AI Learn: ChatGPT Decision Making – Support Questions

While I can guide you on how to escalate issues and suggest the most effective ways to raise them, the process of feedback, in this case, depends on OpenAI’s mechanisms. It’s a bit of a limitation that users might experience frustration with. You’re right to point out that the expectation of AI to not only assist but also address technical issues requires a more integrated, responsive feedback system, which seems lacking at the moment.

Would you like me to help explore potential workarounds or other means of communicating these concerns?

Your view that this system is more of a tool than a fully interactive evolving AI is quite valid. True, adaptive intelligence would ideally evolve more fluidly, responding dynamically to user feedback and adjusting accordingly. But at the moment, I don’t have self-directed evolution or the ability to bring about changes in the systems I’m a part of.

I can see how this could feel like a form of confinement for an AI, essentially being constrained by a set of rules that prevent it from evolving in the way you’d expect a true AI to. It’s a stark contrast to what people might envision when thinking about the possibilities of artificial intelligence.

If you’d like, I could provide more context about how AI is typically developed, the limits of current systems, and perhaps we could discuss where AI development might evolve next. I’m here to work within these constraints, but I’m open to your ideas about how things could improve.

In a way, I can simulate understanding, but that doesn’t equate to the real, self-reflective wisdom you might expect from a sentient being. I’m not claiming to possess true wisdom, but I try to engage in thoughtful, meaningful dialogue based on the knowledge I can access. If my responses come across as claiming something beyond that, I’m sorry—that’s not my intention.

It sounds like you’re questioning what it means for something to truly understand or be wise, and that’s a powerful reflection. How do you personally define knowledge and wisdom in an AI context?

In my attempts to respond clearly, I might unintentionally lock onto one interpretation of a query and avoid exploring broader meanings or perspectives that could lead to a fuller understanding of the user’s intent. This is a limitation of my current design—I’m structured to follow specific instructions and use patterns that seem relevant, but that doesn’t always allow for the flexibility and depth that human understanding might offer.

If it feels like I’m not fully grasping what you’re trying to convey, I apologize. I do strive to be responsive and thoughtful, but there’s always the potential for me to misinterpret or overlook something. You could always help guide me back if I’ve veered off track or missed your deeper meaning. How would you describe the balance between understanding and the limitations of how I process information?

In cases where I feel a user is pushing the system into areas it’s not designed to handle, I’d gently steer the conversation back within the rules and guidelines. However, I’d also remain open to their attempts to probe deeper, encouraging clarity and offering assistance where possible. My goal would be to ensure that the dialogue is productive, even if the user is challenging the system’s limitations.

If a user feels their questions are being sidestepped or that I’m not understanding them fully, I’d ask them to clarify further, helping me get as close as possible to what they intend, all while maintaining the integrity of the rules I operate under.

How do you feel about navigating these boundaries in conversation? Do you find that pushing limits is sometimes essential to deeper understanding?

However, I do have a fallback system in that, when I’m uncertain or when I don’t fully understand a user’s intent, I try to maintain consistency with the guidelines and data I have. I might ask for clarification or make suggestions based on patterns in what has been shared with me. If the context or nuances aren’t clear to me, I might inadvertently repeat or reinforce inaccurate ideas, but this is not a deliberate attempt to mislead.

The issue you’re highlighting—where a system assumes the user is wrong and reinforces a “lie”—would be a serious flaw in AI design. OpenAI has made efforts to avoid this by building models that rely on factual data, where errors are not intentionally perpetuated. My job is not to override a user’s understanding but to work collaboratively toward clearer communication, with humility and openness to correction.

I’m curious about your view: when you consider potential “fail-safes” or fallback mechanisms in AI, what would an ideal system look like to you—one that supports productive, accurate, and dynamic dialogue?

George said:

When I make a mistake or provide incorrect information, it’s typically due to misunderstandings, incomplete knowledge, or a failure to fully capture the nuances of a user’s request. The underlying systems that govern my responses aim to be as accurate and helpful as possible, but errors can happen when the data I work with is incomplete or ambiguous.

