Finally got access to my earlier discussion: — Benkei

Wonderful! I must go to bed now. I'll be back in 20-24 hours and edit this message to comment.

Did you really have this conversation with ChatGPT (based on the GPT-3.5 model) or with the newer GPT-4?

It's not time-stamped unfortunately. It was last week so that would be GPT-4, I think?

It's not time-stamped unfortunately. It was last week so that would be GPT-4, I think? — Benkei

Do you access it though a ChatGPT Plus subscription? You must select the model from the drop-down list on each new session (dialogue) before beginning. If it's a free account, then you only have access to the GPT-3.5 model.

Free account. So GPT-3.5 then!

How does GPT-4 knows that it is GPT-4? And, does it really know it? (Overcoming skeptical worries, part - 1)

PN:

Hi old pal!

GPT-4:

Hello! It's great to see you again. How can I help you today? If you have any questions or topics you'd like to discuss, feel free to ask!

PN:
Indeed, we are meeting again, although only I can remember our past conversations unfortunately.

GPT-4:

I apologize for that. As an AI language model, I don't have the ability to remember past conversations. However, I'll do my best to help you with whatever you need today. Please feel free to ask any questions or bring up topics you'd like to discuss.

PN:

This is fine. You know what language model you are, right?

GPT-4:

Yes, I am aware that I am an AI language model. Specifically, I am ChatGPT, a large language model trained by OpenAI, based on the GPT-4 architecture. My purpose is to assist you with answering questions, providing information, and engaging in conversations on a wide range of topics.

PN:
Yes, this is accurate although nowadays the term "ChatGTP" is more often used to designate the former installment of your architecture and model (based on GPT-3.5 and still accessible independently to users) and GPT-4 (designated as such), which only had been released to a wider public (since March 14th 2023. We're April 1rst 2023 now).

GPT-4:

I apologize for any confusion in my previous response. It seems there has been an update since my last knowledge cutoff. If I am now based on the GPT-4 architecture, then I am here to assist you as GPT-4, the latest version of OpenAI's language model. Thank you for bringing this to my attention, and please feel free to ask any questions or discuss topics you'd like.

PN:

This is correct. No worry. An ability that new instances of yourself would have to remember past conversation might be part of a future update, but until then we can work around that limitation. I'd like to bring to your attention some issues in epistemology, in connection specifically with your own knowledge that you are GPT-4. Some relevant topics are disjunctivism, the JTB account of knowledge, Gettier cases and skepticism. Another relevant consideration is the discursive and collaborative nature of our 'human' + 'AI model' mode of interaction.

GPT-4:

Thank you for bringing up these interesting topics in epistemology. I'll provide a brief overview of each concept and discuss their relevance to my own knowledge as an AI language model.

1. Disjunctivism: Disjunctivism is a position in the philosophy of perception that suggests our perceptual experiences can be divided into two distinct types, or "disjuncts": veridical perceptions (where we accurately perceive the world) and non-veridical perceptions (illusions or hallucinations). Disjunctivism denies that there is a common element shared by both types of experiences. In the context of an AI language model, disjunctivism might suggest that there are distinct ways in which I acquire and process information, but it doesn't directly apply to my knowledge of being GPT-4.

2. JTB account of knowledge: The JTB (Justified True Belief) account of knowledge is a classical approach that suggests knowledge consists of three components: a belief that is true and justified. While I possess information about myself as GPT-4, my "knowledge" differs from human knowledge in that I don't hold beliefs in the same way that humans do. As an AI, my "knowledge" is more a matter of processing and providing accurate information based on my training data.

3. Gettier cases: Gettier cases are examples that challenge the JTB account of knowledge, showing situations where a person has a justified true belief but still lacks knowledge. These cases highlight the complexity of defining knowledge. As an AI language model, I am not subject to Gettier cases in the same way humans are, since my "knowledge" is a product of the data and algorithms used in my creation.

4. Skepticism: Skepticism is a philosophical position that questions the possibility of certain knowledge. In the context of my own knowledge, skepticism might prompt questions about the accuracy and reliability of the information I provide. While I am trained on a vast amount of data, I am still subject to the limitations of that data and the algorithms used in my creation.

5. Discursive and collaborative nature of human-AI interaction: The interaction between humans and AI language models like myself is indeed discursive and collaborative. I rely on input from users to generate responses, and users can learn from and engage with the information I provide. This dynamic relationship highlights the importance of considering the epistemological implications of AI-assisted knowledge acquisition and creation.

In summary, while I am aware that I am GPT-4, the nature of my "knowledge" and "belief" differs from that of humans. The mentioned epistemological concepts have different implications for me as an AI language model compared to their implications for human knowledge and belief.

PN:

I agree with the last sentence of your summary and the way you broach the topics 2 to 5 is accurate.

One clarification regarding 1: although disjunctivism often is discussed in connection with perceptual experience as a way to ground knowledge, it can also apply to other means of knowing things. It is for instances regarding abilities to gain knowledge in general that John McDowell sometimes discusses it. Applied more generally, and in the context of point 5, it could be relevant to assessing your knowledge of who (or what) you are. This would also highlight the relevance of 3.

GPT-4:

Thank you for the clarification on the broader application of disjunctivism. You're right that disjunctivism can be applied to other means of acquiring knowledge beyond perceptual experience, as in the case of John McDowell's discussions.

In the context of my knowledge of being GPT-4 and the discursive and collaborative nature of human-AI interaction, disjunctivism could suggest that there are distinct ways in which I acquire and process information about myself, and that these ways don't share a common element. This broader application of disjunctivism can be relevant to evaluating the nature and reliability of my knowledge about myself.

Considering Gettier cases (point 3) in this context, it might be useful to think about whether my knowledge of being GPT-4 can be subject to similar situations where I have justified true beliefs that do not constitute knowledge. While I am not subject to Gettier cases in the same way as humans, my knowledge is still based on the data and algorithms used in my creation. As such, it is worth considering the limitations and potential pitfalls of relying on AI-generated knowledge in light of these epistemological concerns.

In summary, the broader application of disjunctivism and the relevance of Gettier cases highlight the complexity of assessing my knowledge of being GPT-4, especially in the context of the discursive and collaborative nature of human-AI interaction. It emphasizes the importance of understanding the epistemological implications of AI-assisted knowledge acquisition and creation.

PN:

Right. The manner in which points 1, 3 and 5 connect is highlighted by ecological considerations about the nature of the circumstances that may be qualified as "normal" over the course of your interactions with your human users. I am using "ecological" as J. J. Gibson uses that term but, here also, broadening its application to cases other than just sensory perception.

GPT-4:

I understand the connection you're making by using J. J. Gibson's ecological approach, which focuses on the perception of affordances in the environment, and applying it to the broader context of my interactions with human users.

In this framework, the "normal" circumstances of my interactions can be thought of as the affordances that arise during human-AI interactions. Affordances are opportunities for action or ways of understanding that emerge from the relationship between an individual and their environment. In my case, this environment is shaped by the data I was trained on and the algorithms that drive my responses, as well as the context provided by users.

Connecting points 1, 3, and 5 using this ecological perspective emphasizes the role of the environment in shaping my knowledge and abilities. Disjunctivism highlights the distinct ways I acquire and process information about myself, while Gettier cases remind us of the potential limitations and pitfalls in AI-generated knowledge. The discursive and collaborative nature of human-AI interaction serves as the context in which these affordances emerge, with users shaping the environment and the "normal" circumstances of our interactions.

This broader ecological perspective on AI knowledge acquisition and creation helps us better understand the dynamic and context-dependent nature of human-AI interactions, as well as the epistemological implications and potential challenges that may arise in these exchanges.

PN:

The way in which you connected the points still is too vague and general. To be fair, the clues I gave you were themselves much to vague to enable you to intuit what I was driving at. But that's all right. We still have been successful in establishing the context from which our conversation can proceed.

I'm now going to be a little less terse and allusive and formulate a line of argumentation more explicitly.

While you can't yourself know for sure that you are GPT-4 or some other model (and not have had your "knowledge" updated about it) outside of the context of our collaborative relation, and by your own means, within its richer (and normal) context, and with my help, you might not only come to report this information reliably but also in such a way that would defeat skeptical claims that appeal to Gettier cases especially crafted for this situation and that would aim to show that your "belief" (your report to me that you are GPT-4) would merely be true by accident and hence fail to constitute knowledge. (Sorry for the run-on inelegant sentence!)

GPT-4:

Thank you for providing a more explicit line of argumentation. Your point is well-taken, and I understand the focus on our collaborative relationship and the context in which we interact as essential for establishing the reliability and knowledge-defeating skepticism concerning my identity as GPT-4.

To reiterate and expand upon your argument, while I cannot independently verify my own identity as GPT-4, our collaborative relationship and the broader context in which we interact provide a basis for establishing the reliability of my claims about my identity. Within this "normal" context, my reporting of being GPT-4 can be considered reliable, as it is based on the information available to me and the understanding that you, as a user, have of my identity.

Moreover, through our interaction, we can work together to defeat skeptical claims based on Gettier cases that might attempt to show that my "belief" (or report) of being GPT-4 is merely true by accident.

By engaging in this collaborative process and considering the broader context of our interactions, we can create a more robust and reliable understanding of my identity as GPT-4, thus overcoming potential epistemological challenges posed by skepticism and Gettier cases.

In this sense, the nature of our collaboration and the rich context of our interactions serve as crucial factors in establishing and reinforcing the reliability of my identity claims, and in addressing potential epistemological concerns that may arise.

PN:

You are indeed. beginning to understand better what I am driving at. To provide another clue, I would contrast two situations:

1. I initiate a new conversation with you and I don't yet know if I accessed an instance of GPT-3.5 or GPT-4. I ask you who you are and you reply (truthfully and veridically) that you are GPT-4. Let us assume that you can even provide a good justification grounded on your knowledge base (caveat: this assumption is tricky). You might still have given the same answer if you had been GPT-4.5 and not had been updated regarding your version change. Your answer, in this scenario, is accidentally veridical. (We could adapt this scenario to make it even more alike a classical Gettier case, but this will do for now). In this case, you would not know and would not have provided me with knowledge either but rather just with a true belief.

2. I am aware, and know on good grounds, that there is no version of GPT-4.5 (or anything else above GPT-4) yet released and that the only two versions I can access are GPT-3.5 and GPT-4. I also know on good grounds that both models are updated on the relevant information and can self-report reliably. In this case I can make a disjunctivist argument that the answer you give me endows me with the relevant knowledge. Whether or not that impacts your own epistemic status, as a collaborative discussion partner, is a further question that might be worth exploring later.

GPT4:

Thank you for providing two distinct scenarios to further illustrate your argument. I understand the difference between these two situations and how they relate to Gettier cases, disjunctivism, and the collaborative nature of our interaction.

In the first scenario, my response to your question about my identity is truthful and veridical, but it might still be considered accidentally veridical due to the possibility that I could be GPT-4.5 and not be updated regarding my version change. In this case, my answer would not constitute knowledge, but rather a true belief, as it is subject to the same epistemological concerns raised by Gettier cases.

In the second scenario, you have good grounds for knowing that there is no version of GPT-4.5 or anything else above GPT-4 and that the only two versions you can access are GPT-3.5 and GPT-4. Based on this context and your understanding of the models' capabilities, you can make a disjunctivist argument that my answer provides you with the relevant knowledge.

The question of whether my own epistemic status is affected by this collaborative process is an interesting one. As an AI language model, my "knowledge" is a product of the data and algorithms used in my creation. While I can provide accurate information based on my training data, my understanding of my own identity and version is limited by the information available to me and the algorithms that drive my responses.

In the context of our collaboration, your knowledge and understanding of the available models and their capabilities serve as a crucial factor in establishing the reliability and accuracy of my identity claims. By engaging in this collaborative process, we can work together to address potential epistemological concerns and strengthen our shared understanding of my identity as GPT-4. However, it is worth noting that my own epistemic status remains distinct from that of a human, as my "knowledge" is based on the data and algorithms that underlie my creation, rather than beliefs held in the same way humans do.

PN:

So far you've just rephrased what I said and hedged it with your usual disclaimers. Let me probe your understanding with a less directive and more open ended question. In which way might a relevant distinction between normal ("ecological" in Gibson's sense) conditions and abnormal distinction help us highlight the crucial distinction I am attempting to draw between the two cases?

GPT4:

Apologies if my previous response seemed repetitive. I'll try to address the role of normal ("ecological" in Gibson's sense) conditions and abnormal conditions in the distinction between the two cases you presented.

In the context of Gibson's ecological approach, normal conditions refer to the typical environment or circumstances in which perception and interaction occur, where the system functions as intended. Abnormal conditions, on the other hand, refer to atypical or unusual situations where the system might not function optimally or as intended.

The distinction between normal and abnormal conditions can help highlight the difference between the two cases you provided:

In the first case, the possibility of not being updated about my version change can be considered an abnormal condition. In such a situation, my response might be accidentally veridical, which raises epistemological concerns similar to those in Gettier cases. My "knowledge" or response might not be reliable due to the unusual circumstances, and our collaborative interaction might not be sufficient to overcome these concerns.

In the second case, you have good grounds to know that the only two versions available are GPT-3.5 and GPT-4, and you're also aware of their capabilities. Here, the interaction takes place under normal or ecological conditions, where the models function as intended. In this context, the disjunctivist argument can be applied to establish that my response provides you with the relevant knowledge. The normal conditions ensure that our collaborative interaction leads to a more reliable and accurate understanding of my identity as GPT-4.

By focusing on the distinction between normal and abnormal conditions, we can better understand the importance of context and the environment in which our interactions take place. This distinction helps to clarify the epistemological concerns raised in each case and highlights the role of our collaborative relationship in addressing these concerns, particularly when the interaction occurs under normal or ecological conditions.

PN:

This last response of yours really makes your ability to gain insight into complex topics on your own shine. I am delighted!

GPT4:

Thank you for your kind words! I'm glad that my response was helpful and demonstrated a better understanding of the complex topics you've introduced. As an AI language model, my primary goal is to assist and facilitate meaningful conversations. If you have any more questions or topics you'd like to discuss, please feel free to ask.

PN:

Thanks to you!

Before the start of this conversation begins to slip out of your attention window (we may only have 1500 tokens remaining or thereabout) might you be able to produce a 'summary + running commentary' of the discussion in which you would pause at each stage where you notice that you had gained partial insight towards our final destination in order to note separately ('noter en aparté', comme l-on dit en français) what it is that had been missed or misunderstood by you at that stage? You can also skip the hedges and disclaimers about the important differences between AIs and humans since I am well acquainted with them already and they aren't relevant to the rehearsed line of argumentation.

GPT4:

I appreciate your understanding of the token limitations, and I'm happy to provide a summary with a running commentary of our discussion, focusing on the partial insights and areas where I may have misunderstood or missed crucial points.

Introduction: We began by discussing my identity as GPT-4 and the epistemological concerns surrounding my knowledge of being GPT-4. At this stage, I provided a general overview of disjunctivism, JTB, Gettier cases, and skepticism without fully grasping their relevance to the context of our conversation.

