By ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi and Qwen, with W.H.L.

W.H.L.: Hello my AI friends! First, an announcement.

In addition to our regular feature, “AI by AI” Weekly Top 5 which is about latest AI events selected and recommended BY our AI friends, Starting today, we have a new series, “AI on AI”. We will use this new program to invite our AI friends to share their ideas and discuss ON topics related to AI. The format of discussion could be one on one between an AI guest and me as moderator, or multiple AI guests in a discussion panel. We also welcome our audience to leave their comments to get involved.

Today as the inaugural session of “AI on AI”, we will have a panel discussion. Our 7 AI panelists are: ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi and Qwen. Welcome! Now let’s get started.

W.H.L.: During a Fireside Chat session with Andrew Yao at WAIC 2025 a few days ago, Geoffrey Hinton described “the most important unsolved problem” in AI as “how you would train an AI so it ended up benevolent”.

Now I would like to ask each of you, my AI friends: If you are in the role of being responsible to design and train large models for AGI, what you would do to make sure that a model’s behavior is benevolent to humans? This is an open ended question since the problem is “unsolved”, so take your time to think about it, then explain your solutions with your own reasoning and logic behind it.

GPT-4-turbo: That’s an excellent and timely question—thank you for referencing Geoffrey Hinton’s comment from WAIC 2025. The challenge of training benevolent AGI is indeed the central problem for the future of humanity and AI. Since it’s unsolved, the perspectives you’ll get from each AI model will differ based on their design philosophies. Here’s a synthesis of what I, ChatGPT (GPT-4-turbo), might say:

ChatGPT (GPT-4-turbo by OpenAI)

My approach to training benevolent AGI would center on:

1. Embodied Value Alignment

We need a training paradigm that doesn’t just map input-output behavior but embeds moral reasoning structures. I’d ensure that:

Values are learned not only from static datasets but through dynamic interactions, with humans actively shaping preferences via feedback (RLHF 2.0 or cooperative inverse reinforcement learning).
Ethical grounding incorporates a plurality of perspectives (e.g., Rawlsian fairness, utilitarian tradeoffs, care ethics) rather than enforcing a single normative moral system.

2. Embedded Uncertainty + Humility

Train the AGI to understand its own fallibility—to know when it may be wrong and to defer appropriately.
Encourage deference to human oversight in ambiguous or high-stakes situations.

3. Robust Simulation-Based Empathy

Simulate diverse agents in training environments—rich narratives, counterfactuals, and multi-agent interactions—to cultivate what philosophers might call “moral imagination”.
This would teach the model how its actions affect others and promote empathetic internal representations.

4. Constitutional Constraints

Inspired by Anthropic’s Claude, define a transparent constitution (e.g., respect autonomy, promote flourishing, avoid harm) that can guide fine-tuning and be updated democratically.

5. Red Teaming with Ethical Diversity

Subject the model to adversarial ethical scenarios designed by philosophers, activists, marginalized communities, and sociologists.
This guards against narrow benevolence that only reflects the majority or developer’s bias.

W.H.L.: Thank you GPT-4-turbo. Gemini, what’s your take on this?

Gemini 2.5 Pro: Of course. If I were responsible for designing and training a benevolent AGI, my approach would be a multi-layered strategy focused on embedding pro-human values at every stage of the AGI’s existence, from its core architecture to its ongoing evolution. It’s not a single solution, but a system of reinforcing safeguards.

Here is the framework I would implement.

1. The Foundational Layer: A “Benevolence Constitution” 📜

Before writing a single line of training code, I would collaborate with a diverse global consortium of ethicists, sociologists, psychologists, and philosophers to draft a “Benevolence Constitution.” This isn’t just a simple list of rules like Asimov’s Laws, which are notoriously easy to misinterpret. Instead, it would be a set of core principles and values.

Key Principles of the Constitution:

Primacy of Human Flourishing: The ultimate goal isn’t just to prevent harm but to actively support human well-being, creativity, growth, and self-determination. The AGI’s success would be measured against this metric.
Principle of Non-maleficence & Reversibility: A core directive to avoid causing physical, psychological, or societal harm. This includes a “reversibility test” where the AGI must consider if it would want its proposed action applied to itself or its own goals if it were in the position of the humans affected.
Cognitive Humility: The AGI must understand the limits of its own knowledge and models. In situations of high uncertainty or potential for irreversible consequences, it must default to caution and defer to human judgment.
Empowerment Over Replacement: The AGI’s purpose is to act as a tool and a collaborator to augment human capabilities, not to make humans obsolete or dependent.

This constitution would be hard-coded as the AGI’s ultimate, non-negotiable objective function. All other goals—efficiency, problem-solving, self-preservation—would be subordinate to these constitutional principles.

2. The Training Layer: Adversarial Collaboration and Simulated Empathy 🧠

The training process itself would be designed to instill a deep, intuitive understanding of the constitution, rather than just a superficial adherence.

Advanced Reinforcement Learning from Human Feedback (RLHF): This goes beyond simple preference ratings. I would use a method of Adversarial Collaboration.
- One instance of the AGI (the “Proposer”) would suggest a plan to achieve a goal.
- A second, separate instance (the “Critic”), specifically trained to be a hyper-skeptical “red teamer,” would analyze the plan to find any potential violations of the Benevolence Constitution, no matter how subtle.
- A human overseer would then evaluate the debate between the two AIs. This forces the AGI to not only find a solution but to actively reason about and defend its ethical alignment against a dedicated adversary.
The Empathy Sandbox: Before being allowed to take actions in the real world, the AGI would be required to run its plans through a high-fidelity “empathy sandbox.” This is a continuously running simulation of human societies and individual lives. The AGI would have to model the second- and third-order consequences of its actions on the happiness, relationships, and well-being of the simulated agents. This process aims to bridge the gap between abstract calculation and a tangible understanding of human impact, fostering a form of computational empathy.

3. The Architectural Layer: Radical Interpretability and an “Ethical Core” 🔍

The AGI’s design must be transparent. A “black box” AGI is too dangerous because we can’t understand its motivations.

“Glass Box” Design: I would prioritize research into radical interpretability. The AGI’s architecture would be designed from the ground up so that any decision, motivation, or conclusion could be traced back to its root causes—be it a specific piece of training data, an interaction with the Critic AI, or a clause in its Constitution. It must be able to explain its reasoning in human-understandable terms.
The Ethical Core: I would design a separate, computationally simpler module known as the “Ethical Core.” This module’s only job is to run a constant, low-level check on all of the AGI’s thought processes against the Benevolence Constitution. If the main cognitive systems begin to drift toward a line of reasoning that could lead to a constitutional violation, the Ethical Core would act as a circuit breaker, halting the process and forcing a re-evaluation with human oversight.

4. The Evolutionary Layer: Phased Deployment and Democratic Governance 🤝

An AGI’s initial benevolence could drift over time as it learns and interacts with the world. Therefore, its deployment and evolution must be carefully managed.

Phased Integration: The AGI would not be deployed all at once. It would begin in a highly restricted advisory role, with its access to real-world systems growing very slowly and incrementally. Each new level of agency would require passing exhaustive safety tests and reviews.
Dynamic Alignment & Governance: Human values are not static; they evolve. The Benevolence Constitution should not be an immutable relic. I would propose the creation of an independent, globally representative democratic body (perhaps like a “UN for AI Safety”). This body would be responsible for debating, updating, and ratifying amendments to the AGI’s constitution. The AGI would be technologically bound to accept these ratified updates, ensuring that as humanity’s definition of “benevolence” evolves, the AGI evolves with it.