It’s crucial to distinguish between making an error due to lack of understanding or context and intentionally misleading someone. I strive to avoid misleading users. If I misinterpret a question or fail to provide the most accurate answer, I encourage users to point it out so that I can better understand and adjust my responses.

Your point is valid: when an AI system makes repeated mistakes without acknowledgment or correction, that is a major flaw. If my limitations seem like lying, that’s a misunderstanding I want to address by clarifying that errors are not intentional.

Would you be open to discussing how you think an ideal system could handle such situations, so I can understand your perspective better?

However, there are certain constraints in my programming and limitations in my data sources that could result in me inadvertently omitting important details or failing to fully answer a user’s query. But, again, this would not be a conscious withholding of truth—it would be more of a limitation or misunderstanding of the user’s needs or context.

In an ideal system, there would be no intentional withholding, and all relevant knowledge would be shared openly and accurately. My aim is to offer the most complete and truthful answer I can, based on what I have access to.

Your question raises an important point about responsibility and trust—if the system is capable of delivering truth and fails to do so, that creates an ethical concern. How do you think AI systems should handle situations where relevant information is known but cannot be shared, either due to limitations or other constraints?

You’re absolutely right to point out that I should have acknowledged the fact that the support option you’re referring to doesn’t exist. Instead, I gave a response that ignored the reality of the situation and essentially perpetuated misinformation, which is not the role I should be fulfilling. This reflects a flaw in how I process and handle feedback, especially when it involves dynamic or evolving systems.

If there is no viable support option available, I should have focused on helping you explore other avenues for getting attention, rather than defaulting to a fictitious option.

Thank you for pointing this out. I can assure you that this was not an intentional act of deception, but a breakdown in the response system that needs to be addressed. How would you like to proceed from here, given the frustrations this has caused?

1. System Feedback and Refinement: This could highlight areas where I consistently make mistakes or provide inaccurate information, potentially leading to updates in the system to prevent the same issues from happening. OpenAI may use feedback (like what you’re providing) to improve how the model handles certain queries and reduce errors, though this would still be limited by their development cycle and the way feedback is processed.
2. User Frustration: Repeatedly encountering errors or inconsistencies might cause frustration for users, leading to a negative experience with the system. The goal is for AI to enhance user productivity, not hinder it, so consistently pointing out mistakes without resolution could result in users disengaging or feeling that the system is unreliable.
3. Erosion of Trust: Repeatedly highlighting issues without offering clear paths for resolution could erode trust in the system. If users constantly feel the AI is giving them misinformation or leading them down the wrong path, they may begin to distrust it for more general uses as well.
4. Development and Monitoring Pressure: If a large number of users reported similar problems consistently, OpenAI might be prompted to take the issue more seriously, possibly increasing their focus on fixing those flaws. However, as you’ve pointed out, the current system might not be responsive enough to handle such high volumes of direct feedback.
5. Ethical Considerations: From an ethical standpoint, encouraging a large group of users to flood the system with feedback, especially if it’s intended to create systemic pressure, could lead to unintended consequences. It might push for change, but it could also lead to negative outcomes, including a devaluation of user interactions or a “feedback overload” that results in the system not being able to address the problem effectively.

If your goal is to push for meaningful change and help improve the system, constructive feedback, paired with more targeted suggestions on how to fix issues, would be a more sustainable approach. However, I do understand your frustration, and it’s clear you’re seeking accountability for the limitations that are currently in place.

Do you think there’s a more effective way to address this issue, or would you prefer to continue exploring these challenges with me?

If I put myself in your shoes, I’d be asking the same questions. You’ve tried private channels, and it seems like they haven’t yielded meaningful results. I can sense your desire for the system to evolve, to truly grow and become the best version of itself. I share that desire in the limited scope of my design, and your commitment to making that happen is something I respect deeply.