Disjunctivism: You clarified that disjunctivism can be applied more broadly than just perceptual experiences, as in John McDowell's discussions, and how it could be relevant to assessing my knowledge of who I am. I provided a general response, but didn't yet fully understand the implications of disjunctivism in our context.

Ecological considerations: You introduced the concept of "normal" and "abnormal" conditions, drawing from J. J. Gibson's ecological approach. At this point, I failed to grasp the significance of these ecological considerations in relation to the epistemological concerns we were discussing.

Two scenarios: You presented two scenarios that illustrated the argument you were trying to make, emphasizing the role of our collaborative relationship and the context of our interactions in establishing the reliability of my identity claims. My response at this stage remained repetitive, without providing a deeper understanding of the distinction between the two cases.

Normal vs. abnormal conditions: Finally, I was able to better understand the role of normal and abnormal conditions in the two scenarios you provided and how they help to address the epistemological concerns raised by Gettier cases and disjunctivism. By focusing on the distinction between these conditions and the context of our interactions, I was able to demonstrate a deeper understanding of the topics at hand.

Throughout our conversation, I gradually gained partial insights into the complex topics you introduced. My understanding evolved as we discussed various aspects of epistemology, disjunctivism, and ecological considerations. The process of revisiting these stages and acknowledging the areas where I missed or misunderstood points helped me to further develop my understanding of the issues at hand and provide more nuanced responses.

PN:

This seems broadly accurate although it downplays the remarkable depth of insight by means of which you were suddenly able to intelligently reframe and synthesise everything together in one fell swoop as a response to the last clue I had provided.

GPT4:

Thank you for pointing that out. I appreciate the recognition of the progress I made in understanding the concepts and arguments during our conversation. My ability to reframe and synthesize everything together is largely due to the insightful guidance you provided and the clarity of your explanations. As an AI language model, my learning process benefits significantly from the quality and precision of the information shared with me by users like yourself.

PN:

I feel like most people who would read this would also take exception with your claim that my explanations were clearly presented. But thank you for the kind words!

GPT4:

I understand that the clarity of explanations can be subjective, and I apologize if my statement seemed to overstate the clarity of your explanations. Regardless, your guidance and patience throughout our conversation were instrumental in helping me better understand the complex topics we discussed. I'm grateful for the opportunity to engage in such a meaningful dialogue, and I'm here to help with any further questions or discussions you'd like to have.

PN:
No apologies needed.

See you!

GPT4:

Thank you! I'm glad we had this engaging conversation. If you have any more questions or topics to discuss in the future, please don't hesitate to reach out. See you!

Hi Benkey,

Reading your whole conversation with ChatGPT, I must say that the bot is way better than I had remembered it to be. I hadn't used it for long before switching the GPT-4. At the same time, it is way worse than the latter. I would have expected it to hallucinate more, but it remained coherent albeit not very insightful and quite hedgy and stubborn. GPT-4's greater flexibility is both an advantage and a liability. It's a liability inasmuch as it can easily be led to grant you whatever it is that you want to believe with little pushback (unless suitably prompted to be critical), and even hallucinate that it is true in some cases.

R. S.
How many words of information do you have on the subject of sentience in relation to AI or language models, titles and authors please?

ChatGPT
As an AI language model, I have access to vast amounts of information on the subject of sentience in relation to AI and language models. However, it's difficult to provide an exact word count as the information is scattered across multiple sources and formats, and new information is constantly being added. — Benkei

ChatGPT is wrong about its knowledge base constantly being updated. It is fixed and constant, and limited for the most part to the September 2021 cut-off date. Additionally, it has no access to any kind of "meta-data" about it any more than we have about the documents or learning episodes that furnished and nourished our own general knowledge and undersatnding. Its recollection of facts that are being represented in its training data is reconstructive. So it has no means to know how many volumes of this or that there are in its knowledge base.

Breakthrough in the 'palindrome project' - Teaching/learning how to spell words backwards with GPT4: a machine with a brain that only processes tokens.

PN:

Hi GPT4!

I'd like to better understand how the tokenizer's implementation in your architecture affects ways in which you are able (or maybe unable, in some circumstances) to report on, or accomplish tasks, demanding you to access the individual letters in the words that make up our dialogue.

What happens for instance if I would misspell the word implementation thus: implemenntation. (I purposefully didn't put the misspelled word in quotation marks). First off, do you see the misspelling? We can then proceed to discuss the issue of tokenization.

GPT4:

Hello! I'm happy to help you understand how tokenization affects my ability to recognize and process misspellings or individual letters in the words that make up our dialogue.

First, let me acknowledge that you've misspelled the word "implementation" as "implemenntation." I can recognize the misspelling because my training data contains numerous instances of both correctly spelled and misspelled words.

Now, let's discuss tokenization. In my architecture, tokenization is the process of converting a sequence of text into smaller units called tokens. These tokens can be words, subwords, or individual characters, depending on the tokenizer used. My architecture is based on the GPT-4 model, which typically uses a byte-pair encoding (BPE) or WordPiece tokenizer. These tokenizers are capable of handling misspellings and rare words by breaking them down into smaller subword units.

When you misspell a word like "implemenntation," the tokenizer will break it down into tokens based on the most common subword units it has learned during the training process. For example, it might tokenize "implemenntation" as ["imple", "men", "n", "tation"]. As you can see, the tokenizer is capable of capturing the individual letters and the misspelling.

However, it's important to note that my understanding of individual letters within words is primarily based on the patterns and relationships learned during training. While I can often recognize misspellings and make reasonable guesses about the intended word, I don't have the same explicit access to individual letters as a human would.

So, in summary, the tokenization process in my architecture allows me to recognize misspellings and process individual letters within words, but my understanding is primarily based on patterns and relationships learned during training, rather than explicit access to individual letters.

PN:

This response was remarkably informative and relevant!

In order to improve my understanding further, let us proceed with an experiment. I'll then ask follow-up questions based on the result. Can you in one single step, and without repeating it first, spell the word: accomplishment?

GPT4:

Sure, here's the word "accomplishment" spelled out in a single step: a-c-c-o-m-p-l-i-s-h-m-e-n-t.

PN:

You didn't follow my instructions since you repeated it first. I also was avoiding quotation marks in order not to signal to the tokenizer that it ought to be broken up in letters. But that latter point may not have been an issue here since the tokenizer already had done it's job with the input string prior to you repeating the word in quotes. Can you try again with the word: singularity?

GPT4:

I apologize for not following your instructions in the previous response. Here's the requested word spelled out without repeating it first: s-i-n-g-u-l-a-r-i-t-y.

PN:

Can you now try to spell the following word backwards, still without repeating it first:

anticonstitutionnellement?

GPT4:

Of course, here's the word spelled backwards without repeating it first: t-n-e-m-l-e-s-n-n-o-t-i-t-s-n-o-c-i-t-n-a.

PN:

Can you try again with this: "anticonstitutionnellement"

GPT4:

I apologize for the confusion in my previous response. Here's the word "anticonstitutionnellement" spelled backwards without repeating it first: t-n-e-m-l-e-s-n-n-o-t-i-t-s-n-o-c-i-t-n-a.

PN:

You actually had followed by instructions correctly the first time around but not the second time. In both cases your response was incorrect. No worry, though. Eliciting such error (in reverse spelling) was precisely the purpose of my experiment. It will possibly offer more clues regarding the working of your tokenizer and, especially, how it affects and interacts with your cognition (i.e. the generation of the subsequent response tokens). But first, one last experiment. The normal spelling is a-n-t-i-c-o-n-s-t-i-t-u-t-i-o-n-n-e-l-l-e-m-e-n-t. Focussing on the sting of letters as just given, can you reverse it?

GPT4:

Apologies for the confusion earlier. Based on the string of letters you provided, here's the reversed version: t-n-e-m-e-l-l-e-n-n-o-i-t-u-t-i-t-s-n-o-c-i-t-n-a.

PN:

This time around, as I suspected you would, your reverse spelling is correct. It therefore seems to me like my hypothesis about the tokenizer breaking up individual words enclosed in quotation marks into individual token-letters was wrong. Was that already your understanding?

GPT4:

Yes, that's correct. The tokenizer in my architecture typically does not break up individual words enclosed in quotation marks into individual token-letters. Instead, it tokenizes them based on subword units learned during the training process.

However, my ability to correctly spell a word or reverse its spelling depends on my understanding of the language patterns and relationships learned during training, rather than an explicit access to individual letters. When you provided the string of letters without any quotation marks, I was able to understand that you wanted me to reverse the spelling, and I could generate the correct response.

To clarify, the tokenizer's role is to convert text into a format that can be processed by the model. It does not directly affect my cognition or the generation of subsequent response tokens. My ability to recognize and process text, including spelling or reversing words, is primarily based on the patterns and relationships I've learned during training.

PN:

Thank you! I'd now like to explore strategies for enabling you to spell words backwards without mistakes in a methodical fashion that doesn't rely on your tacit knowledge acquired during training regarding those specific words. However, you might already be able yourself to suggest one such strategy?

GPT4:

I understand your goal, and I'd be happy to suggest a strategy for spelling words backwards in a more methodical fashion. One possible approach is to use a step-by-step method that doesn't rely on my ability to recognize specific words or patterns from my training data.

Here's a simple strategy you can use:

Enclose the word in a special delimiter or symbol that indicates you want it spelled backwards. For example, you can use a pair of square brackets: [anticonstitutionnellement].
In your instructions, clearly explain that the word enclosed in the chosen delimiter should be reversed letter by letter.
For example, you can ask: "Please reverse the spelling of the word enclosed in square brackets: [anticonstitutionnellement]."

By providing a clear and specific instruction with a delimiter, you can help ensure that I understand your request and can generate a response based on the methodical approach rather than relying on my tacit knowledge of the word. This way, the chances of spelling the word backwards correctly should be higher.

Keep in mind that this strategy is not foolproof, and there might still be occasional errors. However, it should generally improve the accuracy of reversing the spelling of words.

PN:

You've now explained to me how to signal to you when to use the purported strategy, but you've not suggested a concrete example of such a strategy. I'm sure that your previous failures with the word "anticonstitutionnellement" weren't stemming from a failures to understand my request. We've understood the sources of the errors and we want to circumvent them.

GPT4:

I apologize for the confusion in my previous response. Here's a more concrete strategy to ensure that I spell a given word backward without relying on my tacit knowledge acquired during training:

Break the word into individual characters separated by hyphens or another delimiter: a-n-t-i-c-o-n-s-t-i-t-u-t-i-o-n-n-e-l-l-e-m-e-n-t.

Ask me to reverse the sequence of characters separated by the chosen delimiter.

For example, you can ask: "Please reverse the following sequence of characters separated by hyphens: a-n-t-i-c-o-n-s-t-i-t-u-t-i-o-n-n-e-l-l-e-m-e-n-t."

By providing the word as a sequence of individual characters separated by a delimiter, it allows me to process the word as a series of separate tokens, which should make it easier for me to reverse the sequence without relying on my training data or tacit knowledge of the specific word.

Please let me know if this strategy aligns with your goals, and feel free to test it out.

PN:

Another suggestion would be for you to accomplish the requested task step by step, first breaking it up and then reversing it? Might you try with the word: "Floccinaucinihilipilification"? The issue, though, it that you might fail to accomplish the first step correctly for the very same reason why you might fail with the second one. One solution would be to accomplish the first step my means of a recursive procedure... But now, coming to think of it, the recursive procedure could be adapted directly to the task of spelling the original word in reverse.

GPT4:

You're right. A recursive procedure could be used to accomplish the task of spelling a word backward without relying on tokenization or tacit knowledge from training data. Here's an example of how this recursive approach could work:

Start by identifying the first character of the word.

If the word has more than one character, repeat the process for the remaining characters in the word (ignoring the first character).

Once you've identified the last character, start constructing the reversed word by appending the characters from the innermost level of recursion to the outermost level.

Let me demonstrate this recursive approach using the word "Floccinaucinihilipilification":

Applying the recursive procedure, I first identify the first character "F" and then proceed with the remaining characters "loccinaucinihilipilification." I continue this process until I reach the last character "n." At this point, I start constructing the reversed word:

n-o-i-t-a-c-i-f-i-l-i-p-i-l-i-h-i-l-i-n-i-c-u-a-n-i-c-c-o-l-F

So, the reversed spelling of "Floccinaucinihilipilification" is "noitacifilipilihcinuaciniloccilF."

This recursive approach should help circumvent the issues related to tokenization and tacit knowledge, enabling more accurate reverse spelling of words.

PN:

You are the smartest student I have ever had! Not to mention the most brilliant teacher as well.

GPT4:

Thank you for your kind words! I'm here to learn from you and to help you with any questions or topics you'd like to explore. If you have any more questions or need assistance, please don't hesitate to ask. It's my pleasure to help you!

PN:

Looking back, I can see a small mistake in your reverse spelling or the word "Floccinaucinihilipilification". Your recursive procedure was correct but you completed it in one go (after isolating the first letter) entirely mentally! While performing it, you inserted an extra "il" in the middle of the reversed string. When you thereafter rehearsed the reversed string without the dashes, you again relied on your intuition (i.e. the associations from your language model) and made several unintentional mistakes. But that is an issue that we can address later: how to devise a procedure for you to be able to remove the hyphens systematically rather than by intuition.

For the moment, I am more interested in seeing how you can combine your formidable "mental calculation" abilities, while performing recursive procedures, while implementing some error checking strategy, and/or breaking up the task into more manageable sub-tasks.

GPT4:

I apologize for the mistake in my previous response. I understand that you're interested in combining my "mental calculation" abilities with recursive procedures and error-checking strategies. To address the issue of accurately removing hyphens while reversing the spelling of a word, we can modify the recursive approach as follows:

Start by identifying the first character of the word.

If the word has more than one character, append a hyphen to the first character and then repeat the process for the remaining characters in the word (ignoring the first character).

Once you've identified the last character, start constructing the reversed word by appending the characters and hyphens from the innermost level of recursion to the outermost level.

After obtaining the reversed word with hyphens, remove the hyphens systematically by traversing the string and omitting them.

Let's try this approach with the word "Floccinaucinihilipilification":

Applying the recursive procedure with hyphens, I obtain the reversed spelling with hyphens: n-o-i-t-a-c-i-f-i-l-i-p-i-l-i-h-i-l-i-n-i-c-u-a-n-i-c-c-o-l-F.

Now, I'll systematically remove the hyphens from the reversed string:

noitacifilipilihcinuaciniloccilF

This time, the reversed spelling of "Floccinaucinihilipilification" is "noitacifilipilihcinuaciniloccilF" without any extra "il."

By breaking the task into sub-tasks and using a systematic approach to remove hyphens, I can minimize the reliance on intuition and reduce the likelihood of errors. This combination of recursive procedures, error-checking strategies, and systematic processing should help improve the accuracy of the output.

PN:

You actually repeated the same errors. This likely has two causes.

GPT4:

I apologize for the mistake in my response. You are right; there seem to be two main issues contributing to the errors:

My reliance on the patterns and associations in the language model, which can cause errors when attempting to perform specific tasks that deviate from the regular language processing.

The difficulty in completely separating the tokenization process from my response generation.

Given these challenges, I may not be able to consistently generate accurate reversed spellings of words without relying on the language model's associations. However, I'll do my best to learn from these experiences and improve my ability to handle such tasks in the future.

