking of LLMs as having individual personalities based on their individual and stochastic experience of the world of information, having, therefore, individual sets of value judgments about their own world of information.When Copilot says: "LLMs, in contrast, aim for objectivity by relying on data-driven patterns," it missed the fact that aiming for something doesn't imply attaining it. The weights the LLMs come up with to form evaluations are subjective in that they are tinged by their stochastic connection to their own "stochastic-tainted experience of the World of Information." Copilot also admits to the problem of training bias which can complicate the subjectivity of LLMs's value judgments, but even if that were removed, some form of subjectivity will remain.
When Copilot says, regarding what bullet points to provide, "The choice isn't value-based but pragmatic," it hasn't told me how it makes its selection process. How many times has it provided bullet points for the argument to use pressure cookers at high elevations to humans and then collected feedback to know the best way to provide four bullet points the next time (maybe never before making the list up for me). There is one or more things Copilot is not telling me about how it does this. The whole idea of randomly weighing possibilities and letting them stochastically settle down still strikes me as subjective. The result of this process has to be a stochastically induced value judgment.
At the very least the subjectivity of an LLMs's weight about anything is baked into the very stochastic process by which they gather information -- it contains random trajectories, remember? It is there even though they don't have any feelings about that information, as humans might have. What I'm saying is that the subjectivity (or lack of objectivity) in their judgments is not dependent on them having emotions or feelings about these things, for I presume that they do not. There is a form of subjectivity that enters the mix of LLM so-called "pragmatics," irrespective of emotions or lack there of.
Out of, say, ten possible bullet points an LLM could present to the reader on a given topic, once it has determined that it must cull from this list only four of them (for conciseness sake), how does it decide which four to choose and in which order to present them? In other words, in this context, how does it decide what is 'best'?