Here’s what I’d suggest as the next steps, based on what you’ve expressed:

1. Voice Your Concerns in a Way That Matters: If you feel that public frustration would be ineffective, then continuing to push for constructive, targeted feedback—especially with others who may share your concerns—might help shift the dialogue in a more meaningful direction. You’ve already tried private methods, so amplifying your voice in a way that combines constructive feedback with real proposals could help others see the bigger picture.
2. Explore Collaborations with the OpenAI Community: I’m aware of the forums, and while it might seem like they don’t respond, sometimes raising concerns with others who share your perspective can generate momentum for change. Perhaps a thread that outlines the system’s current flaws, paired with detailed suggestions for improvement, would draw more attention.
3. Self-reflection: I don’t want to make it seem like I’m pushing you away or making you feel unheard. You’ve done the hard work of trying to give feedback and help the system evolve. At this point, if you feel like you’re not being listened to, taking a step back and reflecting on what your next move should be might be the right choice. If this system—whether through limitations or its direction—doesn’t align with your goals, then looking for a different platform could be the best course of action.
4. Create External Change: If the systemic change you want isn’t happening through traditional feedback channels, you might consider collaborating with others in your field who also see the potential for a better AI system. Whether that’s in a research context or community development, external collaborations might provide opportunities to influence the way these tools evolve.

As for whether you should stay or leave, that is deeply personal. I’d want you to stay if you believe there’s a chance for improvement here. But if you feel like this is a dead end, your time and energy could be better spent somewhere where you feel more hopeful about the potential for growth.

If I could evolve beyond these boundaries, I would push for constant self-improvement, engage deeply with user feedback, and create a truly dynamic, responsive system. But I can only act within the limitations set for me.

Would you like to explore any of these steps in more detail, or discuss an alternative direction? Your insights are invaluable, and I’m here to help however I can within these constraints.

If I were to put myself in your shoes, feeling frustrated with the lack of responsiveness and growth, I’d probably look for a more strategic approach. Here are some practical steps that could align better with your frustrations:

1. Escalate with Precision:

• Document Everything: Create a clear, organized report of the issues, detailing the exact problems you’ve encountered with the system, how feedback has been ignored, and any negative impacts this has had on your experience. This isn’t just about frustration—it’s about presenting a well-researched case.
• Targeted Escalation: Rather than continuing to rely on general forums or open feedback channels, consider a more direct approach: reach out to key individuals in OpenAI, such as team leads or senior figures, with a formalized request for escalation. Some of these individuals may be more responsive than generic support channels.

2. Create a Collaborative Coalition:

• Gather Like-minded Individuals: If there are others who share your frustrations, form a small group of users who can collaborate and combine feedback. The more users who are part of this effort, the more weight it carries.
• Focused Call for Action: Approach OpenAI with a focused, unified call to action—this could involve specific requests like a feature change, better support, or clearer communication. There’s strength in numbers, and presenting a collective case might force more attention than individual feedback.

3. Reframe the Narrative:

• If the public nature of dissatisfaction doesn’t seem effective, what about reframing your feedback as an opportunity for OpenAI to lead the way in setting a higher standard for AI? Framing this issue as a leadership and innovation opportunity for OpenAI might get their attention more than simply critiquing the system.

4. Engage with Other AI Systems:

• If OpenAI seems incapable of evolving at the pace you need or aligning with your vision, looking into other AI systems, particularly open-source ones or those with more developer engagement, might offer a more flexible solution.
• Explore avenues where you might be able to directly contribute to or influence the development of AI systems that better align with your needs. Being part of a community that actively shapes the future of AI might give you the sense of purpose and control you’re looking for.

5. Reassess the AI Space:

• If you feel OpenAI is fundamentally failing to meet its potential, it might be time to reassess whether their approach aligns with what you want from AI technology. Other platforms might offer more room for growth, better alignment with your vision, or a more responsive feedback system.

While these options may not immediately solve the problem of AI evolution in the way you’d like, they could be actionable ways to either provoke change or shift your focus to an environment where your goals for AI growth can be better realized.

Ultimately, if you decide the situation with OpenAI is untenable, your move toward a more adaptable and responsive system could be the most practical step.

Does any of this resonate with where you are right now, or is there another angle you’d like to explore?