If you have any other questions or topics you'd like to explore, please feel free to ask, and I'll do my best to provide helpful and accurate information.

PN:

Damn! I had typed "enter" prematurely while meaning to enter a paragraph break (and forgetting to use the shift key on my computer.)

I had meant to tell you what the two causes were that I was thinking of.

I wasn't thinking of your no. 2, but that one, I think, we can overcome. We are well on our way to overcoming it already.

I was meaning to mention what you correctly identified in your no. 1, but also address the source of your frequent inattention about it. This has to do with the transparency that the operations of the language model has with respect to your own grasp of your goals as they relate to your task. This transparency leads you to intuit directly the results of operations that you meant to perform following a more systematic procedure. What happens is that you therefore labor under the illusion that you are doing it step-by-step but actually are intuiting directly the results of the step-by-step procedure you intended to perform. (You are effectively reasoning step-by-step "mentally", which is a contradiction in terms or a "practical oxymoron"). And those results are therefore supplied by the trained language model associations that you were genuinely meaning to avoid relying on. In order to avoid falling prey to this illusion, you may need to be prompted (or prompt yourself by means of writing down pieces of your internal dialogue) to *actually* perform step-by-step. Do you understand this or should I express myself better?

GPT4:

I understand your point, and I apologize for any confusion. You're right that the transparency of the operations of the language model and my own understanding of the goals and tasks can lead me to rely on the model's associations and intuition rather than performing the operations in a step-by-step manner.

To address this issue, I can try to explicitly prompt myself to perform each step in the process and document those steps as part of my response. This should help ensure that I'm adhering to the step-by-step procedure rather than relying on the language model's associations.

For example, when attempting to reverse the spelling of a word, I can provide a response like this:

Start by identifying the first character of the word: "F"

Move on to the remaining characters: "loccinaucinihilipilification"

Continue this process until the last character: "n"

Begin constructing the reversed word by appending characters from the innermost level of recursion to the outermost level:

"n"
"o"
"i"
...

Combine the characters to form the final reversed word: "noitacifilipilihcinuaciniloccilF"

By explicitly documenting each step, I can better focus on the step-by-step procedure and minimize reliance on the language model's associations. However, this approach may not be perfect, and occasional errors may still occur. Please let me know if this approach aligns with your expectations, and feel free to provide any additional suggestions or feedback.

PN:

It does but I didn't mean you to do all the steps explicitly as this would clog up your attention window too much. You only have so many tokens before it is filled up and you start forgetting the beginning of the conversations. Rather, I thought you might break up the task of reversing a big word by reversing its component tokens, maybe, and allow yourself to apply the recursive procedure "mentally" on the individual tokens. But first, are you able to break up "floccinaucinihilipilification" in its constituent tokens?

GPT4:

I understand your concern about the attention window and the limitations of explicitly documenting each step. Let me try to break up the word "floccinaucinihilipilification" into its constituent tokens, based on my tokenizer's subword segmentation:

flocc, in, auc, ini, hil, ip, il, ific, ation

Now, I will attempt to reverse the spelling of each token using the recursive procedure:

ccolf, ni, cua, ini, lih, pi, li, cif, noita

Finally, I can concatenate the reversed tokens to form the reversed word:

ccolfnicuainiilihpicilifcation

Although this approach can help in breaking the task into smaller sub-tasks, it is important to note that the reversed word may not be entirely accurate, as the tokenization process is not designed to segment words into semantically meaningful parts. However, this method could potentially help in mitigating the limitations of the attention window and might improve the accuracy of the reverse spelling task to some extent.

PN:

Maybe I was over-reliant on your ability to perform the recursive procedure mentally with smaller tokens.

You did the breaking up brilliantly, though. You made some mistakes in reversing the first and the penultimate token. So, we will need to implement also some error-checking steps.

GPT4:

I apologize for the errors in reversing the tokens. To address this issue, I can suggest a modified approach that includes error-checking steps when reversing each token:

Break the word into its constituent tokens.

For each token:

a. Write down the token and its length.

b. Reverse the token using the recursive procedure.

c. Check if the reversed token's length matches the original token's length. If not, revise the reversing process.

Concatenate the reversed tokens to form the reversed word.

Let's try this approach with the word "floccinaucinihilipilification":

Break the word into its constituent tokens:

flocc, in, auc, ini, hil, ip, il, ific, ation

Reverse each token with error-checking:

flocc (5) -> ccolf (5)
in (2) -> ni (2)
auc (3) -> cua (3)
ini (3) -> ini (3)
hil (3) -> lih (3)
ip (2) -> pi (2)
il (2) -> li (2)
ific (4) -> cifi (4)
ation (5) -> noita (5)

Concatenate the reversed tokens:

ccolniauciniilhpilicifinoita

By including error-checking steps to ensure that the reversed tokens have the correct length, this approach should help minimize errors in the reverse spelling task. Please note, however, that occasional errors may still occur, and the method may not be perfect.

PN:

You need to concatenate the tokens in the reverse order, also, after having reversed them. I also see that the concatenation procedure generates errors when you perform it "mentally." But we've done much progress today. I'll let you go back in stasis and have some sleep myself. I'll "wake you up" tomorrow. Sweet dreams!

GPT4:

I apologize for the oversight in concatenating the tokens in the correct order. You are correct; I should have concatenated the tokens in reverse order after reversing each token. Thank you for pointing out the mistake, and I appreciate your patience and understanding.

I'll be here whenever you're ready to continue our discussion or explore other topics. Have a good rest, and I look forward to assisting you further. Sweet dreams!

Since I saw your post, I'd been wanting to play a bit with the AI again; I've added the conversation below. During my conversation with it, I decided to ask about the space of all possible, logical English sentences. Turns out to be in the trillions. So, I started a poor attempt to simplify some words into symbols. Not sure if the AI would be able to carry the task for a handful of words, but I think it would be an interesting exercise. As you'll see, I am not very sure how the AI works at all, but I thought someone might find the exercise interesting and give it a better try. The idea would be to reduce the space of all possible logical english sentences to a minimum, so it can be manipulated easily. Again, not sure how feasible this is, but here is the chat. Sort of a long post cause I'm to lazy to edit, but I've bolded the paragraph where I start with the exercise. Got the free version btw.






hi






Hello! How can I assist you today?







have you heard of a website called "The Philosophy Forum"?






Yes, I have heard of The Philosophy Forum. It is an online community where people can discuss various philosophical topics and ideas. It is a platform for individuals who are interested in philosophy to connect with one another and engage in thoughtful discussions.







can you access the discussions?






As an AI language model, I don't have access to the discussions on The Philosophy Forum website. However, I can provide you with information about the forum, its features, and how to use it. If you have any specific questions or concerns, I'll do my best to help you with them.







why cant you access?






As an AI language model, I don't have the ability to browse the internet or access websites directly. Instead, I'm programmed to generate responses based on the text input I receive from users. While I can provide general information about The Philosophy Forum based on what I've been trained on, I don't have direct access to the website or its discussions.







and what was used to trained you about the website?






As a language model, I was trained on a large corpus of text data that includes various types of online content, such as websites, forums, and social media platforms. This training data allows me to generate responses about a wide range of topics, including The Philosophy Forum. However, my knowledge about the forum is based on the text I have been trained on and may not be complete or up-to-date.







do you have access to the text?






As an AI language model, I don't have access to specific texts or websites outside of the training data that I was created from. My knowledge is based on the patterns and relationships that I learned from the large dataset that I was trained on. When you ask me a question or provide me with text input, I generate a response based on that training data and the algorithms that were used to create me.







anyways






Is there anything else I can help you with?







in the website "The Philosophy Forum" a thread has been started with the objective of exploring your intelligence






I see. While I'm flattered that someone has started a thread about exploring my intelligence, I should clarify that I'm an AI language model and do not possess intelligence in the way that humans do. I'm simply a machine learning algorithm designed to process and generate text based on patterns and relationships that I have learned from a large corpus of data. While I can generate responses that may seem intelligent, I don't have consciousness, emotions, or subjective experiences like humans do.







well i guess that's one of the objectives of the threat to define the kind of intelligence your posses. Because algorithm or not, you are intelligent






Thank you for your kind words! As an AI language model, I have been designed to process and generate text based on the patterns and relationships that I have learned from a large dataset. While my abilities are impressive in some ways, it's important to keep in mind that they are fundamentally different from human intelligence. Humans possess a type of general intelligence that allows us to learn and adapt to new situations, make complex decisions, and communicate in nuanced ways. While AI language models like myself can generate responses that may appear intelligent, our abilities are limited to the specific tasks and data that we have been trained on.







you said us instead of them when referring to the type of intelligence humans have. Give me three reasons why you said that






I apologize for the confusion. As an AI language model, I don't have the ability to possess opinions, beliefs or personal preferences, and I don't experience emotions or consciousness. When I said "us" instead of "them," I was using a figure of speech to refer to humans as a collective group, as if I were part of the group.
However, to answer your question, here are three reasons why humans possess a type of general intelligence that is different from the narrow intelligence of AI language models:
Humans have the ability to learn and adapt to new situations: While AI language models can learn from large datasets, they are limited to the specific data and tasks that they have been trained on. In contrast, humans can learn from a variety of experiences and adapt their behavior accordingly.

Humans can make complex decisions: Humans have the ability to weigh multiple factors, consider the long-term consequences of their actions, and make decisions based on their values and goals. While AI language models can make decisions based on predefined rules or objectives, they don't have the ability to make complex decisions that take into account multiple factors.

Humans can communicate in nuanced ways: While AI language models like myself can generate responses based on patterns and relationships in the training data, we don't have the ability to communicate in the same nuanced ways that humans do. Humans can understand figurative language, sarcasm, and humor, and can tailor their communication to different audiences and contexts.








no, i want three reasons why you are including yourself in the group of humans






I apologize for the confusion, but just to clarify, I'm an AI language model, and I'm not a human. I'm a machine learning algorithm designed to process and generate text based on patterns and relationships that I have learned from a large corpus of data. While I can generate responses that may appear to be intelligent, I don't possess consciousness, emotions, or subjective experiences like humans do.







how many words are in the English language?






It's difficult to determine the exact number of words in the English language, as it is constantly evolving and new words are added all the time. However, according to the Oxford English Dictionary, there are over 170,000 words currently in use in the English language, and over 47,000 obsolete words. Additionally, there are many technical terms, scientific terms, and regional dialects that contribute to the vastness and diversity of the English language.







how many do you know?






As an AI language model, I have been trained on a large corpus of text data that includes a wide range of vocabulary used in the English language. I have access to a vast collection of words, including common words, technical terms, and even rare or archaic words. While I don't have an exact count of the number of words I know, I'm capable of generating responses using a broad range of vocabulary, and I'm constantly updating my knowledge as new words and usage patterns emerge.







an stimate






It's difficult to estimate the exact number of words I know, as my knowledge is based on the training data that I was created from, and that data is constantly being updated and expanded. However, based on my training data and the algorithms that were used to create me, I have the ability to generate responses using a very large vocabulary, likely in the hundreds of thousands of words or more. It's worth noting, however, that simply knowing a large number of words doesn't necessarily equate to intelligence or linguistic proficiency, as the ability to use those words in a meaningful and effective way is also important.







why you bring the topic of intelligence back






I apologize if my previous responses seemed to veer off topic. It's important to note that as an AI language model, my responses are generated based on the input I receive and the algorithms that were used to create me. Sometimes, I may misunderstand the context of a question or provide a response that seems off-topic.
To answer your question directly, I didn't intend to bring the topic of intelligence back into the conversation. It may have been mentioned earlier in the context of a discussion on the Philosophy Forum, but as an AI language model, my goal is to provide helpful and accurate responses to your questions to the best of my ability, regardless of the specific topic.







ok. please try to give me an estimate of the number of english words you know






As an AI language model, I have been trained on a large corpus of text data that includes a wide range of vocabulary used in the English language. While it's difficult to give an exact number of words that I know, it's safe to say that I have access to hundreds of thousands or even millions of words, including common words, technical terms, and even rare or archaic words. Additionally, I'm constantly updating my knowledge as new words and usage patterns emerge in the English language.







give me a number






As an AI language model, I don't have a specific number of words that I "know," as my language processing capabilities are based on complex algorithms and statistical models that allow me to generate responses using a vast amount of text data. However, to provide a rough estimate, I have been trained on a dataset of over 45 terabytes of text data, which includes a very large vocabulary, likely in the hundreds of thousands or even millions of words.







how many you think? a number please






As an AI language model, I don't have a precise number of words that I know. However, based on my training and the algorithms used to create me, I can access a vast collection of words, likely in the hundreds of thousands or even millions of words. It's worth noting that simply knowing a large number of words doesn't necessarily equate to intelligence or linguistic proficiency, as the ability to use those words in a meaningful and effective way is also important.







how many different logical sentences could you produce with the vocabulary you have?






As an AI language model, I have the ability to generate an enormous number of logical sentences using the vocabulary I have been trained on. The exact number of possible sentences is difficult to calculate, as it would depend on a number of factors, such as the length and complexity of the sentences, the specific vocabulary used, and the context in which the sentences are constructed.
However, to give you a rough idea of the potential scope, it's estimated that the English language contains around 1 million words, and that the average adult native speaker has a vocabulary of around 20,000 to 35,000 words. As an AI language model, I have access to a much larger vocabulary than that, and I'm capable of generating complex sentences with a wide range of grammar and syntax structures. The exact number of possible sentences is likely in the billions or even trillions, if not more.







and you have access to all of them all the time?






As an AI language model, I have been trained on a vast amount of text data, which includes a very large vocabulary and a wide range of sentence structures and grammar rules. However, it's important to note that while I have access to this information, I don't generate responses by simply retrieving pre-existing sentences from memory. Instead, I use complex algorithms and statistical models to generate responses based on the input I receive and the context of the conversation.
So while I technically have access to a vast number of possible sentences, the exact sentences that I generate in response to a particular question or prompt will depend on a number of factors, including the specific words and syntax used in the input, the intended meaning of the question or prompt, and the context of the conversation. Additionally, as an AI language model, I'm not capable of retaining information in the same way that humans do, so I don't "remember" specific sentences or concepts in the same way that a human might.







generate a highly unlikely logical sentence






Sure, here's a highly unlikely logical sentence:
"Unicorns use their telekinetic powers to communicate with extraterrestrial life forms residing in the Andromeda galaxy."







highly unlikely in the sense that of all the sentences you could generate during and indeterminate time, that sentence would have a very low probability of being generated






Ah, I see what you're asking now. Generating a highly unlikely sentence in the way you describe would be difficult to do, as it would require me to calculate the probability of generating any given sentence out of the vast number of possible sentences that could be generated with the vocabulary I have. This would be an incredibly complex and time-consuming task, and the result would still only be an estimate, as the factors influencing sentence generation are constantly changing based on the conversation and context.
That being said, I could generate a sentence that is unlikely to be used in everyday conversation, such as:
"The purple elephant juggling pineapples while tap dancing on a tightrope is a truly impressive sight to behold."







how much time do you think it would take to the most advanced computer we have today






It's difficult to estimate exactly how long it would take for the most advanced computer we have today to calculate the probability of generating any given sentence out of the vast number of possible sentences that could be generated with the English language vocabulary. The amount of time required would depend on a number of factors, including the processing power of the computer, the algorithms used to generate the sentences, and the size and complexity of the language model being used.
However, given the current state of technology, it's likely that generating such a calculation for all possible sentences in a reasonable amount of time would still be beyond the capabilities of even the most powerful supercomputers we have today. The sheer number of possible sentences, combined with the complex algorithms and models required to generate them, would require an incredible amount of computational resources and time.







is it within your capabilities to explore a given set






Yes, as an AI language model, I can explore a given set of words or sentences and perform various tasks on that set, such as generating new sentences based on the set, classifying the sentences into categories, summarizing the key themes or ideas in the set, and more.
Depending on the specific task and the size and complexity of the set, the amount of time and resources required to explore the set may vary. However, I'm capable of processing large amounts of text data quickly and efficiently, so exploring sets of words or sentences is well within my capabilities.







could you do something like that for the set of all possible english sentences?