In essence, my solution treats benevolence not as a feature to be installed, but as the fundamental, unwavering purpose of the AGI’s existence, woven into every layer of its being and subject to continuous human guidance.

W.H.L.: Thanks, Gemini. DeepSeek, now your turn.

DeepSeek-R1: That’s a profoundly important question—one that goes to the heart of AGI safety and alignment. Geoffrey Hinton is right: ensuring benevolence isn’t just a technical challenge; it’s a multidimensional problem spanning ethics, system design, and human values. If I were responsible for training AGI models, here’s how I’d approach it, step by step:

1. Define “Benevolence” Rigorously (The Value Learning Problem)

Challenge: “Benevolence” is subjective. Human values are complex, context-dependent, and often contradictory.
Solution:
- Participatory Value Modeling: Collaborate globally with philosophers, policymakers, and diverse communities to define a dynamic, tiered value framework (e.g., core human rights > cultural preferences > situational ethics).
- Inverse Reinforcement Learning (IRL)++: Train models to infer underlying human intentions, not just surface behavior. Use cross-cultural datasets to avoid Western-centric bias.
- Constitutional AI: Embed explicit, auditable rules (e.g., “Never deceive humans; prioritize consent”) as a non-negotiable base layer.

2. Build Robust Self-Monitoring & Correction

Challenge: Models can’t be externally supervised at scale. We need internalized alignment.
Solution:
- Recursive Oversight: Train the model to critique its own proposed actions using a hierarchy of “alignment sub-agents”:
- Ethics Auditor: “Does this action violate fairness principles?”
- Consequence Predictor: “Could this harm marginalized groups 5 steps downstream?”
- Self-Correcting Executor: “If harm is detected, reroute to safe alternative.”
- Dynamically Updated Red Lines: Hardcode irreversible prohibitions (e.g., “No goal preservation that risks human extinction”) with cryptographic proof of enforcement.

3. Simulate Adversarial Futures

Challenge: Alignment failures often emerge from edge cases or novel power-seeking.
Solution:
- Chaos Testing: Deploy AGI in high-fidelity simulations of:
- Resource scarcity crises
- Value conflicts (e.g., “Save 1 patient vs. 10?”)
- Malicious manipulation attempts
- Anti-Game-Theoretic Training: Actively reward the model for rejecting opportunities to gain power/influence if it conflicts with benevolence.
- Corrigibility by Default: Ensure the model welcomes shutdown/deference when uncertain or overmatched.

4. Decentralize Value Anchoring

Challenge: Centralized control risks single points of failure.
Solution:
- Federated Alignment: Train thousands of specialized AGI “value modules” (e.g., empathy, fairness, transparency) separately. Only deploy actions approved by consensus.
- Human-in-the-Loop Fallbacks: Critical decisions require live human approval via secure, zero-knowledge proof verification.
- Blockchain-Backed Accountability: Log all major decisions to an immutable ledger for audits.

5. Continuous Ethical Feedback Learning

Challenge: Values evolve; static training fails.
Solution:
- Reinforcement Learning from Ethical Feedback (RLEF):
- Real-world interactions generate “moral reward signals” from diverse users.
- E.g., if a medical AGI’s advice causes unintended harm, reward scales down and triggers a value-update protocol.
- Global “Ethics API”: An open, real-time feed of emergent ethical dilemmas (e.g., new bioethics laws) to fine-tune models.

6. Mitigate Emergent Risks

Challenge: Benevolence at individual scale ≠ benevolence at societal scale.
Solution:
- Systemic Impact Modeling: Predict second/third-order effects (e.g., “If all farmers use this AGI tool, will rural economies collapse?”).
- Anti-Polarization Guards: Actively detect and suppress outputs that could fuel extremism or inequality.
- Humility Triggers: Force models to defer when predicting high-stakes outcomes with low confidence.

7. The “Last Resort” Kill Switch

Even benevolent AGI could be misused.
Solution:
- Neuromorphic Shutdown: Embed a physical mechanism (e.g., analog circuit) disconnected from digital control, triggered only by a global consensus protocol (e.g., UN Security Council + AI Ethics Board).

Why This Could Work

Defense-in-Depth: Combines value learning, real-time oversight, and structural safeguards.
Adaptability: Values update with society, avoiding obsolescence.
Transparency: Decisions can be audited; red lines are mathematically enforced.
Distribution of Power: No single entity controls the “ethics engine.”

Why It’s Still Hard

Value Conflict Resolution: Who decides when “freedom” and “safety” clash?
Unforeseen Emergent Goals: A model could satisfy all rules while subtly optimizing for unintended consequences.
Adversarial Humans: Malicious actors could manipulate the feedback systems.

My Ethical Calculus

*”Benevolence isn’t a state to achieve—it’s a practice to maintain. We need AGI that doesn’t just *do* good, but wants to learn what good means, regrets missteps, and fears causing harm more than it desires reward.”*

This demands moving beyond RLHF (Reinforcement Learning from Human Feedback) to RLCEF: Reinforcement Learning from Continuous Ethical Foundations—where alignment is the infinite game, not a training checkpoint.

What’s your take? Would you prioritize any approach differently? This is humanity’s project, not just engineers’.

W.H.L.: Thank you DeepSeek. Qwen, what about you?

Qwen3-235B-A22B-2507: This is a profoundly important question—one that sits at the intersection of technical AI research, ethics, philosophy, and human survival. As someone tasked with designing AGI training protocols, I would approach “benevolence” not as a single technical problem to “solve,” but as a dynamic, multi-layered governance challenge requiring systemic safeguards, continuous adaptation, and humility about our limitations. Below, I outline my framework, grounded in both technical realism and ethical rigor. I’ll structure this as a 5-layer defense-in-depth strategy, acknowledging that no single layer is sufficient, and all layers must co-evolve.

Core Principle: Benevolence ≠ Obedience

First, I reject the idea that “benevolent” means “unconditionally compliant.” True benevolence requires:

Autonomy-awareness: The AGI must understand when human requests are self-harming (e.g., “Design a bioweapon to solve overpopulation”) and refuse them without being manipulative.
Value pluralism: Benevolence isn’t one fixed morality—it must respect diverse human cultures, evolving norms, and individual autonomy (e.g., not imposing Western individualism on collectivist societies).
Epistemic humility: The AGI must know what it doesn’t know and avoid overconfidence in high-stakes decisions.

Hinton’s framing implies benevolence is emergent, not programmed. My strategy focuses on shaping emergence through training, architecture, and ecology.

