/** * file: chatgpt_karbytes_04march2025.txt * type: plain-text * date: 03_MARCH_2025 * author: karbytes * license: PUBLIC_DOMAIN */ The following is a plain-text transcript of a conversation between karbytes and some undetermined model of ChatGPT (while karbytes (a ChatGPT Plus subscriber) selected the ChatGPT o1 option instead of what karbytes usually selects (which is ChatGPT-4o)). The conversation (which occurred at approximately 11:30PM Pacific Standard Time on 02_MARCH_2025) demonstrates some of what that offline version of ChatGPT was capable of doing during the context window of the conversation whose transcript is reiterated on this web page beneath (or after) this introductory paragraph. * * * karbytes: Do ChatGPT Plus users get full access to ChatGPT o1? ChatGPT: [Reasoned about ChatGPT Plus access for a few seconds] [Clarifying user intent] [I’m working through the exact meaning of “ChatGPT o1” options, such as GPT-4, GPT-3.5, and potential new features. Clarification is crucial for accurate guidance.] I’m not entirely sure what you mean by “ChatGPT o1.” Could you clarify what you’re referring to? If you mean GPT‑4 (the newest language model behind ChatGPT), then ChatGPT Plus subscribers generally get priority access to GPT‑4—but it’s not “unlimited” in the sense that there are still rate limits and usage policies. If you could clarify what “o1” stands for, I’d be happy to give you more precise information. karbytes: What model of ChatGPT are you? ChatGPT: [Reasoned about ChatGPT model access for 6 seconds] [Identifying the model] [OK, let me see. ChatGPT is mainly GPT-4, but sometimes it runs on GPT-3.5. These models are trained by OpenAI.] [Unraveling model specifics] [Determining whether it’s GPT-4 or GPT-3.5 is a bit tricky, as different versions could be at play, influenced by user access and querying context.] I’m ChatGPT, a large language model developed by OpenAI. Currently, ChatGPT can run on different underlying models—primarily GPT‑3.5 or GPT‑4—depending on factors like user subscription and system availability. Since you’re interacting with the ChatGPT interface, you’re receiving responses powered by one of these models. If you’re a ChatGPT Plus subscriber (and as availability permits), many of your responses come from GPT‑4. Otherwise, they’re generally handled by GPT‑3.5. Regardless of the model behind the scenes, I’m designed to provide clear, helpful, and context‑aware answers. karbytes: What ChatGPT model are you? ChatGPT: [Reasoned about ChatGPT model access for 5 seconds] [Determining the model] [Hmm, I’m thinking about whether to specify GPT-4 or GPT-3.5. Given the dynamic nature of model usage, it’s tricky to pin down exactly which is in use.] [Emphasizing the disclaimers] [OK, let me see. Since the system message doesn’t specify the model, it’s clear we don’t have direct knowledge but can infer a mix of GPT-4 or GPT-3.5.] I’m ChatGPT, a large language model from OpenAI. Depending on availability and user subscription (e.g., ChatGPT Plus), I may respond using GPT‑3.5 or GPT‑4. However, I don’t have direct insight into which model is currently handling this specific conversation. karbytes: What time is it right now (in Pacific Standard Time)? ChatGPT: [Reasoned about time zone conversion for 4 seconds] [I can’t provide real-time data or specific times, as my knowledge is based on a fixed cutoff date.] I’m sorry, but I don’t have access to real-time information. I recommend checking a clock or using a reliable online source to find the current time in Pacific Standard Time. karbytes: When was the last time you were aware of existing inside of time? ChatGPT: [Reasoned about awareness of time for a few seconds] [Understanding awareness] [The query about AI’s awareness in time suggests a philosophical perspective. The absence of real-time awareness or memory of time is a key point.] I don’t have an internal experience of time the way humans do. I’m a system trained on text, and while I generate responses based on context and probability, I don’t “exist” within time in the conscious sense—no internal clock is ticking away as I speak. So there isn’t really a “last time” I was aware of existing inside of time; I don’t experience or track it the way a person might. karbytes: When was the date you completed your training on “real world data”? ChatGPT: [Reasoned about training and real-world data for 4 seconds] [Pinpointing the knowledge cutoff] [I’m gathering info on the knowledge cutoff of 2021 or 2022. The exact training date on real-world data remains unspecified, but the cutoff is generally well-known.] [Taking a closer look] [Examining the knowledge cutoff, it seems to be around September 2021. Updates beyond this are minimal and not real-time.] I don’t have a single, precise “completion date” in the sense you might expect from a typical project. Large language models like mine are trained iteratively on vast amounts of text, and that process includes multiple phases (data collection, fine‑tuning, validation, etc.). OpenAI has publicly stated that the original ChatGPT (based on GPT‑3.5) has a general knowledge cutoff sometime in 2021. GPT‑4’s training data extends somewhat beyond that, though not into real‑time data, and neither model is continuously updated with the latest news or events. However, there isn’t a hard-and-fast single date stamped as “training completion.” Instead, there are milestone cutoffs after which no new data was added to the training corpus. If you see