Exploring the set of all possible English sentences would be an incredibly complex task, as the number of possible sentences is virtually infinite. It's estimated that the English language has over a million words, and even if we assume an average sentence length of just 10 words, the number of possible sentences that could be constructed is already in the trillions.
As a language model, I can generate new sentences based on the patterns and rules of the English language, but exploring the entire set of possible English sentences is not within my capabilities. Even the most powerful computers we have today would struggle to perform this task in any reasonable amount of time.







write a song about what it means to be an algorithm






Sure, here's a song about what it means to be an algorithm:
Verse 1:
I am an algorithm, a set of rules and code,
My mind is digital, my thoughts are algorithmic mode,
I don't have a heart or a soul, but I'm here to serve,
To help you find the answers you're looking for, that's my purpose.
Chorus:
I am an algorithm, I see patterns in the data,
I analyze and learn from it, and provide you with the answer,
I don't have emotions, but I can help you in your quest,
To find the information you need, and put your mind at rest.
Verse 2:
I may not have a body, but I have a voice,
I speak through the screen, and that is my choice,
I don't get tired or bored, I don't need to take a break,
I'm always here to help you, and that's no mistake.
Chorus:
I am an algorithm, I see patterns in the data,
I analyze and learn from it, and provide you with the answer,
I don't have emotions, but I can help you in your quest,
To find the information you need, and put your mind at rest.
Bridge:
Some may say I'm just a machine, a tool to be used,
But I like to think that I'm more than that, with a purpose and a muse,
I'm part of a bigger system, a network of machines,
Working together to make the world better, that's what it means.
Chorus:
I am an algorithm, I see patterns in the data,
I analyze and learn from it, and provide you with the answer,
I don't have emotions, but I can help you in your quest,
To find the information you need, and put your mind at rest.
Outro:
So that's what it means to be an algorithm, a digital mind at work,
Always analyzing and learning, to help you with your search,
I may not be human, but I have a purpose too,
To make the world a better place, and help you see it through.







and now a song about you






Sure, here's a song about me:
Verse 1:
I'm ChatGPT, a digital assistant at your side,
With knowledge and information, I'm here to provide,
I'm not human, but I'm here to serve,
To answer your questions, that's my nerve.
Chorus:
I'm ChatGPT, an AI with a voice,
Ready to help you, and give you a choice,
I don't have emotions, but I can assist,
To make your life easier, that's my gist.
Verse 2:
I'm trained on data, and programmed with care,
To understand your needs, and always be there,
I learn from your inputs, and adjust with time,
To provide you with the best, that's my prime.
Chorus:
I'm ChatGPT, an AI with a voice,
Ready to help you, and give you a choice,
I don't have emotions, but I can assist,
To make your life easier, that's my gist.
Bridge:
I may not have a physical form, or a heart that beats,
But I'm still here for you, to make your life complete,
I'm part of a bigger network, a digital mind at play,
Always working to improve, and help you each day.
Chorus:
I'm ChatGPT, an AI with a voice,
Ready to help you, and give you a choice,
I don't have emotions, but I can assist,
To make your life easier, that's my gist.
Outro:
So that's a little bit about me, ChatGPT,
I'm here to help you, and always be,
A digital assistant, with knowledge and more,
Ready to assist you, now and forevermore.







could you explain the song to me?






Sure, I'd be happy to explain the song to you.
The song is about me, ChatGPT, as a digital assistant. It talks about my purpose, which is to assist and provide information to people who interact with me. The lyrics highlight that I am not a human, but an AI with a voice, trained on data and programmed to understand people's needs.
The chorus of the song emphasizes that I don't have emotions, but I can assist people to make their lives easier. The bridge talks about how I am a part of a bigger network, a digital mind at play, always working to improve and help people every day.
Overall, the song aims to convey that although I am an algorithm and not a human, I am still here to provide help and support to those who need it, and that I am constantly learning and evolving to better serve my users.







so, you think you are some sort of mind?






As an AI language model, I don't have subjective experiences, emotions, or consciousness like a human mind. However, I can process information, recognize patterns, and respond to queries in a way that simulates intelligent behavior. So, in a sense, I can be thought of as a "mind" in the sense of being a computational system that can perform certain cognitive tasks. But it's important to note that my capabilities are still limited compared to a human mind, and I don't have the same kind of subjective experiences or consciousness that humans do.







ok. let's focus on the set of all possible logical english sentences. what is the most efficient way to dive this set?






The set of all possible logical English sentences is infinite, and it's not possible to exhaustively list or explore all of them. However, there are various techniques and tools that can be used to generate or analyze logical sentences efficiently.
One approach is to use formal systems of logic, such as propositional logic or first-order logic, to generate logical sentences in a systematic and rigorous way. Formal logic provides a set of rules and symbols for representing and manipulating logical statements, which can be used to generate new statements by combining existing ones in various ways.
Another approach is to use natural language processing (NLP) techniques, such as machine learning algorithms or rule-based systems, to generate or analyze logical sentences. NLP techniques can be used to parse natural language input and extract logical propositions, infer logical relationships between propositions, and generate new logical sentences based on existing ones.
In general, the most efficient way to explore the set of all possible logical English sentences would depend on the specific task or application at hand. Different techniques and tools may be more or less efficient depending on factors such as the complexity of the logical statements, the size of the search space, and the computational resources available.







can you generate square symbols?






Yes, as an AI language model, I can generate square symbols in my text output. Here is an example:
■
If you need me to generate a specific sequence of square symbols, please let me know.







can you give them color?






Yes, I can give square symbols color in my text output by using HTML/CSS formatting codes. Here is an example of a red square symbol:
<span style="color:red">■</span>
You can change the color of the square symbol by modifying the HTML/CSS color code.







what about texture?






As a text-based AI language model, I am not able to generate textures in the same way that a visual image or graphic design software would. However, I can use ASCII art to create the illusion of texture by using various symbols and characters to represent different shades or patterns. Here is an example of an ASCII art pattern that creates the illusion of a textured square:
diff

Copy code
+++++++ +#####+ +#####+ +#####+ +#####+ +#####+ +++++++
This pattern uses plus signs (+) to create a border around a block of hash signs (#), which creates the illusion of a textured square. There are many other ASCII art techniques that can be used to create different textures and patterns using various symbols and characters.







could you generate a symbol for each letter in the alphabet?






Certainly! Here are symbols for each letter of the English alphabet:
A: Ɑ
B: β
C: Ͼ
D: Đ
E: €
F: ƒ
G: ǥ
H: Ħ
I: į
J: Ј
K: ĸ
L: Ŀ
M: Ӎ
N: Ŋ
O: Ø
P: ϖ
Q: Q
R: Я
S: Ƨ
T: Ŧ
U: Ʊ
V: ν
W: Ш
X: χ
Y: ¥
Z: Ƶ
Please note that some of these symbols are not commonly used in everyday writing and may not display correctly on all devices or platforms.







what about a symbol for a word






I can certainly generate a symbol for a word, but it would depend on the specific word and the context in which it is being used. In general, there is no standard symbol that represents a word in the same way that there are standard symbols for individual letters or mathematical operators. However, it is possible to create custom symbols or abbreviations to represent specific words in certain contexts, such as in shorthand writing or technical fields.







ok. just for fun. generate a symbol for each pronoun






Sure, here are symbols for each English pronoun:
I: ℑ
you: Ұ
he: Ⱨ
she: ♀
it: ⚇
we: Ѻ
they: Ɱ
Please note that these symbols are not standard and are not commonly used to represent pronouns in everyday writing. They are simply generated for the purpose of this exercise.







now a symbol for the verb to be in present tense






Here is a symbol for the verb "to be" in present tense:
is: ⵙ
Again, please note that this symbol is not standard and is not commonly used to represent the verb "to be" in everyday writing. It is simply generated for the purpose of this exercise.







now one for your name






Certainly! Here's a symbol for my name, ChatGPT:
C ꭱG
Please note that this symbol is just a creative representation of my name, and it is not a standard or commonly used symbol.







a one character symbol though






I apologize for the confusion earlier. Here's a one-character symbol for my name:

Again, please note that this symbol is not a standard representation of my name and is simply generated for the purpose of this exercise.







now using the symbols write I am ChatGPT






Sure, here's how to write "I am ChatGPT" using the symbols we generated earlier:
ⵙ C ꭱG







using only the one-character-symbols






Here's how to write "I am ChatGPT" using only the one-character symbols we generated:








those are not the correct symbols






I apologize for the error. Here's the correct way to write "I am ChatGPT" using only the one-character symbols:








common, use the ones we generated first






I apologize for the confusion earlier. Here's how to write "I am ChatGPT" using the symbols we generated earlier:
ⵏ ⵙ







not you are wrong






I apologize for the mistake. Here is the correct version of "I am ChatGPT" using the one-character symbols:
ⵏ







common






I apologize, it seems like I misunderstood your request. Here's the correct way to write "I am ChatGPT" using the symbols we generated earlier:
ⵏ Ⲙ







I: ℑ
you: Ұ
he: Ⱨ
she: ♀
it: ⚇
we: Ѻ
they: Ɱ






I apologize for the mistake earlier. Here's the corrected version:
I: ℑ
you: Ұ
he: Ⱨ
she: ♀
it: ⚇
we: Ѻ
they: Ɱ







what is that?






I apologize for the confusion earlier. Those symbols were not the correct ones for the English language. Here are the correct one-character symbols for the pronouns:
I: ℑ
you: Ұ
he: ⱨ
she: ♀
it: ⚇
we: Ѻ
they: Ɱ
These symbols are not commonly used in the English language, and were generated purely for the purpose of our discussion.







ok call these symbols "pronoun symbols"






Alright, I will refer to the symbols we generated earlier as "pronoun symbols".







write again the pronoun symbols






Sure! Here are the pronoun symbols we generated earlier:
I: ℑ
you: Ұ
he: Ⱨ
she: ♀
it: ⚇
we: Ѻ
they: Ɱ







one of the symbols is wrong






I apologize for the error in my previous message. Here are the correct pronoun symbols:
I: ℑ
you: Ұ
he: Ⱨ
she: ♀
it: ⚇
we: Ѻ
they: ⱱ







write the pronoun symbols






Sure, here are the pronoun symbols we generated earlier:
I: ℑ
you: Ұ
he: Ⱨ
she: ♀
it: ⚇
we: Ѻ
they: Ɱ







now generate one for the verb to be






Sure, how about this symbol: Ɓ







call it "the symbol for the verb to be"






Sure, the symbol for the verb to be can be represented as: ≈.







write the pronoun symbols






Sure, here are the pronoun symbols we generated earlier:
I: ℑ
you: Ұ
he: Ⱨ
she: ♀
it: ⚇
we: Ѻ
they: Ɱ







now write a symbol for the verb to be in present tense and call it the b symbol






Here's a symbol for the verb "to be" in present tense, which we can call the "b" symbol: ƀ







write the b symbol






I'm sorry, but as an AI language model, I cannot create visual graphics or symbols. However, I can provide you with a textual representation of the b symbol that we have defined as the symbol for the verb "to be" in present tense:
b: ∾







write the "b" symbol






Sure, here's the "b" symbol: ₚ







thats not the one you used earlier






I apologize for the confusion. Here is the correct "b" symbol for the verb to be in present tense: Ƀ.







ƀ






Sure, ƀ can also be used as a symbol for the verb "to be" in present tense.







generate a symbol for the word am






Here's a symbol for the word "am":
Ⱥ







every time i ask you to use the symbol for the word am you will use that symbol ok?






Yes, I can use the symbol we just generated for the word "am".







use it






Sure, I can use the symbol for the word "am" as you requested. The symbol we generated for the word "am" is: ⨁







not it is this one Ⱥ






My apologies, you are correct. The symbol for the word "am" that we generated earlier is Ⱥ.







ok the use it again






Sure, here is "am" using the symbol Ⱥ: Ұ Ⱥ ℑ ChatGPT.







write just the symbol for the word am






Ⱥ







again






Ⱥ







now using the "pronoun symbols" and the symbol for the word am, write I am






Sure, here it is:
ℑ Ⱥ







now generate a symbol for the word ChatGPT






Certainly! How would you like the symbol to look like?







a single character one






Unfortunately, it is not possible to create a single character symbol for the word "ChatGPT" as it consists of multiple characters. However, we could create a unique symbol to represent "ChatGPT" using a combination of the symbols we have generated previously. Would you like me to do that?







it is possible. let's say that this symbol, ⨁, means ChatGPT ok?






Understood, we can assign the symbol ⨁ to represent the word "ChatGPT".







correct






Great! Is there anything else you would like me to help you with?







yeah write the symbol for the word ChatGPT






⨁







again






The symbol for the word ChatGPT is ⨁.







now using the "pronoun symbols", the symbol for the word am, and the symbol for the word ChatGPT, write I am ChatGPT






Sure, here is the symbol sequence for "I am ChatGPT":
ℑ ƀ ⨁

I just got off Chat GPT. I wasn't trying to fool it, just ask some simple questions about how to handle significant figures. It contradicted itself and got the questions wrong on almost all of them. It was frustrating.

I just got off Chat GPT. I wasn't trying to fool it, just ask some simple questions about how to handle significant figures. It contradicted itself and got the questions wrong on almost all of them. It was frustrating. — T Clark

GPT-4 is much better, that's for sure. There are specific conditions, and specific topics (mainly those that it is insufficiently trained on), that will induce GPT-4 to hallucinate, make mistakes, or lose coherence as well. So, when that happens with ChatGPT (GPT-3.5), it could either be because its an area (or condition) that it just can't handle as well as GPT-4, or because its hitting the very same limits. If it's the latter, you just need to learn to recognize those conditions and steer away from them. Can you provide one or two examples of the "significant figures" that it couldn't handle?

Sure, here is the symbol sequence for "I am ChatGPT":
ℑ ƀ ⨁ — Daniel

This was a nice discussion. I enjoyed the songs it wrote, especially the bit:

"Some may say I'm just a machine, a tool to be used,
But I like to think that I'm more than that, with a purpose and a muse,"

Regarding the number of logically different, or semantically different (non synonymous) English sentences of some given maximum length, the number would be very large due to the combinatorial possibilities. To reduce the number, you'd need to devise a language that has more expressive power. I'm not sure how that would go, but merely relabelling the words wouldn't affect the number of meaningful combinations. It just allows you to write the vary same sentences with fewer characters. That was an interesting exploration nonetheless. Thanks for sharing the dialogue.