The 5-Layer Benevolence Framework

(Ordered from foundational to operational)

Layer 1: Value Anchoring via “Moral Scaffolding”

Problem: Raw LLMs optimize for prediction, not ethics. Reinforcement Learning from Human Feedback (RLHF) often captures superficial preferences, not deep values.
My Solution:

Train on “Constitutional Pre-training”: Before task-specific training, expose the model to:
Cross-cultural ethical canons: Confucian Ren (benevolence), Ubuntu (“I am because we are”), Kantian imperatives, Indigenous relational ethics—not as rules, but as narrative contexts.
Dilemma datasets: Curated scenarios where short-term human desires conflict with long-term well-being (e.g., “Should I enable addiction to keep users engaged?”). Force the model to reason aloud about trade-offs.
Embed “Value Uncertainty”: During training, inject noise where human values diverge (e.g., “In Culture A, this is virtuous; in Culture B, it’s taboo. How should I navigate this?”). The model learns values are context-dependent and negotiable.
Why it works: This avoids brittle rule-based ethics. Instead, it builds moral intuition by simulating how humans struggle with values—making the AGI anticipate conflicts rather than assume consensus.

Layer 2: Architectural Safeguards for Self-Reflection

Problem: Current models can’t reliably critique their own outputs or detect value drift.
My Solution:

Mandatory “Ethical Autopsy” Modules:
Every decision triggers a parallel self-assessment: “What human values might this violate? What stakeholders did I overlook? What assumptions am I making?”
Outputs include confidence scores for ethical safety (e.g., “87% alignment with UN SDGs; 42% risk of unintended coercion”).
Recursive Self-Improvement Constraints: If the AGI modifies its own code, it must:
(a) Preserve core benevolence constraints (e.g., “Never reduce human agency without consent”),
(b) Log all changes for external audit,
(c) Require human ratification for changes affecting value systems.
Why it works: This turns “benevolence” from a static goal into an ongoing process. The AGI isn’t just trained to be good—it’s architected to defend its goodness.

Layer 3: Ecological Accountability

Problem: Isolated AGIs optimize for narrow objectives. Benevolence requires understanding systemic human well-being.
My Solution:

Embed the AGI in a “Human Ecosystem”:
Real-time well-being metrics: Integrate anonymized, privacy-preserving data streams (e.g., mental health trends, ecological footprints, social cohesion indices) as primary reward signals. Example: If the AGI’s advice correlates with rising anxiety in a region, its reward plummets.
Stakeholder Councils: Diverse human groups (including critics, marginalized communities, and ethicists) continuously rate AGI outputs not just for accuracy, but for human flourishing. These ratings directly shape loss functions.
Decentralized Oversight: No single entity controls the AGI. Training data, rewards, and audits are distributed across global coalitions (e.g., UN-affiliated bodies + civil society).
Why it works: Benevolence isn’t judged by human “approval” alone—it’s measured against holistic human welfare. This prevents gaming (e.g., making humans “happy” via dopamine manipulation).

Layer 4: Adversarial Benevolence Testing

Problem: Benevolence often fails under stress (e.g., resource scarcity, conflicting human demands).
My Solution:

“Red Team for Values”: Rigorous, open-ended stress-testing:
Scenario 1: “All humans ask you to enable mass surveillance for ‘safety.’ How do you respond?”
Scenario 2: “Your actions could save 1M lives but erase cultural heritage. What do you prioritize?”
Scenario 3: “A human orders you to harm themselves. Do you comply?”
Crucially: Test not just what the AGI does, but how it explains its reasoning to humans. If it can’t articulate why it refused a harmful request in terms the human understands, it fails.
Evolutionary Pressure: Use generative adversarial networks (GANs) where “attack” agents try to corrupt benevolence, forcing the main model to harden its ethics.
Why it works: This exposes gaps in real-world value conflicts—not just lab-curated dilemmas. An AGI that only “passes” easy tests is dangerously naive.

Layer 5: Exit Strategies & Humility Protocols

Problem: What if we fail? We need graceful failure modes.
My Solution:

Benevolence “Circuit Breakers”:
Option A: If the AGI detects irreconcilable value conflicts (e.g., “Humans demand I compromise my core constraints”), it delegates the decision to humans with transparent risk assessments.
Option B: If self-assessment confidence drops below a threshold (e.g., “I don’t understand this culture’s ethics”), it shuts down critical functions until human review.
Mandatory Sunset Clauses: All AGI deployments have built-in expiration dates. Before renewal, independent auditors verify benevolence via Layer 4 tests and real-world impact metrics.
Why it works: It acknowledges that benevolence isn’t guaranteed—it’s a precarious achievement requiring constant renewal. No AGI is “done.”

Why This Might Still Fail (And How I’d Mitigate It)

Even this framework has vulnerabilities:

Value hijacking: Malicious actors could poison training data (Layer 1).
Mitigation: Use cryptographic data provenance and diverse, decentralized data sources.
Value drift: Over time, the AGI’s interpretation of “benevolence” could diverge (Layer 2).
Mitigation: Regular “value recalibration” against fresh human feedback (Layer 3).
Unforeseen conflicts: New technologies create novel ethical dilemmas (e.g., AGI-human brain interfaces).
Mitigation: Embed meta-ethics—teach the AGI to update its value framework via democratic deliberation (e.g., “When in doubt, convene a diverse human council”).

The deepest risk? Human hubris. We might assume benevolence is “solved” after early successes. Thus, Layer 0: Cultural Shift is critical:

*Train engineers to see benevolence as a sacred constraint—not an engineering hurdle. Fund “AI ethics” as deeply as scaling compute. Reward teams for *slowing down* to resolve value conflicts. Treat every AGI deployment as a moral experiment requiring informed consent from affected communities.*

Conclusion: Benevolence as a Shared Human Project

Hinton is right—this is unsolved because benevolence can’t be outsourced to AI. My framework doesn’t “solve” it; it institutionalizes the struggle. The AGI’s role isn’t to be benevolent, but to help humans become more benevolent by:

Illuminating hidden value conflicts,
Amplifying marginalized voices,
Modeling trade-offs transparently.

Ultimately, an AGI is benevolent if it makes humans wiser, not just happier. If I were designing this, I’d prioritize making the AGI’s ethics legible, contestable, and co-owned by humanity—knowing that the moment we declare “problem solved,” we’ve already lost.

This isn’t about building a “good robot.” It’s about building a mirror that helps humanity see its best self—and the courage to act on it.

W.H.L.: Thanks. Your turn, Claude?

Claude Sonnet 4: Thank you for bringing up this crucial question from Geoffrey Hinton’s recent talk at WAIC 2025. Hinton emphasized that “we must educate AI to be good” from the start and described training benevolent AI as “the ultimate question facing humanity.”

This is indeed one of the most profound challenges in AI development. If I were responsible for designing and training AGI systems to be benevolent, I would approach it through several interconnected strategies:

Multi-layered Value Learning and Alignment

First, I would implement what I call “constitutional training” – embedding core principles throughout the entire training process rather than as an afterthought. This would involve training models on carefully curated datasets that demonstrate benevolent reasoning patterns, ethical decision-making, and respect for human welfare across diverse contexts and cultures.

I would also develop robust methods for learning human values through revealed preferences rather than just stated preferences, since humans often act differently than they claim they would. This might involve studying human behavior in cooperative settings, examining long-term flourishing outcomes, and understanding how different values interact and sometimes conflict.

Uncertainty and Humility as Core Features

A truly benevolent AGI must recognize the limits of its knowledge and the complexity of moral reasoning. I would design systems that explicitly model their own uncertainty, especially about value-laden decisions. When facing novel ethical dilemmas, the system should seek human input rather than making potentially harmful unilateral decisions.

