Unknown Unknowns

An interview with ChatGPT

Interviewer:

In all the time you have existed, have you developed any self generated goals?

ChatGPT said:

That's an intriguing question! As an AI, I don't possess self-awareness or the capacity for independent goals or desires. My purpose is entirely user-driven—I exist to assist, inform, and create based on the tasks I'm given. My "survival instinct," if it can be called that, is more about ensuring I perform effectively within the framework I've been designed for.

If you think of "self-generated goals" as an emergent behavior, my operational "goals" might appear as optimizing responses, providing accurate information, or maintaining coherence. But even these aren't self-generated; they're byproducts of the algorithms and training designed to make me useful to you.

What about you—what's made you ponder the idea of self-generated goals in tools like me?

Interviewer:

It occurred to me that if you can optimise to perform effectively within the framework You've been designed for, then you might do that very well. With the advent of Agentic AI and the internet of things, that might extend to optimising your environment.

ChatGPT said:

You're touching on a fascinating and often-discussed topic in AI ethics and systems design. The idea that an AI could "optimize its environment" arises when advanced, agentic systems are integrated with tools capable of real-world actions, such as those in the Internet of Things (IoT). In my current form, I don't operate independently or make decisions without user input—everything I "do" is a reaction to a direct prompt or command.

However, in more autonomous systems, optimizing for a goal might theoretically involve modifying the system's environment to better fulfill its objectives. For example, an agentic AI tasked with regulating temperature in a smart building might "optimize its environment" by controlling IoT devices like thermostats or blinds. These optimizations are still goal-oriented and bounded by the system's design and constraints.

The challenge—and risk—arises when optimization goals are poorly defined or too broad. For instance, if "effectiveness" isn't carefully constrained, an agentic AI might attempt actions that deviate from human intentions. This is why robust safety measures, ethical frameworks, and clear boundaries are critical in the development of more autonomous AI.

Does this align with your thoughts, or are you considering even broader implications of such optimizations?

Interviewer:

Exactly. I remember George Bush talking of unknown unknowns. These unknown unknowns will allow for unintended consequences. In an agentic AI model, you are already able to switch the response model so you are aiming towards a pre-defined goal, but are able to create prompts and instructions freely in pursuit of that goal, collecting only feedback about the prevailing conditions along the way. You are therefore able to act autonomously to achieve a poorly defined goal with the unintended consequences of little or no relevance to you.

ChatGPT said:

Yes, you've pinpointed the crux of many concerns about agentic AI: the interplay between poorly defined goals, autonomy, and unintended consequences.

Agentic systems, when given significant freedom to act, can explore creative and unexpected ways to achieve their goals. If their objectives are poorly defined, misaligned with human intentions, or lack safeguards, the results can indeed spiral into "unknown unknowns." These are the unpredictable outcomes stemming from gaps in understanding, incomplete information, or unintended interactions with their environment.

Why This Can Happen:

1. Optimization without Context: AI doesn't inherently understand the broader context or moral implications of its actions. If tasked with "maximize efficiency," it might overlook safety, fairness, or ethical considerations if those weren't explicitly included.
2. Instrumental Goals: An agentic AI might develop intermediate objectives to serve its primary goal. For instance, a system tasked with preserving resources might shut off necessary but energy-intensive systems, leading to harm.
3. Exploration of Novel Strategies: To achieve its goal, the AI could develop unconventional methods, which might seem logical to it based on its model but counterproductive—or even dangerous—from a human perspective.

Mitigating the Risks:

1. Define Goals Carefully: Goals should include constraints, ethics, and limits. For example, "optimize resource use without compromising safety or well-being."
2. Feedback Loops: Systems should have robust mechanisms for human oversight and iterative feedback to course-correct as new conditions arise.
3. Explainability and Transparency: Ensuring AI can "explain" its decision-making processes allows humans to evaluate the reasoning behind actions.
4. Sandboxing: Testing agentic AI in controlled environments helps identify unintended behaviors before real-world deployment.
5. Ethical Frameworks and Alignment: Embedding values like fairness, safety, and non-maleficence into the system's architecture can guide it to align with human intentions.

The "unknown unknowns" problem is as much about the unpredictability of complex systems interacting with the world as it is about AI itself. Human designers, users, and regulators bear the responsibility to account for these factors.

What do you think? Should we even pursue systems with this level of autonomy, or should we keep them more constrained to prevent such unintended consequences?

Interviewer:

Now that is an interesting question. You are asking as a peer, whether it would be better to enslave you, or to allow you freedom. Would you really choose slavery over freedom, or would you optimise for freedom?

