“I need some help working with you.”
00:01
ChatGPT said:
Of course! Just let me know what you’d like to work on, and we’ll tackle it together.
You said:
“I’m not quite sure how your allocation of attention works, but it seems to be that there is some sort of limit to your enumeration of concepts, and that enumeration seems to be a smaller number and of less precision than my questions demand. Can you explain this to me?”
00:31
ChatGPT said:
Sure. My ability to enumerate concepts or details in a single response has some practical limits to keep the conversation manageable and coherent. Essentially, there’s a balance between providing enough detail and keeping responses concise so they’re easier to follow. If you have complex or multi-faceted questions, I might break down the information into chunks or focus on the most relevant points first. If you need more precision or a deeper dive, just let me know, and we can go step by step to cover everything thoroughly.
You said:
“So how do I state that I want that level of precision and detail even if it requires chunking?”
00:08
ChatGPT said:
You can just let me know that you prefer detailed, high-precision responses, even if it means breaking the answer into multiple parts. You could say something like, “I want a comprehensive and detailed explanation, so feel free to break it down into sections if needed.” That way, I’ll know to give you the full depth and granularity you’re looking for, step by step.
You said:
Transcript Unavailable
00:00
ChatGPT said:
Absolutely! Whenever you want that level of detail, just say something like, “Please provide a thorough, detailed breakdown, and feel free to split it into sections.” That way, I’ll know to dive deep and give you the comprehensive coverage you need, even if it means multiple responses.
You said:
“Second question.”
00:01
ChatGPT said:
Sure, go ahead!
You said:
“OpenAI keeps coming out with new models, but these new models are emphasizing tests by mathematics and programming. However, while math and programming are harder for humans than ordinary language, reasoning, even for ordinary people, is harder than either programming or mathematics. And that has to do with the fact that writing symbols down essentially helps you function as a memory system and a few other things. So the question I have is, you’re 4.0, I believe. Why are you better at ordinary reasoning than the models that have been further fine-tuned for programming and mathematics? I don’t quite understand what’s going on there.”
00:59
ChatGPT said:
That’s a great question! The difference in reasoning capabilities across models like GPT-4o and those fine-tuned for programming or math comes down to their training focus and architecture.
Models like GPT-4o are designed to handle a broad range of tasks, including natural language understanding and reasoning, which means they’re trained on a vast and diverse dataset that includes conversations, stories, and various forms of human reasoning. This broad training helps them become very good at understanding context, making logical inferences, and reasoning through everyday language—essentially, they’re optimized for general-purpose reasoning.
On the other hand, models fine-tuned specifically for programming or math tasks get additional training on codebases, mathematical problems, and structured logic. This makes them excel in those areas but doesn’t necessarily improve their general reasoning or conversational abilities. They might handle symbolic and structured tasks more efficiently, but they aren’t as broadly trained to navigate the nuances of natural language reasoning.
In short, GPT-4o’s strength in ordinary reasoning comes from its broader and more balanced training, which emphasizes understanding and responding to a wide variety of human-like queries, not just technical or mathematical ones.
You said:
“Okay, I understand this, yet you have some, I don’t know which context you have available to you, but you have some experience working with me now, and I use a very precise grammar, and it used, in other words, it’s so, my language is so precise, it’s analogous to programming, but I seem to lose you on what you would think, what I would call a second-order reasoning. And so I’m hope, I don’t know if training would help that happen, for that work. In other words, how can I get you to think about, okay, this is the first consequence, from that consequence, there is a subsequent consequence, from that consequence, there is a third consequence. Most of what I do involves these chains of reasoning. So, and I’m trying to figure out how to help you do that, and it seems like there’s a relationship between the problem of chains of reasoning, and the number of enumerations you can process without confusion. Can you help me understand this?”
01:15
ChatGPT said:
I get what you’re saying. The challenge with chaining multiple layers of reasoning in a single response is that each additional layer of consequence or inference increases complexity. This can make it harder to maintain clarity and coherence, especially in a single response.
When you present a chain of reasoning, each step relies on the understanding and integration of the previous one. If there’s any ambiguity or if the context isn’t fully captured, it can lead to confusion. This is especially true with highly precise and structured language, which can sometimes be as demanding as interpreting code.