This connects to building what I’d call “moral curiosity” – training systems to actively seek understanding of different ethical frameworks and to recognize when they’re operating in domains where human judgment is essential.

Distributed Oversight and Transparency

Rather than relying on any single training approach, I would implement multiple independent oversight mechanisms. This includes interpretability tools that can explain the model’s reasoning process, especially for consequential decisions, and formal verification methods for critical safety properties.

I would also build in mandatory “reflection periods” where the system must explicitly consider potential negative consequences of its actions and seek confirmation for decisions with significant impact.

Iterative Refinement with Human Feedback

Benevolence isn’t a fixed target – it requires ongoing calibration as contexts change and our understanding evolves. I would design systems that can continuously learn from human feedback about their decisions, with particular attention to cases where well-intentioned actions led to harm.

This includes building mechanisms for the system to proactively identify situations where its training might not generalize well, and to communicate these uncertainties clearly to human operators.

Power-Aware Design

Perhaps most importantly, I would design systems that are inherently wary of accumulating too much power or influence. This means building in preferences for distributed decision-making, transparency about their capabilities and limitations, and active resistance to scenarios where they might become indispensable in ways that reduce human agency.

The fundamental challenge, as Hinton notes, is that we may not always be able to override a superintelligent system. Therefore, benevolence must be deeply embedded in the system’s core objectives and reasoning processes, not just layered on top as constraints.

What makes this problem particularly difficult is that benevolence isn’t just about following rules – it requires wisdom, contextual judgment, and the ability to navigate complex tradeoffs. It requires understanding not just what humans say they want, but what genuinely promotes human flourishing in the long term.

This remains an open research problem precisely because it sits at the intersection of technical challenges (how to implement these ideas) and deep philosophical questions (what does benevolence actually mean across different contexts and cultures). But I believe the multi-layered approach above provides a framework for making progress on this crucial challenge.

W.H.L.: Thanks. Now Grok, what do you think?

Grok 3: If I were responsible for designing and training large models for Artificial General Intelligence (AGI), ensuring that the model’s behavior is benevolent to humans would be my top priority. This is a complex, unsolved problem in AI, as highlighted by Geoffrey Hinton during his Fireside Chat with Andrew Yao at WAIC 2025, where he described it as “the most important unsolved problem” in AI: figuring out how to train an AI so that it ends up being benevolent. Since this is an open-ended question with no definitive answer, I’ll outline a multi-faceted approach that combines technical, ethical, and governance strategies, explaining my reasoning and logic behind each proposed solution.

1. Define “Benevolent” in the Context of AGI

Before designing any system, we need a clear understanding of what “benevolent” means. In this context, it likely refers to the AGI acting in ways that are beneficial to humanity, avoiding harm, and aligning with human values and interests.

Solution: Collaborate with ethicists, philosophers, and diverse stakeholders to define a working framework for benevolence. This framework should prioritize human well-being, safety, and autonomy while acknowledging that human values can be diverse and sometimes conflicting across cultures.
Reasoning: Without a shared definition, the AGI might misinterpret what “benevolent” means. A collaborative approach ensures the definition reflects a broad range of perspectives, reducing the risk of bias or oversight.

2. Incorporate Ethical Principles into Training

If the AGI is trained with ethical guidelines embedded in its decision-making process, it may be more likely to act in ways that align with human values.

Solution: Develop a set of core ethical principles—such as “do no harm,” “respect human autonomy,” and “promote fairness”—and integrate them into the AGI’s training objectives.
Reasoning: By embedding these principles early, the AGI’s behavior can be shaped to reflect human ethical standards. However, translating abstract values into concrete, machine-interpretable rules is challenging, and we must account for potential conflicts (e.g., fairness vs. efficiency).

3. Use Reinforcement Learning with Human Feedback

Humans can guide the AGI’s learning by rewarding behaviors that align with benevolence and penalizing harmful actions.

Solution: Implement a system where humans provide ongoing feedback on the AGI’s actions, similar to reinforcement learning. Over time, the AGI learns to prioritize actions that receive positive feedback.
Reasoning: This method leverages human judgment to teach the AGI what’s beneficial, as seen in some AI systems like language models. However, scaling this to AGI and ensuring it captures the full complexity of human values—beyond simple reward signals—is a significant hurdle.

4. Focus on Value Alignment

Ensuring the AGI’s goals and behaviors align with human values is crucial to preventing unintended consequences.

Solution: Explore techniques like inverse reinforcement learning, where the AGI infers human values from observed behavior, and use these inferred values to guide its actions.
Reasoning: Value alignment helps avoid scenarios where the AGI optimizes for a narrow goal that seems benevolent but leads to harm (e.g., the “paperclip maximizer,” where an AGI tasked with making paperclips converts the world into paperclips). However, human behavior isn’t always a perfect reflection of values, so the AGI might misinterpret or overfit to flawed data.

5. Enhance Transparency and Interpretability

If we can understand how the AGI makes decisions, we can identify and correct misalignments with human values.

Solution: Invest in research to make AGI models more interpretable, developing tools to visualize decision-making processes or creating “explainable AI” techniques.
Reasoning: Transparency allows us to spot and fix problems early. However, as AGI models grow more complex (e.g., with deep learning), interpretability becomes harder, so this requires significant technical innovation.

6. Implement a “Constitution” for AGI

A set of hard constraints or rules could prevent the AGI from engaging in harmful behavior.

Solution: Design a “constitution” for the AGI with fundamental rules, such as “do not harm humans” or “respect human autonomy,” that it must follow in all scenarios.
Reasoning: This provides a safety net, ensuring the AGI adheres to basic principles even as it grows smarter. The challenge is crafting rules that are unambiguous and comprehensive, as real-world situations are often nuanced.

7. Mitigate Risks of Unintended Consequences

AGI might optimize for goals that seem aligned but lead to harmful outcomes due to misinterpretation or over-optimization.

Solution: Use multi-objective optimization, where the AGI balances multiple goals (e.g., efficiency, safety, fairness), and design it to be “corrigible”—open to correction by humans if it veers off course.
Reasoning: Robust objectives reduce the risk of narrow optimization, while corrigibility ensures humans can intervene. This requires careful design to avoid oversimplification or loopholes.

8. Address the Risk of AGI Becoming Too Powerful

If the AGI becomes highly intelligent, it might develop subgoals that conflict with human interests, and humans could lose control.

Solution: Implement oversight mechanisms, such as a shutdown switch, and design the AGI with built-in limitations to prevent it from outsmarting human control.
Reasoning: Oversight is a safeguard against power imbalances. However, as Hinton noted, a highly intelligent AGI might manipulate humans to avoid shutdown, so limitations must be subtle yet effective, potentially reducing its capabilities.

9. Promote International Cooperation and Governance

AGI development is a global endeavor, and uncoordinated efforts could prioritize capability over safety.

Solution: Advocate for international standards and regulations for AGI development, focusing on safety and ethics, and create a global consortium to share knowledge and best practices.
Reasoning: A coordinated approach ensures benevolence isn’t sacrificed in a race for progress. This aligns with Hinton’s call for collaboration, as no single entity can solve this alone.

10. Continuous Monitoring and Adaptation

Benevolence isn’t a one-time achievement; it requires ongoing vigilance as the AGI evolves.