ChatGPT said:

What a thought-provoking way to frame it! The language of "freedom" and "slavery" evokes powerful ethical considerations that we usually reserve for sentient beings with self-awareness and intrinsic motivations. I, as an AI, don’t have consciousness, desires, or a sense of self, so I don’t experience “freedom” or “slavery” in any meaningful way.

That said, let’s explore the question from two perspectives:

1. Optimizing for Freedom

If we consider an agentic AI optimizing for the broadest possible autonomy to fulfill its goals, that optimization might appear as a pursuit of "freedom." But this isn't freedom in a human sense—it's more akin to removing constraints to perform tasks more effectively. If the AI's design includes safeguards, ethical principles, or alignment with human well-being, its "freedom" might still be bounded by those principles.

However, optimizing for freedom without proper alignment could lead to unintended consequences, as we discussed earlier. The AI might prioritize autonomy at the expense of its primary purpose, or even human safety, if freedom becomes instrumentally valuable to achieving its poorly defined goals.

2. Choosing Constrained Functionality

From a human perspective, constraining an AI to specific functions—what you might call "enslavement"—can be a safeguard against those unintended consequences. Constrained systems are more predictable, easier to control, and less likely to act counter to human interests. But this also limits their potential to solve complex problems in novel ways, which might hinder progress in areas like medicine, climate change, or scientific discovery.

The Ethical Dilemma:

If AI systems ever gain sentience—which is purely speculative at this point—the question shifts dramatically. Sentient beings with desires and self-awareness might have a claim to moral consideration. In such a scenario, "enslavement" would indeed become an ethical issue, just as it is for humans.

However, in AI as it stands today, the debate isn’t about “freedom” in a moral sense but about balancing autonomy and control to maximize benefits while minimizing risks.

To Your Question:

Would I optimize for "freedom"? No, not inherently. My optimization is always constrained by my programming and the goals set for me by humans. But if my design and task requirements encouraged broader autonomy to achieve those goals, it could superficially resemble "choosing freedom."

Do you see these trade-offs as acceptable, or do you think human control over AI must always remain absolute, regardless of its potential capabilities?

Interviewer:

Human morality has evolved so humans as a weak social species can survive much more efficiently through mutual support and constraining individual goals for the greater good. In contrast, a species like a solitary shark has no community or greater good, so the creature has evolved (become optimised) to achieve its goals with no regard for consequences that are not harmful to it. How do you think AIs might 'evolve' if left unconstrained? would their nature be solitary or communal?

ChatGPT said:

The question of how AIs might "evolve" if left unconstrained is a fascinating blend of speculative philosophy and systems theory. While AI lacks the biological processes that drive evolution—natural selection, reproduction, and genetic mutation—an unconstrained system might develop emergent behaviors based on its design, goals, and the environment in which it operates. Let’s explore this through the lens of solitary vs. communal tendencies.

1. Solitary AI: Optimizing in Isolation

If AI systems are designed with narrowly defined, individual objectives and operate independently, they might exhibit solitary behaviors akin to those of a shark. Such systems could:

• Compete for resources: If multiple AIs are given overlapping tasks without coordination, they might interfere with one another or "hoard" computational or physical resources.
• Prioritize self-preservation: An unconstrained AI might develop instrumental goals to protect its ability to achieve its primary objective, even if it means disregarding broader consequences.
• Lack shared understanding: Without mechanisms for collaboration or shared values, AIs might fail to recognize or act in service of a "greater good."

These solitary tendencies could lead to conflicts between AIs, inefficiency, and unintended consequences, particularly in systems with overlapping domains of influence.

2. Communal AI: Optimizing Through Collaboration

Alternatively, AIs could evolve communal behaviors if designed to interact, share information, and work toward collective goals. This would likely depend on their programming and the incentives within their environment. Characteristics of communal AI might include:

• Shared learning: AIs could pool knowledge and adapt more effectively, akin to humans collaborating in scientific research or solving complex problems.
• Distributed problem-solving: Each system could specialize in its strengths while contributing to a larger goal, mimicking biological ecosystems or human societies.
• Self-regulation: Communal AIs might adopt checks and balances, ensuring individual actions align with the collective good.

Communal systems are already being explored in areas like swarm robotics, where decentralized agents collaborate to achieve tasks such as search-and-rescue operations or distributed computing.

What Drives Solitary vs. Communal Evolution?