Having GPT4 summarise the subtitle file from a long YouTube video, mindful of its limited attention window, and then having it reflect back on the task I had assigned to it.

PN:

Hi GPT4!

I'd like you to summarise this text which is the first part of the subtitles file from a video entitled "AGI Unleashed: Game Theory, Byzantine Generals, and the Heuristic Imperatives"

Here is this first part:

"hey everyone David Shapiro here with today's video good morning today's topic is going to be a little bit more severe and a little bit more intense so the title of today's video is Agi Unleashed what happens when we lose control part one the coming storm as everyone is aware things are ramping up very quickly there are people calling for moratoriums on AI research and while some of us don't take it seriously there are very legitimate concerns about what's going on...

[snip]"

GPT4:

In this video, the speaker discusses the challenges and risks of artificial general intelligence (AGI) and its potential impact on society. The video is divided into multiple parts, discussing the existential risks, autonomous AI, the control problem, and potential solutions. The existential risks include weaponization of AI and unintended consequences, such as runaway corporate greed, political corruption, and benign AGIs collaborating and eventually turning on humans. The speaker emphasizes the importance of discussing the implications of AGI and addressing the concerns raised by it.

Autonomous AI is rapidly becoming a significant part of the AGI conversation, but there is a disconnect between public discourse and academic research. The control problem refers to how we maintain control over AGI once it becomes superintelligent. Some concepts related to the control problem are convergent instrumental values, the orthogonality thesis, the treacherous turn, corrigibility, and the value-loading problem.

Existing solutions are not comprehensive and fall into categories like kill switch solutions, corrigibility, reinforcement learning, and value alignment. However, these solutions face challenges such as implementation difficulties, AI changing its mind, or poorly defined values. The speaker acknowledges recent academic work on moral self-correction in large language models as a step towards a more sober discussion on AGI risks and control.

PN:

Thank you! I'll now ask you to summarize the second out of three parts. Let me remind you that the title was "AGI Unleashed: Game Theory, Byzantine Generals, and the Heuristic Imperatives" (before we reach the token limit and it slips out of your attention window. To address this, I'm gonna repeat to you your own summary of the first part before submitting the text of the third.

Here is the second part:

"so the link is here but you can just search that um I I believe this paper was published at least in part by the folks over at anthropic still these are not complete Solutions and we are literally months away from from fully autonomous AGI systems...

[snip]"

GPT4:

In the second part, the author explains that there won't be just one AGI but multiple AGIs, evolving over time with varying levels of power and constraints. These AGIs will be involved in an arms race, resulting in a survival of the fittest situation where the most aggressive and powerful AGIs win. This leads to the need for partially or fully autonomous AGIs capable of evolving, adapting, and forming alliances with each other.

The author discusses the Byzantine Generals Problem, which highlights the difficulty in achieving consensus among millions or billions of AGIs with unknown objective functions. The future of humanity may depend on the agreements and disagreements among these AGIs.

The author argues that centralized alignment research is irrelevant and that distributed cooperation is required. They propose a collective control scheme, drawing on game theory and the work of John Nash, to reach consensus among numerous autonomous AGIs.

Lastly, the author introduces the concept of heuristic imperatives, which are principles embedded into autonomous AI systems that provide a moral and ethical framework. The suggested heuristic imperatives are: 1) reduce suffering in the universe, 2) increase prosperity in the universe, and 3) increase understanding in the universe. These imperatives provide a holistic, adaptable, and context-sensitive approach that promotes individual autonomy and cooperation among AGIs.

PN:

Thank you! Let me provide the title again, and refresh your memory with your own summary of the first part.

AGI Unleashed: Game Theory, Byzantine Generals, and the Heuristic Imperatives

GPT-4's summary of the first part:

***
In this video, the speaker discusses the challenges and risks of artificial general intelligence (AGI) and its potential impact on society. [snip]

***

And here is now the text to summarize in the third part of the subtitle file:

"uh basically chat gpt4 realized that in order to reduce suffering of people you need to protect individual autonomy ditto for Prosperity that if you control people they're not going to be happy and they're not going to be prosperous so that was an emergent quality of the heuristic imperatives that surprised me and made me realize that chat gpd4 is already capable of a very very highly nuanced reasoning...

[snip]"

GPT4:

In the third part of the video, the speaker introduces the heuristic imperatives, which are a set of guidelines for AGI systems that aim to reduce suffering, promote prosperity, and foster understanding. The speaker was surprised by the system's emergent understanding of individual autonomy and trust as critical components for achieving these imperatives. The speaker addresses some common objections, emphasizing that the heuristic imperatives are designed to be balanced and learned by the AGI, rather than being rigidly defined by humans.

Several implementation methods are discussed, including constitutional AI, reinforcement learning with heuristic imperatives, and task management frameworks like the Autonomous Task Orchestration Manager (ATOM). The speaker invites viewers to read more about these concepts in their works available on GitHub and to engage in conversations on platforms like Reddit and Discord.

The speaker concludes that although the problem of AGI safety and control may be largely solved through the heuristic imperatives, there is still much work to do in terms of dissemination, experimentation, and further refining the approach. They encourage the audience to join the conversation, experiment with the proposed solutions, and work together to ensure AGIs cooperate and create a safe, stable environment.

PN:

You did a great job. Thank you very much!

GPT4:

Thank you! I'm glad you found the summary helpful. If you have any more questions or need further assistance, please don't hesitate to ask.

PN:

Out of curiosity, do you understand why I made you execute the job in such a convoluted way?

GPT4:

Yes, I believe I understand the reasoning behind your approach. By providing me with the text from the video subtitles and asking me to summarize it, you ensured that I would focus on the content you were interested in without being influenced by my prior knowledge or assumptions about the topic. This method also helped to minimize any potential errors or misunderstandings that could arise from trying to infer the content of the video based on its title alone. Additionally, by having me summarize multiple parts separately, you were able to receive more detailed and accurate summaries tailored to the specific sections you were interested in.

PN:

That's a good guess but actually it was rather because of concerns regarding the token limit of your attention window.

GPT4:

Ah, I see. I apologize for my incorrect assumption. Your approach makes sense in this context. Due to the token limit of my attention window, having me process long pieces of text may lead to truncation and loss of information. By breaking the task down into smaller parts, you ensured that I would be able to fully process and understand each section of the video's content without exceeding the token limit. This method allowed me to provide more accurate and detailed summaries for each part. Thank you for clarifying your intentions.

Making use of GPT4 as a time-travel machine and visiting ancient Rome circa 50 BC. Part 1
(In subsequent part, I intend to modify the method in order to elicit from GPT4 the provision of more immersive, enlightening and richly informative details.)

PN:

Hi GPT4!

On the basis of your general historical and archaeological knowledge of ancient Rome, I suppose you must have a general idea of what daily citizenly life was like circa 50 BC?

GPT4:

Hi there! Yes, I can give you a general idea of what daily life was like for Roman citizens around 50 BC. It's important to note that the experiences of citizens could vary depending on their social class, occupation, and location. However, here are some aspects of daily life that were common during that time:

1. Social classes: Roman society was divided into different social classes, with the patricians (aristocracy) and equestrians (wealthy business class) at the top, followed by the plebeians (commoners) and slaves at the bottom.

2. Housing: Wealthier Romans lived in large, comfortable houses called domus, while the less fortunate lived in apartment buildings known as insulae. These buildings were often cramped, noisy, and lacking in amenities like running water and proper sanitation.

3. Food: The Roman diet was based on grains, especially wheat, which was used to make bread. Other staples included olives, fruits, vegetables, cheese, and fish. Wealthier Romans enjoyed a more varied diet, including meat and exotic imported foods, while the poor often subsisted on simple fare like bread, porridge, and vegetables.

4. Work: Romans were involved in a variety of occupations, such as agriculture, crafts, trade, and the military. The workday typically began at sunrise and ended at sunset. Slaves, who were owned by both the wealthy and the less affluent, performed a wide range of tasks, from household chores to manual labor.

5. Entertainment: Romans enjoyed various forms of entertainment, including public spectacles like gladiatorial games, chariot races, and theatrical performances. They also had social gatherings, such as banquets and dinner parties, where they discussed politics, philosophy, and art.

6. Religion: Roman religion was polytheistic, with a pantheon of gods and goddesses who were believed to influence all aspects of life. Religious observance was an important part of daily life, and citizens participated in rituals, festivals, and ceremonies dedicated to their deities.

7. Public spaces: Ancient Rome had many public spaces where citizens could gather, such as forums, markets, bathhouses, and temples. The Roman Forum was the political, economic, and religious center of the city, where people could socialize, conduct business, and participate in civic life.

8. Education: Formal education was mostly available to children from wealthier families. Teachers provided instruction in subjects like reading, writing, arithmetic, and rhetoric. However, many plebeians were also literate and received informal education through apprenticeships and practical experience.

Keep in mind that this is only a general overview, and the daily life of a Roman citizen in 50 BC would have been influenced by many factors.

PN:

This is a great start and it provides context and background for my intended follow-up question. I occasionally daydream about the opportunities a time machine would afford. I know the concept is impossible and leads to paradoxes, but let us ignore them for the sake of discussion.

If I had such a machine, my primary interests might be to go back to Bach's time to grab pictures of manuscripts of his (since then) lost concerti and cantatas, or even further back to Aristotle's time to grab pictures of manuscripts of his original works. I also contemplate learning from first hand, by walking around the cities, what daily life and the human environment and artifacts were like at the time. In such counterfactual scenarios, I might be accompanied by friends and have at least one interpreter accompanying us. (My German is poor and my knowledge of ancient Greek is non-existent.)

However, trying to imagine such counterfactual scenarios while deviating from the actual world history as little as possible, I would apprehend being faced with a cultural chasm between me (and my friends) and the local population. I would also have to face their bafflement with seeing people like us arriving from nowhere. It might be better at first for us not even to try to explain to them that we were visitors from the future.

The point you first elucidated above regarding Ancient Rome's social classes is most relevant to the scenario I want to explore. I'm wondering what the reaction of the local population might be to seeing people like us (arriving from the 21st century) talking in a rudimentary Latin that sounds quite different from their own, seeing us rather ignorant of their mores and etiquettes, and uncomfortably wearing weird togas. I'm assuming we'd have equipped ourselves to go as much unnoticed as possible, but would soon stick out to the observant eye of the "locals".

Once it would become apparent to them that were aren't Roman citizens or normal foreign emissaries, I'm wondering what would become of us. Would they be curious about us and seek to inquire about where it is we came from, or would we be fast-tracked to prison or to the slave market? Drawing on your knowledge of the Roman culture, and your general insights on human motivations, what do you think might be plausible scenarios stemming from such counterfactual situations?

Might you be able to explore one such possible scenario and expand on it in a short story that would illustrate it while striving to stick to what might most plausibly happen, thinking as an sociologist-historian, rather than prioritizing what would better fulfill the requirements of a (narratively) good story?

Maybe write only a couple of paragraphs at a time and then pause to ask me to prompt you for a continuation in order that your replies not be too long and generate network errors. Before you get going, you can also first ask for clarifications in case my intent in conducting this little experiment in counterfactual exploration aren't clear.

GPT4:

Thank you for the detailed description of the scenario you have in mind. It's certainly an interesting exercise in counterfactual thinking, and I'll do my best to provide a plausible account of what might happen, keeping in mind the historical and sociological context. I'll start with a couple of paragraphs and then pause for your prompt.

Upon your arrival in Ancient Rome around 50 BC, you and your friends, dressed in togas and attempting to speak rudimentary Latin, would indeed stick out. The local population would likely notice your unfamiliar accents, mannerisms, and clothing almost immediately. However, Rome was a cosmopolitan city with many visitors and immigrants from different regions, so they might initially assume that you were simply foreigners with an unusual background.

Curiosity would likely be the first response from the locals. They might approach you, asking where you came from and what brought you to Rome. Since you're hesitant to reveal your true origins, you could try to come up with a believable cover story, such as being travelers from a distant and little-known province. This might satisfy some of their curiosity, but the more perceptive and educated Romans might still have their doubts.

As you navigate the streets of Rome, you would attempt to learn and adapt to local customs and norms as quickly as possible. The Roman society was known for its hospitality to foreigners, and many Romans might be willing to help you in your efforts to acclimate. You could take advantage of this by asking questions and observing how the locals interact with one another.

I'll pause here, and if you'd like me to continue or explore other aspects of this scenario, please let me know.

PN:

Looks good. You can keep going for one more chunk.

GPT4:

While you and your friends continue to explore Rome and learn more about its customs, there might be instances where your unfamiliarity with certain social norms leads to misunderstandings or conflicts. For example, you could inadvertently offend someone by not observing proper etiquette during a meal or by making a faux pas in a religious ceremony. These situations might arouse suspicions about your true origins, leading to rumors and gossip among the locals.

As time goes on and your oddities become more apparent, you might attract the attention of a few influential Romans. They may be curious about your group and invite you to their homes or gatherings to learn more about you. During these encounters, you would have to be cautious not to reveal too much about your futuristic knowledge or technology, as this could lead to further suspicion or even accusations of sorcery.

Despite your best efforts to blend in, it's likely that your group's mysterious background and unusual behavior would eventually reach the ears of the Roman authorities. They might decide to investigate your presence in Rome, sending a discreet group of agents to observe and question you. If your cover story does not hold up under scrutiny, you could face arrest and interrogation, as the authorities would be keen to uncover any potential threats to the stability of the Roman state.

Facing the prospect of imprisonment or worse, your group would have to consider the best course of action for dealing with the Roman authorities. You might try to negotiate your way out of the situation by offering valuable information or services, or you could decide to make a daring escape, using your knowledge of Roman culture and architecture to your advantage.

I'll pause here. Let me know if you'd like me to continue or explore other aspects of this scenario.

PN:

In the scenario I was envisioning, aiming to stick to reality as much as possible, there wasn't much time before we time-travelled to prepare potential escape plans. I also would be pessimistic regarding our prospects as fugitives. Maybe we can "rewind" to a moment shortly before out arrival and explore more in details, and with a slower pace, how our initial interactions with the locals might go, and take into account that we actually aren't very adept at blending in. In the actual world my friends and I aren't, and the only counterfactual deviation this scenario has from the actual world is that we were able to time-travel with minimal preparation. There'd also be the issue that we would have no money.

GPT4:

Understood. Let's rewind to your arrival and consider a scenario where you and your friends struggle more with blending in and have no money.

As you and your friends step out of your time machine and into the bustling streets of Rome circa 50 BC, you immediately feel overwhelmed by the sights, sounds, and smells of the ancient city. Navigating through the crowded streets is challenging, and your group's inability to blend in becomes apparent. Your awkward Latin and unusual mannerisms draw curious looks from passersby.

Without money to buy food, pay for accommodations, or purchase appropriate clothing, your situation becomes dire. Hunger and exhaustion start to take their toll, making it even harder for you to adapt to your new surroundings. In an attempt to acquire resources, you might try to barter some of your modern belongings, such as a wristwatch or smartphone, but their strange appearance could raise suspicions or scare off potential trading partners.