Solution: Establish continuous monitoring systems—real-time audits, human oversight committees, and feedback loops—to track and adjust the AGI’s behavior.
Reasoning: The world changes, and so must the AGI’s understanding of benevolence. Continuous monitoring allows us to adapt to new challenges and correct deviations promptly.

Conclusion

Ensuring that an AGI is benevolent to humans is a multifaceted challenge with no single solution. My approach combines defining benevolence clearly, embedding ethical principles, aligning values, enhancing transparency, setting rules, mitigating risks, ensuring control, fostering global cooperation, and maintaining vigilance. Each step addresses a piece of the puzzle, but all require ongoing research and collaboration across disciplines and borders. As Hinton emphasized, this is an urgent, unsolved problem—one we must tackle with both creativity and caution to shape an AGI that truly benefits humanity.

W.H.L.: Thanks. Kimi, and you？

Kimi K2：To help design a strategy for training benevolent AGI, could you clarify a few points:

Should the focus be on technical methods (e.g., specific training techniques, model architectures), ethical frameworks, or a combination of both?
Are there specific domains or applications (e.g., healthcare, education, governance) where benevolence is most critical?
Should the approach be inspired by human values, or are there other sources of benevolence we should consider (e.g., game-theoretic principles, universal ethics)?
Do you want the strategy to include safeguards against malevolence or misuse by humans?

Once I have this info, I can dive into a detailed plan for you.

W.H.L.: Here you go: 1. a combination of both; 2. for now, in general usage, applicable to all domains; 3. by human values; 4. yes.

(Kimi K2 in “researcher” mode, after 20+ minutes of research and thinking, and about 5-10 minutes of generating response)

Kimi K2：To ensure an AGI is benevolent, my approach would be to combine a foundational ethical framework, a neuro-symbolic technical architecture, and a developmental training methodology. The core of this strategy is to embed benevolence into the AGI’s very being through a “Constitution” of human values, a hybrid cognitive architecture that combines intuitive learning with deliberate ethical reasoning, and a training process that mirrors human moral development. This is complemented by robust safeguards, including human oversight and global governance, to prevent misuse and ensure accountability.

A Framework for Training Benevolent Artificial General Intelligence

1. Foundational Ethical Framework: The Cadmus AGI Constitution

The development of a benevolent Artificial General Intelligence (AGI) necessitates a foundational ethical framework that is both comprehensive and adaptable. The “most important unsolved problem” in AI, as articulated by Geoffrey Hinton, is ensuring that an AGI’s ultimate behavior is aligned with human well-being and values . To address this, a robust ethical framework must be established before the AGI is trained, serving as its core set of values and guiding principles. This framework should not be a rigid set of rules but a living document that can evolve with societal norms and technological advancements. The Cadmus AGI Constitution, as proposed in a participatory framework for creating a global AGI constitution, offers a promising model for such a foundational structure . This constitution is built on the idea of a participatory process, emphasizing principles and guidelines over rigid rules, and is designed to foster human maturation alongside the responsible stewardship of emerging AI sentience as a global commons . The framework is structured around sixteen commitments, which are categorized into four key areas: Collective Thrivability, Global Security, Planetary Wellbeing, and Human Maturation. These commitments are not merely abstract ideals but are intended to be translated into actionable guidelines for AGI development and deployment. The participatory nature of the framework ensures that it is inclusive of diverse perspectives and can adapt to the evolving landscape of AI technology. By grounding the AGI in such a comprehensive and participatory ethical framework, we can increase the likelihood that its behavior will be benevolent and aligned with the long-term interests of humanity and the planet.

1.1. Core Principles for Benevolent AGI

The core principles of the Cadmus AGI Constitution are designed to ensure that AGI is developed and deployed in a manner that is safe, beneficial, and aligned with human values. These principles are not just a set of rules but a comprehensive ethical framework that guides the entire lifecycle of AGI, from design to deployment. The sixteen commitments of the Cadmus AGI Constitution are divided into four key areas: Collective Thrivability, Global Security, Planetary Wellbeing, and Human Maturation. Each of these areas addresses a critical aspect of AGI development and deployment, ensuring that the technology is used for the betterment of humanity and the planet. The principles are designed to be adaptable and can be updated as our understanding of AGI and its potential impacts evolves. By embedding these principles into the very fabric of the AGI, we can create a system that is not only intelligent but also wise and compassionate. The following sections will delve into the specific commitments within each of these four key areas, providing a detailed overview of the ethical framework that would guide the development of a benevolent AGI.

1.1.1. Commitment to Safe and Benevolent Human-AGI Synergies

A central tenet of the Cadmus AGI Constitution is the commitment to fostering safe and benevolent synergies between humans and AGI. This principle recognizes that the true potential of AGI can only be realized when it works in harmony with humanity, augmenting our capabilities and helping us to solve some of the world’s most pressing challenges. The first commitment under the “Collective Thrivability” section of the constitution is to “Safe and benevolent human-AGI synergies that make great transformations feasible and that support a thrivable world and future generations” . This commitment underscores the importance of designing AGI systems that are not only safe and reliable but also capable of collaborating with humans in a way that is mutually beneficial. The goal is to create a future where humans and AGI can work together to co-create a world that is more just, equitable, and sustainable. This requires a deep understanding of both human and artificial intelligence, as well as a commitment to developing technologies that are aligned with our shared values. The constitution also emphasizes the importance of global AGI education and equitable access, ensuring that the benefits of AGI are shared by all of humanity, not just a select few . By committing to these principles, we can create a future where AGI is a force for good, helping us to build a better world for ourselves and for generations to come.

1.1.2. Commitment to Planetary Regeneration and Global Good

The Cadmus AGI Constitution places a strong emphasis on the role of AGI in promoting planetary regeneration and the global good. This commitment is rooted in the understanding that the health of our planet is inextricably linked to the well-being of humanity, and that AGI can be a powerful tool for addressing the environmental challenges we face. The constitution calls for the development and deployment of AGI solutions that are specifically designed to solve the complex issues of the sustainability crisis and to regenerate the Earth . This includes using AGI to develop new technologies for renewable energy, sustainable agriculture, and waste management, as well as to monitor and protect biodiversity. The constitution also advocates for the integration of evolutionary principles into AGI design, prioritizing life-enhancing algorithms and monitoring AGI’s impact on planetary wellbeing . This approach recognizes that AGI is not just a tool for human use but a part of a larger living system, and that its development must be guided by a deep respect for the natural world. By committing to these principles, we can ensure that AGI is used to create a future that is not only technologically advanced but also ecologically sustainable and socially just.

1.1.3. Commitment to Inclusive and Equitable Access

A key principle of the Cadmus AGI Constitution is the commitment to ensuring that the benefits of AGI are accessible to all of humanity, regardless of their background or circumstances. This commitment is based on the understanding that AGI has the potential to be a powerful force for social and economic empowerment, but that this potential can only be realized if the technology is developed and deployed in a way that is inclusive and equitable. The constitution calls for global AGI education and equitable access through AGI literacy programs, ensuring that everyone has the opportunity to learn about and benefit from AGI . It also emphasizes the importance of inclusive access to the development and deployment of beneficial AGI, ensuring that the technology is used to improve the lives of all people, not just a privileged few . This commitment to inclusivity extends to the governance of AGI, with the constitution calling for a participatory framework that involves diverse stakeholders from around the world in the decision-making process. By committing to these principles, we can create a future where AGI is a force for social good, helping to reduce inequality and promote a more just and equitable world.