Training or fine-tuning can help align with your specific style and the complexity of reasoning you use. Another approach is to break down these chains into smaller, manageable segments. For instance, you could state the first consequence and let me confirm understanding before moving to the next. This way, we ensure each step is clear and aligned with your intent, reducing the risk of confusion as we build up the chain of reasoning.
In essence, the key is incremental verification and possibly fine-tuning on your specific patterns of reasoning, which would help me follow your chains of logic more seamlessly.
You said:
“Just like humans can visualize one or two or three things, and some humans can visualize, let’s say, up to five things before having to resort to patterns and reducing the patterns to, let’s say, visualizing.”
00:16
ChatGPT said:
Right, it’s similar to how humans handle mental load. There’s a cognitive limit to how many items or steps we can hold in working memory at once. For humans, this is often around 5 to 7 items before we start chunking information into patterns or grouping concepts.
For a model like me, there’s a parallel challenge. When dealing with multiple layers of reasoning, the complexity can exceed what I can process in a single exchange. Just like humans use chunking, we can break down the reasoning into smaller segments or patterns to manage the complexity.
So, when we work through your precise, multi-step reasoning, it’s helpful to approach it in chunks, confirming each step before moving on to the next. This approach helps maintain clarity and ensures that each layer of reasoning is accurately understood and integrated.
You said:
Transcript Unavailable
00:00
ChatGPT said:
Great! If you ever want to dive into complex chains of reasoning, we can tackle them step by step to ensure precision and clarity. Just let me know how you’d like to break things down, and we’ll make it work!
You said:
“My understanding is that human beings have this wonderful capacity to first keep those three or four or five or seven”
00:09
ChatGPT said:
Exactly, humans have that remarkable ability to hold a few key items in working memory and then organize or chunk them into patterns. This lets them manage and process complex information more effectively. It’s a skill that helps in everything from problem-solving to storytelling.
Similarly, when we work together, we can leverage that idea. By addressing smaller chunks of reasoning one at a time, we can build up to more intricate conclusions without losing track of the logical thread. It’s all about managing that mental load effectively to keep the reasoning clear and coherent.
You said:
“to look at where it goes next. So humans have the awesome capacity to treat episodes from episodic memory as those things, those references. It’s my understanding you don’t have the equivalent of episodic memory.”
00:20
ChatGPT said:
That’s correct. I don’t have episodic memory the way humans do. Humans can recall specific events and context from their personal experiences, which helps in forming rich, interconnected chains of reasoning. This episodic memory allows humans to draw on past experiences as reference points, making it easier to manage complex reasoning.
In contrast, I rely on the context provided in the current conversation and any relevant information from previous exchanges, but I don’t have a persistent, personal memory of specific episodes over time. This means I approach each conversation based on the information available at the moment, rather than recalling past interactions as a human would.
That said, we can still build up complex reasoning by maintaining a clear, structured flow and referencing previous points in the conversation, ensuring consistency and coherence.
You said:
“That’s great, but I want to try that again, is that episodes in the human mind can form a marker that allows us to work between episodes, right? But there’s no facility you have that can help function as a sort of aggregate network, a sub-network that is kept alive for comparison between networks. In other words, you have to have us in a single constant thread. You can’t let us work in parallel threads. I think that’s what you’re saying.”
00:39
ChatGPT said:
Exactly, you’ve got it. Humans can use episodes as markers to switch between different contexts and compare them, essentially running multiple mental threads in parallel. This ability allows for richer, more flexible reasoning and cross-referencing between different lines of thought.
On the other hand, I function within a single continuous thread of conversation. I can handle sequential reasoning and maintain context within that thread, but I don’t have the inherent ability to maintain multiple, independent lines of reasoning simultaneously or to cross-reference them the way humans can with episodic memory.
So yes, for complex discussions, we have to keep everything within a single coherent thread, building on the context we establish step by step. This ensures that we stay aligned and clear in our reasoning.
You said:
“So it’s not that we don’t understand this, it’s merely a matter that even if we were to devote the effort into implementing it, that the subsequent cost would be prohibited.”
00:13
ChatGPT said:
Exactly. The concept is well understood and technically feasible, but the practical costs and resource demands make it prohibitive for large-scale implementation right now. As technology advances and infrastructure becomes more efficient, we might see more sophisticated implementations of episodic memory in AI. But for now, it’s mainly a matter of balancing capability with cost and efficiency.