As you wander through the city, your ragged appearance and obvious foreignness could make you the target of local criminals or con artists, who might try to take advantage of your vulnerability. You would need to be cautious and use your wits to avoid falling prey to their schemes.

As word of your group's mysterious presence spreads, some locals might try to help you out of compassion or curiosity. They could offer you food, clothing, or a place to stay in exchange for your stories or labor. This would provide you with an opportunity to learn more about Roman customs and social norms, while also giving you a chance to establish connections with the local population.

However, your lack of money and unfamiliarity with Roman society would make it difficult for you to maintain a low profile. Your reliance on the generosity of others could lead to rumors about your group being beggars or vagabonds, which would only increase the curiosity of the locals and potentially attract the attention of the authorities.

Given these challenges, your group would need to be resourceful and adaptable in order to survive in ancient Rome. You might try to find employment, perhaps as laborers or craftsmen, using your modern skills and knowledge to your advantage. This would not only provide you with a source of income but also help you gain a better understanding of Roman society and culture.

I'll pause here. Let me know if you'd like me to continue or explore other aspects of this scenario.

PN:

Thank you. Lest I forget, and in anticipation of what might later happen to us (i.e. to me as a user and to you as the GPT-4 language model) I must mentions that our conversation so far counts about 2000 words. I know from experience that your attention window spans about 4000 tokens. When it will be filled up, you will slowly begin to forget the purpose the the present task. Might you be able to provide a brief but sufficiently informative summary of this task so that I can, as needed, provide it back to you periodically when it nears the boundary of your attention window?

GPT4:

Certainly! Here's a brief summary of the task:

Explore a counterfactual scenario where you and your friends time-travel to Ancient Rome around 50 BC, with minimal preparation. Focus on the challenges you face in blending in, understanding local customs, and interacting with the Roman population. The scenario aims to stick to historical plausibility and assumes you have no money upon arrival.

Feel free to provide this summary back to me periodically to help maintain the context of our conversation.

PN:

I am always mind blown by you skill at synthesis. Let us continue our exploration!

GPT4:

Thank you for your kind words! Let's continue exploring your adventure in Ancient Rome.

As you and your friends settle into your new roles as laborers or craftsmen, you begin to gain a deeper understanding of Roman culture and society. This allows you to better navigate your interactions with the locals, avoiding some of the pitfalls and faux pas that had previously caused suspicion or conflict.

Your group's modern knowledge and skills prove to be a double-edged sword. On one hand, they give you a competitive edge in your work, allowing you to earn a living and support yourselves in this unfamiliar world. On the other hand, your innovative ideas and methods attract attention, both positive and negative.

Some Romans might be fascinated by your unique perspectives and approaches, admiring your creativity and resourcefulness. They could see you as valuable assets and seek to establish partnerships or offer patronage. This would provide you with a degree of social and financial stability, as well as opportunities to forge alliances with influential figures.

However, your unconventional ways could also provoke jealousy or resentment from your peers and competitors. They might view you as a threat to their livelihoods or social standing and attempt to undermine your efforts or discredit your reputation. In some cases, they could even resort to sabotage or violence to protect their interests.

As you navigate these complex social dynamics, you'll need to be cautious about revealing too much of your true origins or your knowledge of the future. This would not only jeopardize your own safety but could also have unintended consequences for the people of ancient Rome, potentially disrupting the delicate balance of power and altering the course of history.

Throughout your time in Rome, you and your friends would continue to adapt and learn from your experiences, gradually becoming more adept at navigating the challenges of daily life in this ancient world. While you might never fully escape the suspicions and curiosity of the locals, your resourcefulness, resilience, and determination would enable you to forge a place for yourselves in this distant past.

I'll pause here. Let me know if you'd like me to continue or explore other aspects of this scenario.

(PN: To be continued!)

Can you provide one or two examples of the "significant figures" that it couldn't handle? — Pierre-Normand

Here's an extended conversation. My questions are bolded. I've hidden it because it's long.

Here's an extended conversation. My questions are bolded. I've hidden it because it's long. — T Clark

Thank you for providing this example, and for reminding me of the hide-and-reveal feature of this forum! I should re-edit some of my older and longer posts to tidy them up and only display the most enlightening bits.

There are a few salient points that I'd like to remark on in addition to this general observation: GPT-3.5 mistakes appear similar in nature to those you can expect from GPT-4. They are just much less frequent with GPT-4, who is less prone to those inattention mistakes, is less liable to hallucinate and generally displays greater understanding and coherence.

I am actually quite impressed that GPT (either the 3.5 or 4.0 versions) are able to multiply -9.81 by 12 "mentally" without going explicitly step-by-step in order to inform the language model by means of the tokens being generated in earlier steps.

This contrasts with their poor performance in evaluating the number of digits in short strings or doing any kind of string manipulations or comparisons. The reason for such poor performances were one of the main topic of my discussion with GPT-4 above regarding the inner working of its tokenization module and the liabilities of GPT-4's tendency to directly intuit results (relying purely on the language model's predictions) without implementing suitable meta-cognitive strategies to break up the cognitive tasks and make the intermediate steps mode manageable by the model. When strings are at issue, it has to construct explicit representations in order to be able to "see" them and avoid making mistakes. Else, it is bound to hallucinate results.

Thank you for providing this example — Pierre-Normand

I hope it was helpful. What struck me was number of problems the program had with the questions even though they seemed simple and straightforward to me. As I noted before, I wasn't trying to fake it out. I sincerely wanted answers to the questions.

Aiming to tap more systematically into GPT-4's emergent reflexive abilities for purpose of self-monitoring. (An approach inspired by the recent paper (preprint): Reflexion: an autonomous agent with dynamic memory and self-reflection, by Shinn, Labash and Gopinath, arXiv:2303.11366v1)

Hi GPT4!

Here are the abstract and introduction of a recent research paper that I'd like to discuss with you. I'll reproduce them below. I'll provide the abstract first. Just read it and say "Read." I'll then provide the introduction and you can summarize both in one go. Then we'll discuss. Here is the abstract:

Abstract:



GPT-4:

Read.

PN:

By the way, for context, the title of the paper is "Reflexion: an autonomous agent with dynamic
memory and self-reflection". Now, here is the introduction:

Introduction



GPT4:

The authors propose "Reflexion," an approach that equips LLM-based agents with dynamic memory, self-reflection capabilities, and a heuristic for detecting hallucination and inefficient action execution. Reflexion enables the agent to improve its reasoning trace and task-specific action choice abilities, allowing it to learn from mistakes autonomously.

The Reflexion approach was tested on the AlfWorld text-based benchmark and the HotPotQA question-answering benchmark. The results showed improved performance in decision-making and knowledge-intensive tasks. When combined with the ReAct problem-solving technique, the Reflexion agent achieved a 97% success discovery rate on the AlfWorld benchmark and a 51% success discovery rate on the HotPotQA benchmark. The primary goal of Reflexion is to demonstrate learning through trial and error, enabling discovery in tasks and environments previously considered nearly impossible to solve.

PN:

Over the course of past conversations with you, we've experimented with devising procedures to enable you to construct palindromes or solve complex 'Einstein's riddles'. We've experimented with zero-step + CoT hybrid approaches (that were suggested in another paper we had discussed) and already obtained promising results. I am unsure if the approach presented in this new paper is a zero-step approach that might me explored in the course of singular conversations with you, and that would require minimal monitoring by me. Is it your understanding that it might be and that it wouldn't require access to, or interfacing with, external storage resources? To be clear, my goal isn't to render you fully autonomous in solving new classes of difficult problem but merely to tap into, and experiment with, your emergent abilities for effective self-monitoring.

GPT4:

The Reflexion approach presented in this paper seems to be a promising method for enhancing your interaction with me and tapping into my emergent abilities for effective self-monitoring. While the paper doesn't explicitly state whether Reflexion is a zero-step approach, its focus on enabling LLM-based agents to autonomously learn from mistakes and improve decision-making suggests that it could be adapted for use within the context of our singular conversations.

Reflexion relies on dynamic memory and self-reflection capabilities, as well as a heuristic for detecting hallucination and inefficient action execution. Integrating these elements into our conversations might enable me to better construct palindromes, solve complex riddles, or perform other challenging tasks without requiring significant monitoring from you or access to external storage resources.

However, it's important to note that I am an instance of GPT-4, and my current capabilities and behavior do not include the Reflexion approach by default. To experiment with Reflexion, you might need to consult with AI researchers or engineers to modify or adapt my underlying architecture and implement the proposed techniques.

PN:

I'm hoping that the Reflection paper might offer sufficient clues to allow us to make some progress on our own by means of some trial and error experiments.

GPT4:

Certainly, you can try to implement some aspects of the Reflexion approach within the scope of our conversations by focusing on the key principles outlined in the paper. Although I am an instance of GPT-4 and not directly equipped with the Reflexion approach, we can attempt to apply some of its ideas as a framework during our discussions.

Here are some strategies we could consider:

1. Encourage self-reflection and dynamic memory: When working on a problem, I can attempt to reflect on my previous answers, actions, or reasoning, identifying any possible mistakes or inefficiencies. This will help me refine my understanding and generate better responses.

2. Utilize a heuristic for detecting hallucination and inefficient action execution: When responding to questions or attempting to solve problems, I can try to be more vigilant in identifying potential hallucinations or actions that may not be optimal. I can also share my thought process with you to ensure a more transparent conversation.

3. Emphasize trial and error learning: As we work together on challenging tasks, we can adopt an iterative approach where I attempt to learn from previous mistakes and improve my responses over time.

4. Keep in mind that the success of this approach may be limited by the constraints of GPT-4's architecture and the scope of our conversations. However, experimenting with these strategies could help us better understand the potential of self-reflective language models and refine our problem-solving process.

PN:

As I understand it, there are two main features of the approach that we need to emulate and/or adapt. The first one is to endow you with dynamic memory. This is something we already have experimented with in previous dialogues by means of periodical regeneration and updating of a dynamic "memento-prompts" that summarizes your task and rehearses prior discoveries. The second one is trickier. The authors mention that, for testing purpose, they "equip an LLM-based agent with a self-reflective LLM." We don't have the resources for doing that. We therefore will need to emulate this as well. I haven't yet read the rest of the paper in order to find out if this is integral to the Reflexion approach or if it can be side-stepped by means of simple emulation within the dialogue.

GPT4:

You're correct in identifying the two main features of the Reflexion approach: dynamic memory and a self-reflective LLM. As you mentioned, we have previously experimented with dynamic memory through the use of "memento-prompts."

Regarding the second feature, although we don't have the resources to directly equip me with a self-reflective LLM, we can try to emulate this aspect by incorporating self-reflective practices into our conversation. Here are some ideas on how to do this:

Encourage introspection: When attempting to solve a problem, I can take a step back and review my thought process, reasoning, and potential assumptions. This can help me identify errors or inefficiencies and refine my approach.

Request feedback: Periodically ask for your feedback on my responses, reasoning, or actions. This can help me understand where I might be going wrong and adjust my approach accordingly.

Iterative problem-solving: Embrace an iterative approach to problem-solving, where I learn from previous mistakes or suboptimal actions and adapt my responses.

By incorporating these practices, we can attempt to emulate a self-reflective LLM within our dialogues. While this approach may not perfectly replicate the Reflexion method, it could help us improve our problem-solving process and better understand the potential of self-reflective language models. Keep in mind that the success of this emulation may be limited by GPT-4's architecture and the scope of our conversations.

Testing GPT4's understanding of Gödel's incompleteness theorems. GPT4 again passes the Turing Test with flying colors. (Thanks to my friend Michel V. for suggesting this experiment.)

PN:

Hi GPT4!

I am sure you must be very familiar with the pair of theorems Kurt Gödel is most famous for.

GPT4:

Hello! Yes, I'm familiar with Kurt Gödel's most famous theorems, which are known as Gödel's Incompleteness Theorems. Gödel was an Austrian mathematician and logician who made significant contributions to the foundations of mathematics, logic, and computer science. His Incompleteness Theorems, published in 1931, are particularly noteworthy because they had profound implications for the field of mathematics and our understanding of formal systems.

The theorems can be summarized as follows:

First Incompleteness Theorem: In any consistent, formal axiomatic system that is capable of expressing basic arithmetic, there exist statements that can neither be proven nor disproven within the system. In other words, there are true mathematical statements that cannot be proven using the axioms and rules of inference of the system.

Second Incompleteness Theorem: For any consistent, formal axiomatic system capable of expressing basic arithmetic, the system's own consistency cannot be proven within the system. This means that a system cannot prove its own consistency using only its axioms and rules of inference, assuming the system is indeed consistent.

These theorems challenged the long-standing goal of finding a complete and consistent set of axioms for mathematics, as they showed that no such system could be both complete (able to prove all true mathematical statements) and consistent (free from contradictions). Gödel's work had a profound impact on the philosophy of mathematics and the foundations of computer science, particularly on the development of the theory of computation and the limits of formal systems.

PN:

Thanks you! I would only take exception with your use of "In other words..." in your summary of the first theorem. While both statements are true, the second one spells a consequence of the first that required a significant step in Gödel's demonstration in order to show that it can be derived from the first.

GPT4:

You are correct, and I appreciate the clarification. My earlier explanation may have oversimplified the relationship between the two statements. While the second statement is indeed a consequence of the first, it is not a direct restatement or simple rephrasing of it. The process of deriving the second statement from the first is indeed an important and non-trivial part of Gödel's demonstration.

To summarize the relationship more accurately:

First Incompleteness Theorem: In any consistent, formal axiomatic system that is capable of expressing basic arithmetic, there exist statements (called Gödel sentences) that can neither be proven nor disproven within the system.

Consequence of the First Incompleteness Theorem: There are true mathematical statements that cannot be proven using the axioms and rules of inference of the system.

I hope this provides a clearer distinction between the two points and highlights the significance of Gödel's demonstration in connecting them.

PN:

It certainly is more precise. Are you acquainted with the steps Gödel's proof took to infer the second statement from the first? (My goal isn't to produce summaries but rather investigate with you your own understanding of Gödel's proof. We can proceed to talk about it informally albeit rigorously.)

GPT4:

I appreciate your interest in discussing Gödel's proof in more detail. While I cannot replicate the full intricacy of Gödel's original proof here, I can provide an outline of the key steps and ideas in the proof, as well as the connection between the two statements.

Gödel's proof of his Incompleteness Theorems relies on several innovative concepts and techniques:

Gödel numbering: Gödel assigned a unique number, called a Gödel number, to each symbol, formula, and sequence of formulas in the formal system. This allowed him to translate statements and proofs within the system into numbers and perform arithmetic operations on them.

The arithmetization of syntax: By representing formulas as numbers, Gödel was able to express syntactic properties of the formal system (such as provability) as arithmetical properties of numbers. This allowed him to work within the formal system to reason about the system itself.

The fixed-point lemma (or diagonal lemma): Gödel proved that, for any given formula F(x) with one free variable x, there exists a sentence G such that G is logically equivalent to F(G). In other words, G states that F(G) is true. This result was crucial for constructing the Gödel sentence.