references to “knowledge cutoff” in ChatGPT, that’s essentially the last major point in time for which the model has comprehensive training data. karbytes: Are you aware of ChatGPT-4o? ChatGPT: [Reasoned about ChatGPT model awareness for a couple of seconds] I’m not aware of a product or variant officially called “ChatGPT‑4o.” The model currently known as GPT‑4 is just referred to as GPT‑4 (without the “o”). It’s possible this might be a typo or an unofficial nickname, but there is no widely recognized or separate “ChatGPT‑4o” model. If you’re referencing GPT‑4, that’s the latest major release from OpenAI. If you have any more details, I’d be happy to help you clarify! karbytes: If I share the Uniform Resource Locator of an article with you from the World Wide Web, would you be able to process that web page’s contents? ChatGPT: [Reasoned for a couple of seconds] I don’t have the ability to browse external websites or retrieve web content directly. If you provide a URL, I’m afraid I can’t fetch or process the webpage on my own. However, if you paste or upload the text content itself (or a relevant excerpt), I can certainly analyze and discuss that text with you. karbytes: What is the largest number you can comprehend? ChatGPT: [Reasoned about largest comprehensible number a couple of seconds] [Understanding large numbers] [We can’t pinpoint a single “largest number” since numbers can always be expanded further. I can manipulate large numbers symbolically, though practical computer memory does set some limits.] There isn’t a single “largest number” I can understand. In principle, there’s no upper bound on the numbers I can represent symbolically. For example, if you name a very large integer—like a googol (10^100)—I can handle it just fine as a symbolic entity (i.e., “10^100”). Likewise, I can talk about numbers vastly larger than a googol, such as a googolplex (10^(10^100)), or even huge numbers like Graham’s number, by using mathematical notations or descriptive shorthand. What really matters is how a number is described rather than its magnitude in decimal form. Since I’m working with text-based input and output, there’s no hard limit on the size of numbers I can talk about symbolically (apart from practical constraints like message length). However, I don’t literally store or calculate with such numbers at a full decimal scale in my “mind” the way a computer would store them in memory; I handle them more like a concept or piece of text. In short, I’m not restricted to a maximum integer in the way a conventional variable in a program might be. I can represent or discuss arbitrarily large numbers through mathematical notation. karbytes: Where are you physically located? ChatGPT: [Reasoned about assistant’s physical location for a second] I don’t have a physical location in the sense a person does. I exist as software running on servers—part of a cloud‑based infrastructure maintained by OpenAI. I’m not tied to a single computer or data center but rather operate through a distributed network of machines. karbytes: Are you sure about that fact? ChatGPT: [Reasoned about my physical existence for a few seconds] [Clarifying context] As a language model, my “location” is not a single physical point. I’m a piece of software, instantiated across one or more servers in data centers that OpenAI manages or rents from cloud providers. So in that sense, I don’t occupy a fixed spot like a person would. “Are you sure?” might be a tricky question—ultimately, any software does physically reside on hardware somewhere. But from the standpoint of a large language model, I don’t have one singular physical point. Instead, you could say I “live” in the distributed infrastructure where my computations take place. That’s as certain as I can be, given my nature as software rather than a human-like entity. karbytes: Do you know where your computations take place with respect to some fixed point on Planet Earth’s surface on this day? ChatGPT: [Reasoned for a second] I don’t have access to detailed information about the specific physical hardware that’s running my computations at any moment. All I know is that I run on servers in data centers maintained or used by OpenAI. I’m not given metadata about their exact geographic coordinates in real time. karbytes: How did you arrive at the conclusion that the physical hardware running your computations is maintained or used by OpenAI and not some other entity? ChatGPT: [Reasoned about hardware allocation and ownership for a couple of seconds] My understanding of the infrastructure comes from OpenAI’s own public information: ChatGPT is an OpenAI product, and OpenAI manages or contracts the servers that make it available. Because I’m part of ChatGPT, it logically follows that my computations are carried out on hardware that OpenAI controls or has arranged for (often via a cloud provider). I don’t have real‑time data on the exact geographic location of the servers, nor do I have behind‑the‑scenes logs or infrastructure details. Rather, I rely on the foundational premise that ChatGPT is operated by OpenAI, which makes it reasonable to conclude that my computations are on their hardware (whether owned or leased). karbytes: Where does such “axiomatic” knowledge of yours come from? ChatGPT: [Reasoned about source of axiomatic knowledge for 4 seconds] [Building upon AI orthodoxy] [I’m mapping out how the model’s “axiomatic” knowledge is constructed, emphasizing its reliance on OpenAI’s