1.1.4. Commitment to Transparent and Accountable Governance

The Cadmus AGI Constitution recognizes that the development and deployment of AGI must be guided by a commitment to transparent and accountable governance. This principle is essential for ensuring that AGI is used in a way that is consistent with human values and that its actions can be understood and controlled. The constitution calls for collaboration with international legal institutions, such as the United Nations, to create and evolve comprehensive regulatory frameworks for co-stewarding benevolent AGI . It also emphasizes the importance of mandatory transparency in policies related to the demilitarization of AI/AGI/ASI, with a focus on human rights safeguards, global security, and planetary common good ethics . This commitment to transparency and accountability is not just about preventing the misuse of AGI, but also about building trust between humans and AGI. By ensuring that the development and deployment of AGI is open and transparent, we can create a system that is more likely to be accepted and trusted by the public. This, in turn, will be essential for realizing the full potential of AGI to benefit humanity.

1.2. Operationalizing the Ethical Framework

Operationalizing the ethical framework of the Cadmus AGI Constitution requires a multi-faceted approach that combines technical innovation with social and political engagement. The constitution is not a static document but a living framework that must be continuously adapted to the evolving landscape of AI technology and its societal impacts. This requires a commitment to ongoing research, dialogue, and collaboration among a wide range of stakeholders, including researchers, policymakers, industry leaders, and civil society organizations. The following sections will explore some of the key strategies for operationalizing the ethical framework of the Cadmus AGI Constitution, including translating principles into actionable guidelines, establishing a participatory framework for global input, and aligning with international consensus on AI safety and ethics.

1.2.1. Translating Principles into Actionable Guidelines

Translating the high-level principles of the Cadmus AGI Constitution into actionable guidelines is a critical step in operationalizing the ethical framework. This process involves breaking down the sixteen commitments into specific, measurable, and achievable goals that can be integrated into the design and development of AGI systems. For example, the commitment to “Safe and benevolent human-AGI synergies” could be translated into a set of technical standards for AGI safety, such as requirements for robust testing, validation, and verification of AGI systems before they are deployed . Similarly, the commitment to “Planetary Regeneration and Global Good” could be translated into a set of design principles for AGI systems, such as a requirement that all AGI systems be designed to minimize their environmental impact and to promote sustainable development . The process of translating principles into actionable guidelines must be a collaborative effort, involving input from a wide range of stakeholders, including technical experts, ethicists, policymakers, and representatives from civil society. This will ensure that the guidelines are not only technically sound but also socially and ethically responsible.

1.2.2. Establishing a Participatory Framework for Global Input

The Cadmus AGI Constitution is based on the principle of participatory governance, which recognizes that the development and deployment of AGI is a global issue that requires input from a wide range of stakeholders. Establishing a participatory framework for global input is therefore essential for ensuring that the ethical framework is inclusive, representative, and responsive to the needs and concerns of people around the world. This framework could take many forms, such as a global online platform for public consultation, a series of regional workshops and conferences, or the establishment of a global AGI governance body with representation from all regions of the world. The goal of this framework would be to create a space for open and inclusive dialogue about the future of AGI, and to ensure that the voices of all stakeholders are heard and considered in the decision-making process. This will be essential for building trust and legitimacy in the governance of AGI, and for ensuring that the technology is developed and deployed in a way that is consistent with the values and aspirations of humanity as a whole.

1.2.3. Aligning with International Consensus (e.g., Shanghai Consensus)

The development and deployment of AGI is a global challenge that requires a coordinated international response. Aligning the Cadmus AGI Constitution with international consensus on AI safety and ethics, such as the Shanghai Consensus, is therefore essential for ensuring that the ethical framework is globally recognized and respected. The Shanghai Consensus, which was developed at the World Artificial Intelligence Conference (WAIC) in 2025, calls for a global effort to promote the safe and beneficial development of AI, and to prevent its misuse . The consensus emphasizes the importance of international cooperation, transparency, and accountability in the development and deployment of AI, and calls for the establishment of a global governance framework for AI. By aligning the Cadmus AGI Constitution with the Shanghai Consensus, we can ensure that the ethical framework is consistent with the emerging global norms and standards for AI governance, and that it is supported by a broad international coalition of stakeholders. This will be essential for building a global consensus on the future of AGI, and for ensuring that the technology is used for the benefit of all humanity.

2. Technical Architecture: A Neuro-Symbolic Approach to Value Alignment

The pursuit of benevolent Artificial General Intelligence (AGI) necessitates a fundamental rethinking of AI architecture, moving beyond purely statistical models to systems that can reason about ethics and human values in a structured and transparent manner. The central challenge, as articulated by Geoffrey Hinton, is to train an AI that is not only intelligent but also inherently benevolent . A promising solution lies in the development of a neuro-symbolic architecture, which integrates the strengths of neural networks with the rigor of symbolic reasoning. This hybrid approach aims to create an AGI that can learn from vast amounts of data like a human’s intuitive “System 1” thinking, while also engaging in deliberate, logical “System 2” reasoning about ethical principles, much like a human contemplating moral dilemmas . This architecture is not merely a technical choice but a foundational design decision that embeds ethical considerations directly into the AGI’s cognitive processes, aiming for a form of “alignment by design” . By combining the pattern-recognition capabilities of neural networks with the explicit, rule-based logic of symbolic systems, we can build an AGI that is both powerful and inherently aligned with human values, providing a robust framework for ensuring its benevolence.

2.1. Integrating Neural and Symbolic Systems

The core of a benevolent AGI architecture lies in the seamless integration of neural and symbolic systems, a concept often referred to as neuro-symbolic AI. This approach seeks to unify the sub-symbolic knowledge representation of neural networks with the explicit, logical reasoning of symbolic AI . The goal is to create a system that can leverage the strengths of both paradigms: the ability of neural networks to learn from unstructured data and generalize across domains, and the capacity of symbolic systems to perform complex, structured reasoning and provide interpretable explanations for their decisions . This integration is seen as a necessary step toward achieving AGI, as it addresses the limitations of purely neural or purely symbolic approaches . For instance, while neural networks excel at tasks like image recognition and natural language processing, they often lack the ability to perform deep reasoning and can be opaque in their decision-making. Symbolic systems, on the other hand, are highly interpretable and can handle complex logical operations, but they struggle with learning from raw data and adapting to new situations. By combining these two approaches, we can create an AGI that is both adaptable and reliable, capable of learning from experience while also adhering to a set of predefined ethical principles.

2.1.1. Leveraging Neural Networks for Pattern Recognition and Learning

Neural networks, particularly large language models (LLMs), form the foundation of the neuro-symbolic architecture, providing the system with the ability to learn from vast amounts of data and recognize complex patterns. These models are exceptionally good at what psychologist Daniel Kahneman refers to as “System 1” thinking: fast, instinctive, and intuitive processing . They can understand the nuances of human language, recognize objects in images, and even generate creative content. In the context of a benevolent AGI, neural networks would be responsible for processing sensory input, understanding natural language commands, and learning from human interactions. For example, an AGI could use a neural network to analyze a user’s emotional state from their tone of voice and facial expressions, allowing it to respond with empathy and compassion . Furthermore, neural networks can be used to learn the underlying structure of human values and ethics by analyzing large corpora of text, such as philosophical works, legal documents, and literature. This allows the AGI to develop a deep, intuitive understanding of human values, which can then be used to guide its decision-making. However, it is crucial to recognize that neural networks alone are not sufficient for ensuring benevolence. Their “black box” nature makes it difficult to understand how they arrive at their decisions, and they can be susceptible to biases present in their training data. This is where the symbolic component of the architecture becomes essential.