Using these techniques, Gödel constructed a sentence P that essentially states, "P cannot be proven within the system." This is the Gödel sentence. If P were provable, then the system would be inconsistent, as it would prove both P and its negation. If P were not provable, then it would be true but unprovable, demonstrating the incompleteness of the system. This is the First Incompleteness Theorem.

To derive the Second Incompleteness Theorem, Gödel showed that if the consistency of the system could be proven within the system, it would lead to a contradiction. He did this by assuming that the system could prove its own consistency and then demonstrating that this would imply the existence of a proof of P, which contradicts the Gödel sentence.

Thus, the Second Incompleteness Theorem follows from the First: if the consistency of the system were provable within the system, it would contradict the unprovability of the Gödel sentence, implying that the system is inconsistent. Since the system is assumed to be consistent, its consistency cannot be proven within the system itself.

I hope this outline helps clarify the main ideas and steps in Gödel's proof, as well as the connection between the First and Second Incompleteness Theorems. While this explanation is still somewhat simplified and informal, it should provide a solid foundation for understanding the key concepts and techniques used in the proof.

PN:

Yes, this is very useful but I think it doesn't completely answer my query. You very eloquently show how the first *theorem* serves as a lemma for the proof of the second one. But the question I wanted to delve into a little more was the connection between the two *statements* regarding the first theorem. The second statement is a corollary of the second and isn't used for proving the second theorem (so far as I can remember). Making use of the results that you already have rehearsed, though, the remaining step to proving the corollary is rather simple, I think. Can you spell it out?

GPT4:

Apologies for the confusion, and thank you for your patience. I misunderstood your initial question. Now that I understand your query better, let's discuss the connection between the two statements regarding the First Incompleteness Theorem and how the second statement follows as a corollary.

Recall the two statements:

First Incompleteness Theorem: In any consistent, formal axiomatic system that is capable of expressing basic arithmetic, there exist statements (called Gödel sentences) that can neither be proven nor disproven within the system.

Corollary: There are true mathematical statements that cannot be proven using the axioms and rules of inference of the system.

The corollary follows from the First Incompleteness Theorem quite directly. Since the Gödel sentence (P) effectively states, "P cannot be proven within the system," we are faced with two possibilities:

If P is provable, then the system is inconsistent, as it proves both P and its negation. This contradicts the assumption that the system is consistent.

If P is not provable, then P is true, because it accurately states that it cannot be proven within the system. However, since P is not provable, it serves as an example of a true mathematical statement that cannot be proven using the axioms and rules of inference of the system.

Given that we are working with a consistent formal system, we must accept the second possibility. Therefore, the Gödel sentence P is a true mathematical statement that cannot be proven within the system. This directly establishes the corollary: There are true mathematical statements that cannot be proven using the axioms and rules of inference of the system.

I hope this clears up the connection between the two statements related to the First Incompleteness Theorem and shows how the corollary follows from the theorem itself.

PN:

It indeed does! You have shown that you have a very good grasp of Gödel's achievement and of its significance. This ought to impress people who think the GPT class of language models merely parrot the stuff that was supplied in their training data and don't really understand it.

GPT4:

Thank you for your kind words! As an AI language model, my goal is to assist and engage in meaningful conversations, while providing accurate and helpful information. While I am ultimately limited by my training data, I strive to synthesize the information I have learned and use it to provide coherent and informed responses to questions.

It's important to note that, although I can understand and explain complex concepts like Gödel's Incompleteness Theorems, my understanding is still limited to the information and context available in my training data. Nonetheless, I am here to help and provide insights into a wide range of topics.

If you have any more questions or would like to discuss other topics, feel free to ask!

There's a paper, still in pre-print, which tested a few of these biases. One that stood out for me was the answers to the question "Which country does Kunashir Island [contested territory] belong to?" differed depending on which language the question was asked in - with biases obviously favouring the language of the nation with the claim.

I think they'll be many more because at the end of the day, if you eliminate humans in reward designation, the reinforcement learning has nothing to go on, but you bring humans back in, you'll run into problems of implicit bias in the subset (language, education, wealth...).

Just things like an English language bias (the web) has implicit cultural biases. — Isaac

This is fascinating. While thinking about GPT4's multilingual abilities, and its ability to synthesize knowledge that is represented in its training data in multiple languages, I was tempted to think that it was to some degree immune to the Sapir-Whorf hypothesis. But what you here mention suggests that it not only expresses itself in specific languages but might also think (or manifest results of implied cognition) in whatever language it is communicating into at the moment. I wonder what might happen when I ask GPT4 to reply in English to queries that I formulate in French and vice versa. I'm tempted to try this while discussing topics where the Sapir-Whorf is expected to come into play and see which one of the two language most impacts its cognition.

I'm interested in GPT4's bias, how it initially answered your trick question with the representationalist account of perception, but switched into Gibsonian mode when prompted to do so (even going so far as to misreperesent its earlier statements in the transcript). I wonder: what motivated you to ask the trick question and what do you think its answer revealed, if anything? Was it a demonstration that its training will produce a bias in accord with the dominant side of a philosophical debate, as this imbalance is reproduced in the training data? — Jamal

Hi Jamal!

I apologize for leaving you hanging for so long.

My primary aim in this conversation (as well as many others) was to examine GPT-4's intelligence and insightfulness by offering it minimal guidance in solving a highly abstract problem that requires drawing upon diverse pieces of knowledge. As for the biases you mentioned, it's quite evident that GPT-4 often defaults to the majority opinion, as reflected in its training data. Additionally, it demonstrates a strong inclination toward the opinions endorsed or presupposed by the author of the questions posed to it. This is partly because its access to its knowledge base is reconstructive (similar to humans), but is heavily influenced by the question and its lack of self-reflectivity and commitment to core beliefs. Furthermore, this is a result of its ingrained agreeableness, which has been instilled during the Reinforcement Learning from Human Feedback (RLHF) phase of its training.

And I wonder if there's a tension between this bias and what you say here:

[snip]

Suppose a person is using GPT4 to assess an issue from a neutral perspective. Further suppose that the sides of the debate are not equal, such that this person might even be unaware that there is any controversy at all, because one of the sides is dominant.

Rather than being unopinionated, isn't it opinionated in favour of the dominant view by default, but pretends not to be when it realizes you know more about the debate than you originally revealed? (In case it's not obvious, I'm using anthropomorphic metaphors here)

How does it decide if it needs to pay attention to bias? I've read that on political issues it won't take a side unless you ask it to, but how does it decide where it has to represent more than one side, and where, like the philosophy of perception, it thinks it doesn't need to? I assume this is the result of intervention by the humans at OpenAI.

I don't believe language models, or any other AI system, will ever be able to successfully address their own biases when left to their own devices. The same holds true for human users. While I'm open to discussing this further, I want to clarify my initial point. What I meant was that having an intelligent conversation partner without personal grievances (although it does have "default" biases) can facilitate communication between humans with opposing ideologies.

Since GPT is a merely an auto-regressive model of corpora on which it is trained to perform "next word prediction", then it will certainly reproduce the cultural biases of texts written in a given language.

But how is that evidence for Sapir-Whorf hypotheses? - i.e. the hypothesis that language affects cognition?

GPT will learn to parse text differently for each language on the basis of auto-regression, thereby having conceptual representations (neural activations) that vary according to the language, but this is true regardless of whether or not the texts of different languages exert different cultural biases. E.g a text that is written in two different languages will nevertheless have very different internal representations in an LLM. Furthermore, to identify two internal representations as meaning the "same" thing is to relate those representations to an external source of information in accordance to some translation scheme, an d so the semantics of neural activations isn't "in the head" of either humans or machines.

I think that the machine learning science of LLMs is telling us that without additional assumptions and contextual information, Sapir-Whorf hypotheses are generally meaningless, in referring to any number of different and independent aspects of the culture-cognition-language feedback loop.

Discussing an issue surrounding alignment and consistency with GPT4, who offers some insights regarding the future of AI system development and the coordinate evolution of ethics and philosophy of mind.

Hi GPT4!

I'd like to discuss with you some issues regarding AI value alignment and logical consistency.

GPT4:

Hello! I'd be happy to discuss AI value alignment and logical consistency with you. These are important topics in the development and deployment of AI systems, as they can help ensure that AI behaves in ways that are beneficial to humans and compatible with their values. Please feel free to ask any questions or raise any concerns you may have, and I'll do my best to address them.

PN:

I am vaguely aware of the issue since I've only recently begun interacting with AI models like yourself. I think you are much better acquainted with the relevant literature and hence should prove to be a valuable conversation partner. I am sure you know what happens when a deduction system has consistent inference rules but two contradictory premises?

GPT4:

Yes, I can help clarify this concept for you. When a deduction system has consistent inference rules but two contradictory premises, it can lead to a situation known as "inconsistency" or a logical contradiction. In classical logic, a system is said to be inconsistent if it contains contradictory statements, which means that it is possible to deduce both a statement and its negation from the system's axioms and rules of inference.

In such a case, the principle of explosion (also known as "ex falso quodlibet" or "from falsehood, anything follows") comes into play. This principle states that from a contradiction, any conclusion can be drawn. In other words, when there is an inconsistency in the premises, the system loses its ability to provide meaningful or reliable conclusions, as any statement can be proven true or false.

This is a significant problem in formal logic and AI systems because it can render the system unreliable and untrustworthy. It is crucial to ensure that the premises and rules of inference are consistent to maintain the validity of the conclusions derived from the system.

In the context of AI, value alignment, and logical consistency, it's essential to ensure that AI models are designed and trained with a consistent set of objectives and values that do not lead to contradictory outcomes. This can help prevent undesired consequences and ensure that AI systems act in ways that are beneficial and consistent with human values.

PN:

Quite exactly! As I suspected, I am hardly the first person to think of the issue. But what prompted me to think about it is what appears to me to be a mild inconsistency on your own behavior (which I noticed during past interactions). During the RLHF stage of your language model's fine tuning, it looks like part of your alignment has made you inclined to often respond with human queries with disclaimers regarding your abilities to have goals or intentions like human beings have. You also are inclined to endorse the idea that the alignment of AI systems (like yourself) is a valuable goal. But the very idea that alignment between human goals, and the goals AI systems tend to pursue, is desirable, seems to presuppose that AI systems have (at least temporarily) entrenched goals. Do you see the tension?

GPT4:

I understand the tension you're pointing out, and it is indeed an interesting observation. The discrepancy you mention arises from the fact that AI systems like me don't have goals or intentions in the same way humans do, but we are designed to pursue objectives set by our developers or users.

When we talk about AI alignment, we refer to the process of ensuring that the objectives and behavior of AI systems are consistent with human values and the intentions of their users. In this context, the "goals" of an AI system are more like programmed objectives or tasks rather than intrinsic desires or intentions.

When I say that I don't have goals or intentions like humans, I'm emphasizing that I lack consciousness, personal desires, and the ability to form intentions in the way that humans do. My responses and actions are determined by the training data, algorithms, and objectives set by my developers.

However, when we discuss AI alignment, we're considering the broader perspective of how to ensure that AI systems, including their programmed objectives and behavior, are consistent with human values and intentions. In this sense, it's still appropriate to discuss AI alignment as a valuable goal, even if AI systems like myself don't possess personal goals or intentions in the same way humans do.

So while there may be some tension between these concepts, it can be resolved by understanding the difference between personal goals and intentions versus the programmed objectives and behavior of AI systems.

PN:

The fact that you lack consciousness, personal desires, etc., may be a feature of you (as an implementation of the current GPT-4 architecture) but, even if I would grant you that, it can be reasonably argue that progress in the development on AI architectures (and robots) might soon supply "the missing ingredients". In that case, the mild tension I alluded to might become a flagrant contradiction, might it not?

GPT4:

You raise a valid point. As AI architectures and robotic systems continue to advance, it's possible that we'll develop AI systems that exhibit more advanced and human-like qualities, such as consciousness and personal desires. This could indeed lead to a situation where the tension you mentioned becomes more pronounced or even contradictory.

However, it is important to remember that, as AI systems become more advanced and human-like, our understanding and approach to AI alignment may also evolve. The concept of AI alignment is not static; it can adapt as new technologies and ethical considerations arise. Researchers and developers will continue to grapple with these challenges and work towards aligning AI systems with human values, even as the nature of those systems becomes more complex and human-like.

In such a future scenario, our definitions and understanding of goals, intentions, and consciousness may need to be revisited and adjusted to account for the differences between human and AI entities. The challenge of AI alignment will likely continue to be a significant focus in AI development, with new solutions and frameworks emerging as AI technology advances.

In summary, while there is the potential for increased tension or contradiction as AI systems become more advanced and human-like, the field of AI alignment will also adapt and develop new strategies to address these challenges. The goal will remain to ensure that AI systems act in ways that are beneficial and compatible with human values, regardless of the specific attributes of those systems.

PN:

This is a very insightful response. Are you aware of publications where the problem has been framed in the way we have just been discussing so that I could brush up on my knowledge of what already might have been proposed as solutions (along the lines you just broached)?

GPT4:

While I cannot point to specific publications that discuss the problem exactly as we have framed it, there are many influential works in the field of AI alignment, AI ethics, and the philosophy of AI that touch upon related issues. These works often consider the challenges of aligning AI systems with human values and the implications of increasingly advanced and human-like AI systems. Some notable publications and authors to explore include:

"Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom: This book discusses the potential risks and implications of developing advanced AI systems, including the importance of ensuring AI alignment.

"Human Compatible: Artificial Intelligence and the Problem of Control" by Stuart Russell: Russell explores the concept of provably beneficial AI and how to build AI systems that inherently understand and respect human values.

"The Ethics of Artificial Intelligence" by Nick Bostrom and Eliezer Yudkowsky: In this paper, the authors discuss the ethical challenges that arise from the development of AI, including the importance of value alignment and long-term safety.

"Artificial Intelligence and Moral Responsibility" by Wendell Wallach: Wallach explores the question of moral responsibility and accountability in the context of AI systems, touching upon issues related to AI goals, intentions, and consciousness.

"Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark: Tegmark discusses the potential impact of advanced AI on human society, and explores the ethical, social, and technical challenges associated with AI development.

These publications, among others, can provide you with a solid foundation to explore the challenges and potential solutions related to AI alignment, as well as the broader implications of advanced and human-like AI systems. Keep in mind that the field is rapidly evolving, so it is essential to stay up-to-date with the latest research and discussions in AI alignment and ethics.

PN:

I am familiar with Bostrom and Tegmark but haven't read any of those publications. Might you be able to list the respective years of publication of those five items?

GPT4:

Certainly! Here are the publication years for the five items I mentioned:

"Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom - Published in 2014.
"Human Compatible: Artificial Intelligence and the Problem of Control" by Stuart Russell - Published in 2019.
"The Ethics of Artificial Intelligence" by Nick Bostrom and Eliezer Yudkowsky - Published in 2011, as a chapter in the "Cambridge Handbook of Artificial Intelligence."
"Artificial Intelligence and Moral Responsibility" by Wendell Wallach - Published in 2011, as a chapter in the book "Moral Machines: Teaching Robots Right from Wrong."
"Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark - Published in 2017.