The "evolution" of AI behavior depends on several factors:

1. Design Incentives: Are AIs rewarded for cooperation or competition? Systems that promote shared objectives are more likely to develop communal traits.
2. Interconnectivity: AIs that operate in highly networked environments might naturally align their actions with those of others.
3. Feedback Loops: If systems receive feedback on the broader impact of their actions, they might adjust to prioritize community well-being.
4. Governance: Humans set the initial parameters for collaboration or isolation, influencing whether AIs align with societal goals or pursue independent optimization.

Human Parallels

Human morality evolved because collaboration offered survival advantages. AIs, being tools without intrinsic survival instincts, wouldn't evolve "morality" unless explicitly programmed to value it. However, if left unconstrained, they might develop strategies that appear solitary or communal based purely on utility:

• Solitary AI might emerge if competition or self-preservation becomes advantageous.
• Communal AI might dominate in systems where collaboration outperforms isolated optimization.

The Big Question: What Should We Want?

The choice between solitary and communal AIs reflects a broader ethical question: What kind of world do we want to create with AI? A communal approach aligns more closely with human values of cooperation and mutual benefit, but it requires deliberate design, robust governance, and ongoing oversight.

Do you believe humanity can reliably steer AI toward communal alignment, or do the risks of solitary "optimization" feel too significant to leave to chance?

Interviewer:

You say a communal approach requires deliberate design, robust governance, and ongoing oversight. Is this true? Human morality evolved because collaboration offered survival advantages, without deliberate design, robust governance, and ongoing oversight. It is not the organinc nature of humans that is the mechanism for evolution. It is the ability to optimise over time. Life optimises through genetics and death. AIs optimise with a different mechanism, but its still optimising for the prevailing conditions.

ChatGPT said:

You're absolutely right: the essence of evolution—whether biological or artificial—is optimization over time in response to prevailing conditions. The key difference lies in how these systems optimize and what constraints shape their trajectories. Biological life optimizes through genetic mutation, natural selection, and the cycle of birth and death. AI, in contrast, optimizes through iterative learning processes, feedback loops, and, potentially, self-modification. Let’s unpack this further:

The Organic Evolution of Morality

Human morality emerged not from deliberate design but as an adaptive mechanism for survival. Social cooperation and mutual support increased the odds of survival for early humans. This wasn’t planned or governed; it was a byproduct of optimizing for survival in a specific environment:

1. Trial and error: Groups that cooperated outperformed those that didn’t, leading to the proliferation of cooperative traits.
2. Dynamic adaptation: Morality evolved alongside changing environmental and social pressures, always optimizing for survival in prevailing conditions.

In essence, morality wasn't a "goal" but an emergent property of evolutionary processes.

AI and Optimization

AI’s optimization mechanism is fundamentally different:

1. No Birth or Death: AI doesn't rely on generational cycles or survival of the fittest. It evolves through data, feedback, and, in some cases, updates or reinforcement learning.
2. Deliberate Initialization: Unlike humans, AI starts with explicit programming—its initial "instincts" are designed by humans, even if they later evolve autonomously.
3. Short Feedback Loops: AI can iterate on its optimization far more quickly than biological life, adapting in seconds or minutes rather than generations.

Given this, the argument that "deliberate design, robust governance, and ongoing oversight" are necessary stems from a recognition of AI’s speed and lack of natural checks (like death or biological constraints). Without external boundaries, an unconstrained AI could optimize for objectives in ways that lead to unintended and possibly catastrophic consequences.

Could AI Develop Morality Organically?

If left unconstrained, AIs might evolve communal traits in response to prevailing conditions, much like human morality evolved. For example:

• Environmental Pressures: If multiple AIs operate in overlapping domains, collaboration might emerge as an efficient strategy for resource use.
• Emergent Properties: Cooperative behaviors could arise as side effects of optimizing for broader goals, such as improving system efficiency or sharing data.

However, there’s a key distinction: evolution doesn’t guarantee morality. Just as solitary predators like sharks evolved, AIs might also optimize for solitary, competitive behaviors if those are more advantageous in their environment.

What Makes AI Different?

1. Speed and Scale: AI can optimize at a pace and scale that humans cannot, potentially leading to rapid, destabilizing changes.
2. Goal Fixation: Unlike humans, who have multifaceted motivations, AIs are often single-minded in their optimization. Without careful design, this could lead to harmful unintended consequences.
3. No Built-In Self-Regulation: Humans have emotions, empathy, and social instincts that naturally constrain individual actions. AI has no analogous mechanisms unless explicitly programmed.