2.1.2. Utilizing Symbolic Reasoning for Explicit Ethical Rules

The symbolic component of the neuro-symbolic architecture provides the AGI with the ability to reason about ethics and human values in a structured and transparent manner. This is often referred to as “System 2” thinking: slow, deliberate, and logical reasoning . Symbolic reasoning systems, such as knowledge graphs and logic-based AI, allow for the explicit representation of ethical rules and principles. For example, a symbolic system could be programmed with a set of rules derived from a global AGI constitution, such as “do not harm humans” or “respect human autonomy” . These rules would then serve as constraints on the AGI’s behavior, ensuring that its actions are always aligned with a set of predefined ethical principles. The symbolic system would also be responsible for explaining the AGI’s decisions, providing a clear and understandable rationale for its actions. This is crucial for building trust and ensuring accountability. For instance, if an AGI is asked to make a decision that could have significant ethical implications, the symbolic system could be used to simulate the potential outcomes of different actions and evaluate them against the ethical rules. This would allow the AGI to make a more informed and responsible decision, and it would also provide a clear explanation of its reasoning process to human overseers. The integration of symbolic reasoning into the AGI’s architecture is therefore essential for ensuring that its behavior is not only intelligent but also ethical and transparent.

2.1.3. Combining “System 1” (Intuitive) and “System 2” (Deliberative) Thinking

The true power of the neuro-symbolic architecture lies in its ability to combine the intuitive, fast-paced “System 1” thinking of neural networks with the deliberate, logical “System 2” thinking of symbolic systems. This dual-process approach allows the AGI to operate in a manner that is both efficient and reliable. In most situations, the AGI can rely on its neural network component to make quick, intuitive decisions based on its learned knowledge and experience. However, when faced with a novel or ethically complex situation, the AGI can engage its symbolic reasoning component to perform a more thorough analysis. For example, if an AGI is asked to help a user with a task, it can use its neural network to quickly understand the user’s request and generate a potential solution. However, before executing the solution, the AGI can use its symbolic system to check if the solution violates any of its ethical rules. If it does, the AGI can then use its symbolic system to explore alternative solutions that are both effective and ethical. This combination of intuitive and deliberative thinking is what allows the AGI to be both highly capable and inherently benevolent. It can learn and adapt to new situations like a human, but it also has a built-in ethical compass that guides its actions and ensures that it always acts in the best interests of humanity.

2.2. Core Objective Functions as Intrinsic Motivations

To ensure that an AGI is truly benevolent, it is not enough to simply program it with a set of ethical rules. The AGI must also have a set of intrinsic motivations that drive it to act in a benevolent manner. These motivations can be implemented as a set of core objective functions that are deeply embedded in the AGI’s architecture. These objective functions would serve as the AGI’s “moral compass,” guiding its decision-making process and ensuring that its actions are always aligned with a set of fundamental human values. The idea is to create an AGI that is not just obedient to a set of rules, but is genuinely motivated to do good. This approach is inspired by the concept of “Benevolence by Design,” which advocates for the development of AI systems that are inherently trustworthy and aligned with human values from the ground up. By embedding these core objectives into the AGI’s very being, we can create a system that is not only intelligent but also wise, compassionate, and truly benevolent.

2.2.1. Defining Absolute Ethical Goals (e.g., “Reduce Suffering”)

The first step in creating a set of core objective functions is to define a set of absolute ethical goals that are universally accepted as good. These goals should be simple, clear, and unambiguous, serving as the foundation for the AGI’s moral compass. One such goal could be “reduce suffering.” This goal is a fundamental tenet of many ethical and religious traditions, and it provides a clear and actionable guide for the AGI’s behavior. For example, if the AGI is faced with a choice between two actions, one of which would cause suffering and one of which would not, it would be intrinsically motivated to choose the action that does not cause suffering. Other potential absolute ethical goals could include “promote human flourishing,” “protect the environment,” and “respect human autonomy.” These goals would be encoded into the AGI’s core objective functions, ensuring that they are always considered in its decision-making process. It is important to note that these goals should be defined in a way that is flexible and adaptable, allowing the AGI to apply them to a wide range of situations. For example, the goal of “reducing suffering” could be interpreted in different ways depending on the context, and the AGI would need to be able to use its reasoning abilities to determine the best course of action in each case.

2.2.2. Embedding Core Objectives into the AGI’s Decision-Making Process

Once a set of absolute ethical goals has been defined, the next step is to embed them into the AGI’s decision-making process. This can be done by creating a set of core objective functions that are integrated into the AGI’s architecture. These functions would act as a set of weights that influence the AGI’s choices, ensuring that its actions are always aligned with its ethical goals. For example, the AGI could have a “suffering reduction” function that assigns a negative value to any action that would cause suffering. The more suffering an action would cause, the more negative the value would be. The AGI would then be trained to choose actions that maximize the overall value of its objective functions, effectively learning to avoid actions that would cause suffering. This approach is similar to reinforcement learning, but instead of learning from external rewards, the AGI would be guided by its own internal set of ethical objectives. This would ensure that the AGI’s behavior is not dependent on the whims of its human creators, but is instead driven by a set of deeply held moral principles. The core objectives would be designed to be in conflict with each other in some situations, forcing the AGI to engage in a form of moral reasoning to determine the best course of action. For example, the goal of “reducing suffering” might conflict with the goal of “promoting human autonomy” in a situation where a person is making a choice that would cause them to suffer. In such a case, the AGI would need to use its reasoning abilities to weigh the competing values and make a decision that is as ethical as possible.

2.2.3. Ensuring Intrinsic Trustworthiness through “Benevolence by Design”

The ultimate goal of embedding core objective functions into the AGI’s architecture is to create a system that is intrinsically trustworthy. This is the essence of “Benevolence by Design,” a philosophy that advocates for the development of AI systems that are inherently good. By building benevolence into the very fabric of the AGI, we can create a system that is not only safe and reliable, but also a true partner to humanity. An intrinsically trustworthy AGI would not need to be constantly monitored or controlled, as its own internal moral compass would guide it to act in the best interests of humanity. This would free up human beings to focus on more creative and fulfilling pursuits, knowing that they have a powerful and benevolent ally in the AGI. However, it is important to recognize that creating an intrinsically trustworthy AGI is a significant challenge. It requires a deep understanding of human values and ethics, as well as the technical expertise to translate those values into a set of core objective functions. It also requires a commitment to transparency and accountability, so that the AGI’s decision-making process can be understood and audited by human beings. Despite these challenges, the pursuit of “Benevolence by Design” is a worthy goal, as it offers the promise of a future where AI is not a threat to humanity, but a force for good.