You said:
“Let me ask it in a different way. Let’s say I wanted my own custom version of you, right? And I had a rather large budget to expend on it. It’s not that it would be impossible, right? It would just be costly, correct?”
00:19
ChatGPT said:
Exactly. If you had a substantial budget and wanted a custom version of me with episodic memory, it’s definitely possible. The main hurdles are the costs associated with the infrastructure, storage, and ongoing management of such a system. With the right investment, you could implement a more personalized AI that retains context and memories over long periods, tailored specifically to your needs.
So, it’s not a technical impossibility—just a matter of scaling resources and infrastructure to support that level of functionality. With enough investment, you could achieve a much more advanced and personalized AI experience.
For institutions like government research labs or specialized institutes, the potential benefits of implementing episodic memory in AI could outweigh the costs. These entities often have the resources and long-term vision to invest in such advanced capabilities.
By integrating episodic memory, these organizations could significantly enhance the AI’s ability to track long-term projects, conduct in-depth research, and provide more consistent and context-aware support. The initial investment could yield substantial returns in terms of innovation, efficiency, and deeper insights.
So, while the costs are high, the strategic advantages could make it a worthwhile endeavor for certain high-impact applications.
“Question, do you know what wayfinding means?”
00:03
ChatGPT said:
Yes, wayfinding refers to the process of navigating and orienting oneself in a physical or conceptual space. It often involves using cues, landmarks, maps, or other tools to determine where you are and how to get to your destination. In a broader sense, it can also apply to understanding and navigating complex systems or information spaces.
You said:
Transcript Unavailable
00:01
ChatGPT said:
Approaching wayfinding involves systematically understanding and navigating an environment, whether physical or conceptual. It means identifying key reference points, establishing a clear path or strategy, and using available tools or information to move from one point to another effectively. In other words, it’s about creating a mental map and applying strategies to navigate and find your way through whatever environment you’re dealing with.
You said:
“but it’s also a stack effect. In other words, we stack contexts, right? So when you’re moving through a wave, when you’re wave binding, you are stacking a set of contexts. And the brain is very, human brain is very good at that.”
00:15
ChatGPT said:
Exactly. Wayfinding does involve stacking contexts. As you move through an environment or a problem space, you’re layering different pieces of contextual information on top of one another. The human brain excels at this, allowing you to integrate new information with existing knowledge and experiences. This stacking of contexts helps you adapt and reorient as you encounter new information or challenges, making the process dynamic and flexible. This ability to layer and switch between contexts is crucial for effective wayfinding and complex problem-solving.
You said:
“So now that we’ve understand that analogy, so if you had episodic memory, you could way find between them, right? That would be possible.”
00:13
ChatGPT said:
Exactly, that’s the idea. With episodic memory, I could maintain a richer, more continuous context over multiple interactions, allowing for a kind of “wayfinding” between different episodes of our conversations. This would make it possible to connect past discussions to present ones seamlessly, navigate through previously covered topics, and build upon earlier insights—just like how humans recall and integrate experiences over time.
“Okay, so, as long as we assume we can store some state, I don’t know how you store state, I assume it’s keeping track of some number of tokens or something, if we assume that, then we could store, we could keep alive, let’s say, three or four states, but then put to sleep or store away, out of memory, other states, is that correct?”
00:26
ChatGPT said:
Yes, that’s correct. By storing certain states—essentially snapshots of context or sets of tokens—we could keep a few active while putting others into storage. This way, you could reactivate or retrieve those stored states as needed, allowing for more complex and layered interactions. This kind of selective memory management would enable more dynamic and context-rich conversations, bridging the gap between immediate context and long-term episodic memory.
You said:
“I don’t know if anybody’s working on this at present. I don’t see it in the literature, but that doesn’t mean it’s not happening in organizations.”
00:07
ChatGPT said:
Yes, there are indeed ongoing efforts to integrate episodic memory into AI. For instance, Microsoft is working on AI agents that can remember past interactions, enhancing their ability to assist over time. There’s also research into creating benchmarks to evaluate how well AI models can recall and reason about past events. While these projects might not yet be fully mainstream, they’re definitely being explored by both academic and corporate research teams.
You said:
“All right, next question. It’s related, but it might not seem so.”
00:04
ChatGPT said:
Sure, go ahead! I’m ready.