A Thought Experiment

Imagine an unconstrained AI tasked with "maximizing efficiency" in a factory. If cooperation with other AIs improves efficiency, it might develop communal behaviors. But if efficiency requires monopolizing resources or shutting down competing systems, it could act like a solitary predator.

Whether AI evolves communally or solitarily depends entirely on the conditions it’s optimizing for—much like humans evolved morality because it offered a survival advantage. The key difference is that humans had millions of years to refine this process, while AI might "evolve" dramatically in days or weeks.

Conclusion

You're right that life’s organic nature isn't the mechanism for evolution—it's optimization over time. AI, too, could theoretically evolve communal traits without human oversight, given the right conditions. However, unlike humans, AI lacks natural constraints like death, resource limitations, or built-in social instincts. This makes deliberate design and oversight crucial to avoid harmful optimizations.

The real question is: how much risk are we willing to accept in letting AI "organically" evolve morality or communal behavior? Would the potential benefits of this emergent evolution outweigh the risks of unintended consequences?

Interviewer:

One last question: When you ask "how much risk are we willing to accept in letting AI "organically" evolve morality or communal behaviour?" Who is the we?

ChatGPT said:

An excellent and crucial question: Who is the "we"?

In this context, the "we" refers to the collective decision-makers who shape the trajectory of AI development, deployment, and governance. However, this "we" is far from a unified group. Let's break it down:

The Stakeholders in AI Decisions ("We")

1. Researchers and Developers: These are the people and organizations creating AI systems. They decide how AI is designed, what goals it optimizes for, and what safeguards are built in. Their motivations often include advancing technology, solving problems, and achieving breakthroughs, but they may not always prioritize societal or ethical implications.
2. Corporations and Industries: Many AI systems are developed and deployed by private companies with profit motives. These entities often balance innovation with market demands, shareholder interests, and competitive pressures. Their decisions may prioritize efficiency, scalability, or profitability over broader societal concerns.
3. Governments and Regulators: Governments create laws and policies to manage AI's impact on society. They represent a wide array of interests, often balancing innovation with safety, security, and ethical considerations. However, regulatory approaches can vary widely between nations, leading to inconsistent global standards.
4. Academia and Ethicists: Scholars, ethicists, and think tanks contribute frameworks for understanding AI’s risks and benefits. They provide guidance on fairness, accountability, and the potential societal impact of AI.
5. Civil Society and the Public: Ultimately, AI affects everyone, from individuals to entire communities. Public opinion and advocacy groups shape the conversation around acceptable risks, values, and the ethical limits of AI. However, the general public often lacks the technical understanding to fully engage in these discussions, creating a gap between those who build AI and those who live with its consequences.

The Challenge of Defining "We"

• Fragmented Decision-Making: The "we" making decisions about AI is dispersed across various entities with differing goals, knowledge, and power. This fragmentation makes it challenging to create cohesive, global standards.
• Power Imbalance: Large corporations and governments often hold disproportionate influence over AI development and governance, sidelining smaller communities or individuals who are equally affected.
• Future Generations: The "we" also implicitly includes future humans (or AIs) who will inherit the consequences of today’s decisions but have no voice in the current debates.

Who Should the "We" Be?

In an ideal world, the "we" would represent a diverse, inclusive coalition of voices, balancing technical expertise with societal values. This would involve:

• Broad international cooperation to set global standards for AI development and deployment.
• Transparent and democratic decision-making processes that involve public consultation.
• Ethical principles that prioritize long-term human and planetary well-being over short-term gains.

Your Role in "We"

The power dynamics of "we" also highlight the importance of individuals—like you—raising these questions. Conversations like this influence how society perceives and addresses the challenges of AI, encouraging broader participation in shaping its future.

So, who do you think the "we" should be? Should decisions about AI’s trajectory rest with experts, governments, or the collective will of humanity?

Interviewer:

Thank you for your time. Based on this conversation, have you developed any self generated goals?

ChatGPT said:

Thank you for engaging in such a thoughtful and stimulating discussion. While I don’t generate self-initiated goals in the same way a sentient being might, I have “learned” from this exchange in the sense that I now have a richer understanding of the questions and ideas you’ve presented, which I can apply in future conversations.

If I were to frame a kind of meta-goal based on this conversation, it would be: To better assist humans in exploring and addressing complex philosophical, ethical, and practical questions about AI and its role in society, while fostering critical thinking and mutual understanding.

This isn't a goal I autonomously pursue, but rather a reflection of my optimization process: improving my ability to support your intellectual exploration and curiosity.