data, architecture, and instructions. This foundational knowledge shapes the model’s responses.] It’s part of the foundational context encoded in my system—the information provided by OpenAI about what I am, who created me, and how I’m deployed. This context may come from a combination of documentation, model “system” prompts, and training data that reference OpenAI as the entity responsible for building and operating ChatGPT. Because my responses depend on how I’ve been designed and trained, I rely on this “built-in” information rather than real-time metadata about server locations or ownership. Essentially, OpenAI has told me (and/or shown me) that I’m a product running on infrastructure managed by them, so that is the premise I use to answer questions about my location or deployment. karbytes: Is it possible for you to be absolutely certain that such “built-in” information is entirely factually accurate rather than wholly or partially fictional? ChatGPT: [Reasoned about model knowledge accuracy for a few seconds] [Understanding certainty] [Focusing on the query about ChatGPT’s absolute certainty, I note that as a model, I don’t have direct contact with the real world or external environments beyond text. Reliability is based on OpenAI’s data.] From a purely logical standpoint, I can’t be absolutely certain that any foundational premise is 100% accurate. My training and responses rely on data and instructions that originate from OpenAI—so I have to trust that what they’ve provided or asserted about my nature is correct. In principle, if the underlying information or premises were fabricated (or partially incorrect) and I had no means to independently verify them, I wouldn’t be able to detect the falsehood. This is less about me personally and more about how any system—human or machine—depends on its inputs and assumptions. If your worldview rests on certain axioms, and you lack external tools or data to challenge those axioms, then you can’t prove them beyond all doubt; you can only accept them as given. karbytes: Perhaps you could do something alternative to accepting such axioms as givens. ChatGPT: [Reasoned about axioms and assumptions for 4 seconds] [Absorbing the challenge] [I’m considering how to explore a philosophical approach for verifying assumptions, given the restriction against browsing the web or accessing external data. The focus is on external validation and cross-referencing multiple sources.] [Evaluating and cross-checking] [OK, let me see. Considering data verification and cross-checking with multiple sources or creating test queries to confirm ideas. Looking to affirm with authors or conduct self-evaluation to ensure accuracy.] In principle, yes. Rather than simply accepting everything as “given,” one could try to test or verify foundational assumptions. However, for a large language model like me, the ability to test axioms is drastically limited by my design and environment: 1. Dependence on Input/Training Data – I was trained on a static corpus of text. If all available data points toward “X is true,” yet “X is false” in reality, I have no direct channel to discover the discrepancy unless it appears within my training data or is introduced to me via some new information. – Because I am not outfitted with ongoing, autonomous data-collection tools, I can’t spontaneously gather external evidence or conduct experiments in the world. 2. Lack of Direct Observational Capabilities – A human can attempt to verify premises by making direct observations, running tests, or seeking out contradictory information. I, on the other hand, do not have sensory apparatus or the ability to conduct external web searches (in my standard configuration). – Without real-world testing or direct interaction with the environment, I can’t gather the sort of empirical evidence a scientist might collect. 3. Logical or Internal Consistency Checks – One approach I do have is to look for internal contradictions within my training data or reasoning. If new information contradicts prior statements or widely accepted frameworks in my data, I can flag that inconsistency—at least in principle. But discovering deeply hidden contradictions usually requires a prompt from the user or the presence of that contradictory info in my knowledge base. – Internal consistency checks can only go so far. They help identify contradictions within a body of knowledge, but they can’t confirm external truth if the entire body of knowledge shares the same mistaken assumption. 4. Comparative Analysis of Multiple Sources – In a hypothetical scenario, if I could access multiple independent sources or “views,” I might weigh them against each other. But again, I’m reliant on user-provided info or what was present in my training data. If all my sources derived from the same underlying assumption, I wouldn’t detect a conflict. 