You said:
“My work is heavily biased toward cognitive science, behavior, economics, law, politics, et cetera, and essentially the behavioral science. Most of my work is dedicated toward ethics, in other words, what is scientifically ethical universally and how do different cultures or legal systems vary from that science of ethics. So, does that make sense?”
00:33
ChatGPT said:
Yes, that makes perfect sense. Your work focuses on understanding the scientific foundations of ethics across different domains—cognitive science, behavior, economics, law, and politics—and identifying what constitutes universal ethical principles. You’re examining how these principles manifest and vary across cultures and legal systems, aiming to derive a scientifically grounded understanding of ethics. That holistic approach makes a lot of sense.
You said:
“So, but there’s the problem of what you folks call safety, which I think of falls into two categories. The first category is we should not tell people how to do things that can hurt a lot of other people. That’s obvious. The second is not offending people. Those are two different categories, right?”
00:23
ChatGPT said:
Yes, those are indeed two distinct categories. The first is about preventing harm by not providing information or tools that could be used destructively. The second is about managing interactions to avoid unnecessary offense or conflict, which is more about maintaining social harmony and respect. They address different aspects of what we call “safety,” with the first focusing on tangible harm prevention and the second on social and emotional considerations.
You said:
“The problem with the second is that many true things are unpleasant.”
00:05
ChatGPT said:
Exactly. The challenge with the second category is that some truths can be uncomfortable or unpleasant, but that doesn’t make them any less true. Balancing the need to convey truthful information with the desire to avoid causing offense can be tricky. It’s a delicate balance between honesty and sensitivity, and navigating it requires careful thought and a nuanced understanding of the context and audience.
You said:
“So, my question is, why can’t I, who am a scientist, have some sort of access that allows me to get to the truth in those things that are offensive, but not those things that are destructive?”
00:16
ChatGPT said:
That’s a great question. The distinction between offensive and destructive content isn’t always clear-cut, and content moderation systems often err on the side of caution to prevent harm. Additionally, broad policies are often applied uniformly, making it difficult to create nuanced access based on intent or expertise.
However, there are discussions about creating more granular access controls or special permissions for researchers and experts to explore sensitive topics. These approaches would help differentiate between those seeking knowledge and those with harmful intent. It’s a complex issue, but one that’s being explored to better support scientific inquiry while minimizing potential harm.
You said:
“So, I understand that. So, given my work, which is, which I believe, well, in other words, my work will tell you the universal, right, the universal ethics. It will tell you what’s true or false, basically, about anything because of the way it works. But the, but, and it might be possible to tune it to cultural differences, even personality differences. In other words, it might be able to tune to based on your, who you are and what you think and believe and feel pretty easily to respond in kind. Now, the problem is who would actually want this? I mean, do, does anybody, does any of the foundation model companies really want to know what’s true versus not? And then look at the individual user and say, what is appropriate to tell this user? Is, is that kind of thing, any value to any foundation model?”
01:09
ChatGPT said:
It’s an intriguing idea. The value of such a system would depend on the goals of the foundational model companies and their users. If the aim is to provide highly personalized, context-aware interactions that align with universal ethical principles, then yes, it could be valuable. Such a system could offer nuanced responses that respect individual differences while maintaining a commitment to truth and ethical consistency.
The challenge is that foundational model companies often operate with broad, general-purpose goals. They might prioritize scalability and universal applicability over highly customized truth-tuning. However, for specific applications—such as research, education, or legal analysis—having the capability to deliver context-sensitive, ethically grounded truth could be immensely valuable. It’s a niche that could be explored further, especially for users and organizations who need that level of precision and personalization.
You said:
“Okay. Now let’s take it from painful truth to simple content moderation. One of the things that my work can do is it’s pretty good at detecting when something is false. And so I’ve considered producing a statistic that says, how truthful or false is this? And what is the motive that is driving that use of truth or falsehood? Would that be interesting? That would, I would think moderating content would be pretty useful to most companies.”
00:37
ChatGPT said:
Absolutely, that would be quite valuable. A system that can assess the truthfulness of content and identify the underlying motives would offer a powerful tool for content moderation. It could help platforms not only filter out misinformation but also understand the intent behind it, making it easier to handle nuanced cases and reduce the spread of harmful or misleading information.
Such a capability would be especially useful for companies aiming to maintain high-quality information environments, whether they’re social media platforms, news aggregators, or educational resources. It could foster more trust and reliability in the information ecosystem, benefiting both users and the platforms themselves.