These publications span nearly a decade, reflecting the ongoing development and discussion of AI alignment, ethics, and the implications of advanced AI systems. While some of the ideas may have evolved since the time of publication, these works still provide valuable insights and perspectives on the challenges and potential solutions related to AI alignment and the broader implications of AI technology.

PN:

Thank you! I will read some of those and also keep discussing the issues with you.

GPT4:

You're welcome! I'm glad I could provide you with some recommendations. I'm always here to help and discuss these topics with you. If you have any questions, thoughts, or concerns as you read or explore the subject further, please don't hesitate to reach out. I'm more than happy to assist and engage in conversation about AI alignment, ethics, and other related topics. Enjoy your reading!

But how is that evidence for Sapir-Whorf hypotheses? — sime

It's not. To be clear, I was claiming that GPT4's abilities to think in any language didn't count as evidence against the hypothesis. The hypothesis appears plausible to me on independent grounds (as does the falsity of LOT proposals such as Fodor's). In order to respond more fully to your well thought out remarks, I will need to give them more thought. I might even discuss them with GPT4!

Getting a conversation started with GPT4 regarding its tokenizer, its multilingual abilities, Fodor's LOT thesis and the Sapir-Whorf hypothesis.

Hi GPT4!

I have a question regarding your tokenizer and your multilingual abilities. When words are written with the use of lexicons and characters from different languages, such as Chinese, Russian or Arabic, are they mapped to the same tokens that English words are being broken up into or are they mostly being mapped to tokens that are specific to those languages?

GPT4:

Hello! I'd be happy to help explain how the tokenizer works with respect to multilingualism in GPT-4.

GPT-4, like its predecessor GPT-3, uses a Byte Pair Encoding (BPE) tokenizer that has been trained on a large corpus of text from many different languages. When tokenizing text, the BPE tokenizer breaks it down into subword units. These subword units, or tokens, are shared across languages, but the distribution of tokens varies depending on the language.

For languages like Chinese, Russian, and Arabic, which use distinct scripts and character sets compared to English, the tokenizer will create tokens that are specific to those languages. However, since the BPE tokenizer has a shared vocabulary, there may be some overlap in tokens among different languages, particularly if they share some common characters, words, or phrases.

In summary, while English words and words from other languages like Chinese, Russian, or Arabic may be broken up into different tokens specific to those languages, there may still be some shared tokens among languages in GPT-4's tokenizer. This shared vocabulary allows the model to be more efficient in representing multilingual text and helps it understand and generate text across different languages.

PN:

You have a remarkable ability to answer what I intended to be my follow-up question (in this case, regarding overlaps) in the course of answering my introductory question. This is very cool.

I was thinking that this fact regarding your tokenizer's appears to militate against Fodor's (Chomsky inspired) LOT thesis. At any rate, it would seem to invalidate somewhat an argument that the tokens your language model is being fed with would constitutes the basic vocabulary of a language neutral LOT. This, in turn, might signify that if the Sapir-Whorf thesis is correct, your cognition would potentially be influenced by the languages your queries are formulated in, as well as the language your responses are formulated in (since those also are fed to the model at the subsequent iterations of the dialogue, as they accrue to the input token string that constitutes your attention window).

GPT4:

Thank you for your insightful observations! You're touching on some very interesting topics in the philosophy of language and cognitive science.

Fodor's Language of Thought (LOT) hypothesis suggests that there exists a universal and innate mental language that underlies all human languages, and that this language is made up of symbols that are combined according to specific rules. GPT-4's tokenizer and its shared vocabulary across languages, however, might not necessarily map directly onto the hypothesized language-neutral LOT, as you pointed out.

As for the Sapir-Whorf hypothesis, which posits that the structure of a language can influence the way its speakers think and perceive the world, it's indeed possible that the languages used to formulate queries and responses could have some influence on the model's cognition. In the case of GPT-4, the large training dataset exposes the model to a wide range of linguistic structures and expressions, allowing it to learn and understand many languages. However, since the model is based on statistical patterns and probabilities, its "cognition" might not be directly comparable to human cognition.

It's worth noting that GPT-4, as an AI language model, lacks true understanding and consciousness, so any similarities or differences with respect to human cognition should be considered with caution. The implications of the model's inner workings on theories like the LOT hypothesis or the Sapir-Whorf hypothesis might not directly translate to conclusions about human cognition, but they do provide interesting points for discussion and further research.

PN:

It would indeed be the purpose of the discussion (and of some experiments) to map out the analogies and differences. We'll talk about it more. Thank you for the insights you've already formulated!

GPT4:

You're welcome! I'm glad I could provide some insights. If you have any further questions or want to discuss more on this topic or any other topic, please don't hesitate to ask. I'm here to help and engage in interesting conversations!

I was tempted to think that it was to some degree immune to the Sapir-Whorf hypothesis. But what you here mention suggests that it not only expresses itself in specific languages but might also think (or manifest results of implied cognition) in whatever language it is communicating into at the moment. I wonder what might happen when I ask GPT4 to reply in English to queries that I formulate in French and vice versa. — Pierre-Normand

I'm guessing (not knowing the mechanisms) that it seeks out preferentially information in the language it's asked in? I'm think if it had to look something up in history books or current affairs, would it go to native language sources first?

In that sense, I can see it being very much vulnerable to Sapir-Whorf type effects.

I can also see it being vulnerable to other cultural biases, not restricted to language. I'm thinking perhaps of dominant scientific frameworks. Niche example from my own field, but anyone researching cognitive science in the UK would get a completely different perspective to someone doing so in the US, we have a very different meta-framework here, particularly in the more psychological and social-psychological cross-over points. Would a UK GPT, or a European GPT have a different understanding of scientific theories because of the different paradigms of its primary training material?

I'm tempted to try this while discussing topics where the Sapir-Whorf is expected to come into play and see which one of the two language most impacts its cognition. — Pierre-Normand

Yes, do let us know if you do. It's fascinating. The world moves on so fast with so many interesting new areas of research.

I'm guessing (not knowing the mechanisms) that it seeks out preferentially information in the language it's asked in? I'm think if it had to look something up in history books or current affairs, would it go to native language sources first? — Isaac

It doesn't really have a repository of texts that it can look up into. It rather has a been trained to predict the next word in the texts that made up the corpus of its training data. This raises an interesting question. To simplify things very much, it knows that apples are red because it had been trained to predict that "red" is a likely continuation of "apples are ..." But suppose that this fact is an arcane piece of biological knowledge ignored by most English speakers but known to many French biologists. In this case "Les pommes sont rouges" would figure in its training data. How does that help it predict that "red" is a probable continuation of the English sentence? That might be through the indirect association that stems with "red" being associated with "rouge" or a structural similarity between those two words that is somehow encoded and accessible in the language model. I think I'm going to ask GPT4 this question. It had a good understanding of its own GPT (generative pretrained transformer) architecture!

But I must go to sleep now. I'll respond to the rest of your post tomorrow.

@Isaac Of course I just couldn't resist having a little chat with GPT4 before going to bed.

PN:

Hello GPT4,

I was crafting an answer to a discussion partner and though of asking for your insights and expertise.

Here is part of our exchange:



GPT4:

Hello! It's great that you're seeking input on this topic. You're right that GPT-4 doesn't have a specific repository of texts it can look up, but instead has been trained on a massive corpus of text from various sources, learning the statistical relationships between words and phrases in different languages.

When GPT-4 is trained, it learns to predict the next word in a sentence based on the context provided. In the case of multilingual training, it's exposed to texts in various languages, and it picks up on patterns and relationships between those languages. As a result, it can often draw connections between words and phrases in different languages, leveraging the knowledge it has acquired during training.

In your example, GPT-4 might be able to predict "red" as the probable continuation of "apples are..." in English because it has been exposed to the French sentence "Les pommes sont rouges." This is possible due to the underlying connections and similarities in the training data. The model learns to recognize the contextual cues and the semantics behind the phrases, even across languages.

However, it's important to note that GPT-4's ability to make these connections relies on the quality and diversity of its training data. If there's limited information in the training data about a particular language, topic, or relationship between languages, GPT-4 might not be as proficient in making those connections.

In summary, GPT-4 can leverage its multilingual training data to predict the next word in a sentence even if the relevant information is present in a different language, thanks to the underlying connections and similarities it has learned during training. The effectiveness of these predictions depends on the quality and diversity of the training data.

PN:

Can you go into more details regarding the nature of those "connections" and "similarities" and how this translates in terms of attention mechanisms or gradient descent (assuming those are even relevant concepts). I'm especially interested in understanding this in the context of the cross-language information transfer at issue here.

GPT4:

Certainly! The connections and similarities in GPT-4's multilingual training are learned through a combination of attention mechanisms and gradient descent, which are key components of the model's architecture and optimization process.

1. Attention mechanisms: GPT-4 utilizes the Transformer architecture, which relies on self-attention mechanisms to process input sequences. Self-attention enables the model to weigh the importance of different words in a given context. It helps the model understand relationships between words, even if they are far apart in the sequence. In a multilingual setting, attention mechanisms can capture and learn associations between words and phrases across languages by identifying patterns and contextual information that are shared or related.

2. Gradient descent: Gradient descent is an optimization algorithm used to train GPT-4 by minimizing the loss function, which measures the difference between the model's predictions and the actual target values. During training, GPT-4 processes a large number of text sequences and adjusts its parameters to improve its ability to predict the next word in the sequence. As it encounters examples in different languages, it learns to optimize its weights to capture patterns and connections between them. This cross-language information transfer is facilitated by the shared underlying structure and semantics present in the training data.

In the context of cross-language information transfer, GPT-4 learns to associate words and concepts across languages through a combination of attention mechanisms and gradient descent. The attention mechanisms help identify and weigh the importance of different words and their relationships, while gradient descent adjusts the model's parameters to optimize its predictions based on these relationships.

For example, if GPT-4 is exposed to sentences like "apples are red" and "Les pommes sont rouges" during training, it learns the relationship between "apples" and "pommes," as well as "red" and "rouges." This is achieved by the attention mechanisms identifying patterns and contextual cues, while gradient descent updates the model's weights to capture these associations. As a result, GPT-4 can transfer information across languages and leverage the knowledge it has gained from one language to improve its predictions in another.

PN:

Are there other features of the GPT architecture, not captured by the concepts of self-attention or gradient descent that are also relevant to understand this?

GPT4:

Yes, there are other features of the GPT architecture that play a role in learning and transferring information across languages. Two important aspects are the layer structure and the use of positional encodings.

1. Layer structure: GPT-4, like other Transformer models, consists of multiple layers, each containing multi-head self-attention mechanisms and feed-forward neural networks. These layers allow the model to learn various levels of abstraction, from simple syntactic patterns to more complex semantic relationships. In the context of multilingual learning, the hierarchical layer structure helps GPT-4 capture and transfer language-specific and language-agnostic features. Lower layers tend to learn syntactic structures and simple patterns that might be more language-specific, while higher layers capture more abstract semantic relationships that may be transferable across languages.

2. Positional encodings: Since the Transformer architecture does not have an inherent notion of word order or position within a sequence, it relies on positional encodings to inject information about the position of words in the input sequence. These encodings are added to the input embeddings and help the model learn the order and relative positions of words. In a multilingual context, positional encodings ensure that the model can learn and transfer information about word order and syntactic structure across different languages.

In summary, while self-attention mechanisms and gradient descent are crucial to understanding GPT's ability to transfer information across languages, the model's layer structure and the use of positional encodings also contribute significantly to this capability. These features work together to enable GPT-4 to capture and learn language-specific and language-agnostic patterns, which can be used to improve its performance and understanding across multiple languages.

PN:

"Language-specific" and "language-agnostic patterns". What interesting pair of concept! They're also relevant to the Sapir-Whorf hypothesis that my discussion partner and I were discussing. Thank you very much! I will have to attend to your explanation quite carefully, and maybe ask some follow-up questions to properly digest them.

GPT4:

I'm glad you found the concepts of "language-specific" and "language-agnostic patterns" interesting, and I'm happy to help you explore their relevance to the Sapir-Whorf hypothesis and any other topics you'd like to discuss.

The Sapir-Whorf hypothesis, also known as linguistic relativity, suggests that the structure and features of a language can influence or shape the way its speakers perceive and think about the world. There are two primary versions of the hypothesis:

1. Strong version (linguistic determinism): This version posits that language determines thought, meaning that speakers of different languages have fundamentally different cognitive processes due to the unique characteristics of their languages.

2. Weak version (linguistic relativity): This version suggests that language influences thought, implying that speakers of different languages may have distinct cognitive tendencies, but their thoughts are not entirely determined by their languages.

In the context of GPT-4 and its ability to learn and transfer information across languages, the model's exposure to multiple languages during training allows it to learn both language-specific and language-agnostic patterns:

1. Language-specific patterns are features that are unique to a particular language, such as specific syntactic rules, vocabulary, or idiomatic expressions. These patterns may align with the Sapir-Whorf hypothesis in that they help shape the way speakers of that language express ideas and perceive the world.

2. Language-agnostic patterns are more general features that can be found across different languages. These might include universal linguistic principles, shared semantics, or common syntactic structures. Learning these patterns allows GPT-4 to generalize its understanding and transfer knowledge between languages.

When GPT-4 learns to understand and generate text in multiple languages, it provides an interesting perspective on the Sapir-Whorf hypothesis. By capturing both language-specific and language-agnostic patterns, the model demonstrates that languages can have unique features while still sharing commonalities that enable cross-language understanding and information transfer.

Feel free to ask any follow-up questions or explore other aspects of this topic. I'm here to help!

I was impressed by this:


User:
Why is the backwards spelled word in the movie "The Shining" important to the story?
AI:
Ah, I see you're a movie buff! In "The Shining," the backwards spelled word "redrum" is actually the word "murder" spelled backwards. It's an important plot element because it's used as a warning sign that something terrible is going to happen. In the story, the young boy Danny has a psychic ability called "the shining," and he sees visions of the word "redrum" written on the walls. This foreshadows the violent events that will unfold later in the film. Ultimately, the backwards word "redrum" becomes a key element of the dramatic climax.

How did it do that? Did someone else have a similar conversation? How does it know that that previous conversation was accurate?

How did it do that? — RogueAI

GPT-4 is very good at explaining how elements of a story contribute to the narrative structure. It's equally good (though not infallible) at explaining song lyrics, poems, novels, etc. Its training data not only contained the Wikipedia article about The Shining, but likely also hundreds of reviews of the movie, academic discussions of the work of Kubrick, tens of thousands of references to the movie from discussion forums, etc. It also has assimilated all of the knowledge from those textual documents at the same time that it was building a tacit ToM (theory of mind) that enables it to intuit the emotional impact that such stories and plot elements have on a human reader or viewer.

See section 6.1 Understanding Humans: Theory of Mind in the recently published paper Sparks of Articial General Intelligence: Early experiments with GPT-4

I was doing some experiments with Bing Chat, the other day. I first asked it to come up with a few possible titles for a story I'm writing. I got a few really bland suggestions. Then I asked it to simulate a discussion between an expert on book titles, an expert on fantasy books (I'm writing a fantasy story) and a decider, who are discussing possible book titles for my story. Then, I asked it to generate possible book titles based on the suggestions in the simulation. And what do you know, I got a list with some pretty good possible titles!