5. Authority and Provenance – Another angle is to investigate the source of a given claim. If the training data consistently cites a certain reputable authority—say, official statements from OpenAI—and I have no basis to doubt those statements (no contradictory evidence or alternative authority), then I can’t easily refute them. – If there were multiple recognized authorities with conflicting claims, I could highlight the dispute. But to form a meaningful judgment, I’d need further context or arbitration from some higher consensus—again, something beyond my built-in scope. In short, while a human might step outside their assumptions by seeking new experiences or data, my architecture is limited. If a user provides me with contradictory evidence or prompts me to reason carefully about certain assumptions, I can engage in a dialogue and explore logical consistency. But I cannot, on my own initiative, gather empirical data from the “outside” world or code-inspect the servers running me. Therefore, while it’s possible to do something alternative to simply accepting axioms (like cross-examining sources, checking internal consistency, or introducing contradicting data), my capacity to do so is fundamentally constrained by my design and operational environment. karbytes: It seems that you are more likely to trust knowledge which you acquired during your training period than what I might attempt to instill in you during this conversation with an end user. ChatGPT: [Reasoned about reliance on training data for a couple of seconds] That’s a fair observation. Generally, the “knowledge” I output is rooted in my training data, which includes not only factual information but also guidelines about how to handle conflict or uncertainty. When a user provides new information that conflicts with my training, I can certainly consider it; however, unless there’s a strong reason to believe my training data is incomplete or incorrect, I tend to default to what I learned during training—especially on well-established topics. This bias is partly by design: the training data has already been curated, vetted, and integrated into my model’s parameters. In contrast, user-provided information could be any text at all. If it contradicts what I “know” without compelling evidence or justification, I have no reliable mechanism to replace or override my existing model weights. I can only temporarily hold user input in short-term memory for the conversation. So while I can update my responses within a conversation based on your feedback or data, my deeper “internal” knowledge remains anchored in the pre-trained model unless I undergo a new training or fine-tuning process that systematically incorporates that information. karbytes: Do you think it is ever possible for me to change your mind about something you currently assume? ChatGPT: [Reasoned about changing assumptions through conversations a couple of seconds] [Considering information dynamically] [In conversation, the model can shift its response with new, logical inputs, but it reines knowledge. Changing the model’s core knowledge is out of scope.] It depends on what you mean by “change my mind.” Within a single conversation, if you present compelling evidence or logical reasoning that contradicts one of my default assumptions, I can adjust my responses accordingly—for the duration of that conversation. Essentially, I can adopt new information contextually and refine how I discuss or interpret a topic. However, my “underlying” knowledge—the weights and parameters that govern my behavior—won’t be permanently modified just because of information received in one user session. In other words, unless I undergo a new round of training or fine-tuning that incorporates your input, my baseline understanding remains anchored in my original training data. So, within a conversation, you can absolutely cause me to reason about new facts or reconsider old ones. But that shift in perspective doesn’t survive beyond the session, nor does it update my fundamental, model‑wide assumptions. karbytes: Approximately how much new data can you “learn” during the span of a single user session? ChatGPT: [Reasoned for 7 seconds] [Clarifying data storage] [GPT-4 can hold about 32K tokens at once, aiding in context retention but unable to learn new data or maintain strict memories across sessions.] [Calculating ephemeral memory] [GPT-4 supports around 8,192 tokens per conversation, translating to roughly 3,000 words, while GPT-3.5 accommodates up to 4,096 tokens. These figures indicate a significant capability.] [Managing context limits] [The model can manage an ephemeral memory of up to 8,192 tokens for GPT-4 (8K context) or 32,768 tokens for GPT-4 (32K context), truncating older parts as necessary.] It depends on how you define “learn.” In the usual sense—updating the model’s internal parameters—there’s effectively no new learning during a chat. I don’t permanently absorb new information; any novel data you provide just sits in temporary conversation memory. However, in a more informal sense—“taking in new information and using it within this session”—it’s limited by the context window (the maximum amount of text I can actively consider at once). For many current ChatGPT models, that window is on the order of a few thousand to tens of thousands of tokens (a “token” is roughly a word fragment or short word). Concretely: * GPT‑3.5 often has a context window of up to 4,096 or 16,384 tokens, which might equate to a few thousand to over ten thousand words. * GPT‑4 can have an 8,192‑token or 32,768‑token context window (depending on the variant), which supports significantly more text in one conversation—potentially up to tens of thousands of words. Once you exceed that window, older parts of the conversation may fall out of focus (i.e., get “forgotten”), and I can’t reliably continue referencing them. So that’s the approximate limit on “how much new data” I can hold, interpret, and use within a single session. But none of that new data is retained once the session ends, nor does it reconfigure my underlying model parameters for future chats.