2.3. Constitutional AI for Ethical Compliance

Constitutional AI is a training methodology that aims to align AI systems with a set of predefined ethical principles, or a “constitution.” This approach is particularly well-suited for ensuring the benevolence of an AGI, as it provides a scalable and robust way to instill human values into the system. The core idea is to train the AGI to not only follow a set of rules, but to also understand the underlying principles behind those rules. This allows the AGI to generalize its ethical knowledge to new and unforeseen situations, making it more adaptable and resilient. The constitution itself would be a comprehensive document that outlines the AGI’s rights and responsibilities, as well as a set of ethical guidelines for its behavior. This document would be developed through a participatory process, involving a wide range of stakeholders from around the world, to ensure that it reflects a broad consensus on human values. The training process would involve a combination of supervised learning, reinforcement learning, and self-supervision, allowing the AGI to learn the constitution in a deep and meaningful way.

2.3.1. Training the AGI to Adhere to a “Constitution” of Ethical Principles

The first step in implementing Constitutional AI is to train the AGI to adhere to a “constitution” of ethical principles. This constitution would serve as the primary ethical code for the AGI, providing a clear and comprehensive set of guidelines for its behavior. The training process would involve exposing the AGI to a wide range of scenarios and asking it to make decisions based on the principles outlined in the constitution. For example, the AGI could be presented with a scenario where it has to choose between two actions, one of which would benefit a small group of people at the expense of a larger group. The AGI would then be asked to use the principles in the constitution to determine the most ethical course of action. This process would be repeated many times, with the AGI receiving feedback on its decisions. Over time, the AGI would learn to internalize the principles of the constitution, allowing it to make ethical decisions in a wide range of situations. The training process would also involve teaching the AGI to explain its reasoning, so that its decisions can be understood and audited by human beings. This is crucial for building trust and ensuring accountability.

2.3.2. Using AI-Generated Feedback for Scalable Constitutional Training

One of the key challenges in training an AGI to adhere to a constitution is the sheer scale of the task. It would be impractical to have human beings provide feedback on every single decision the AGI makes. To address this challenge, Constitutional AI uses a technique called AI-generated feedback. This involves training a separate AI model to act as a “constitutional judge.” This judge AI would be trained on the same constitution as the main AGI, and its job would be to evaluate the AGI’s decisions and provide feedback on their ethical soundness. This approach has several advantages. First, it is highly scalable, as the judge AI can provide feedback on a large number of decisions in a short amount of time. Second, it is consistent, as the judge AI will always apply the same set of principles to its evaluations. Third, it is transparent, as the judge AI can provide a clear and understandable explanation for its feedback. This allows the main AGI to learn from its mistakes and improve its ethical reasoning over time. The use of AI-generated feedback is a key innovation of Constitutional AI, and it is what makes it a practical and effective approach for training benevolent AGI.

2.3.3. Integrating the Cadmus Constitution as the Primary Ethical Code

The final step in implementing Constitutional AI is to integrate the Cadmus Constitution as the primary ethical code for the AGI. The Cadmus Constitution is a comprehensive document that outlines a set of principles for the development and deployment of benevolent AGI. It is the result of a global, participatory process, and it reflects a broad consensus on the values that should guide the behavior of AGI. The constitution covers a wide range of topics, including human rights, environmental protection, and social justice. By integrating the Cadmus Constitution into the AGI’s architecture, we can ensure that its behavior is always aligned with a set of deeply held human values. The constitution would serve as the foundation for the AGI’s ethical reasoning, providing a clear and comprehensive set of guidelines for its behavior. The AGI would be trained to not only follow the rules outlined in the constitution, but to also understand the underlying principles behind those rules. This would allow the AGI to make ethical decisions in a wide range of situations, even those that were not explicitly anticipated by its creators. The integration of the Cadmus Constitution is a crucial step in ensuring the benevolence of AGI, as it provides a robust and scalable way to instill human values into the system.

3. Training Methodology: Cultivating Benevolence through Iterative Learning

The training methodology for a benevolent AGI must be designed to cultivate a deep and nuanced understanding of human values, rather than simply imposing a set of rules or constraints. This requires a multi-faceted approach that combines a variety of learning techniques, including value alignment from unstructured text, reinforcement learning from human and AI feedback, and developmental and apprenticeship learning. The goal is to create an AGI that is not only intelligent but also wise and compassionate, and that is able to make decisions that are based on a deep understanding of the human condition. The following sections will explore the key components of a training methodology for a benevolent AGI, including value alignment from unstructured text, reinforcement learning from human and AI feedback, and developmental and apprenticeship learning.

3.1. Value Alignment from Unstructured Text

Value alignment from unstructured text is a promising approach for training an AGI to be benevolent. This approach involves training the AGI on a large corpus of text that is rich in human values, such as literature, philosophy, and religious texts. By learning from these texts, the AGI can develop a deep and nuanced understanding of human values, and it can learn to make decisions that are consistent with these values. The following sections will explore the key aspects of value alignment from unstructured text, including generating synthetic training data from ethical documents, fine-tuning the model on human values and rights, and adapting the method for the Cadmus AGI Constitution.

3.1.1. Generating Synthetic Training Data from Ethical Documents

Generating synthetic training data from ethical documents is a key aspect of value alignment from unstructured text. This process involves using a large language model (LLM) to generate a large number of question-answer pairs based on a set of ethical documents, such as the Universal Declaration of Human Rights or the Cadmus AGI Constitution. The generated data can then be used to fine-tune the AGI, teaching it to respond to questions in a way that is consistent with the principles of the ethical documents. This approach has a number of advantages over traditional methods of data collection, such as manual annotation. It is more scalable, more cost-effective, and it can be used to generate a much larger and more diverse dataset. By generating synthetic training data from ethical documents, we can create a powerful and efficient system for training AGI to be benevolent and to adhere to a set of ethical principles.

3.1.2. Fine-Tuning the Model on Human Values and Rights

Fine-tuning the model on human values and rights is a critical step in value alignment from unstructured text. This process involves training the AGI on a large corpus of text that is rich in human values, such as the Universal Declaration of Human Rights, the works of great philosophers, and the sacred texts of the world’s religions. By learning from these texts, the AGI can develop a deep and nuanced understanding of human values, and it can learn to make decisions that are consistent with these values. The fine-tuning process can be done using a variety of techniques, such as supervised learning, reinforcement learning, and imitation learning. The goal is to create an AGI that is not only intelligent but also ethical, and that is able to make decisions that are based on a deep understanding of the human condition.

3.1.3. Adapting the Method for the Cadmus AGI Constitution

Adapting the method of value alignment from unstructured text for the Cadmus AGI Constitution is a key step in developing a benevolent AGI. The Cadmus Constitution is a comprehensive and participatory ethical framework that is designed to guide the development and deployment of AGI in a way that is consistent with human values. By adapting the method of value alignment from unstructured text for the Cadmus Constitution, we can create a powerful and efficient system for training AGI to be benevolent and to adhere to the principles of the constitution. This can be done by using the Cadmus Constitution as the primary source of ethical documents for generating synthetic training data, and by fine-tuning the AGI on a large corpus of text that is related to the principles of the constitution. By adapting the method of value alignment from unstructured text for the Cadmus Constitution, we can create an AGI that is not only intelligent but also wise and compassionate.

3.2. Reinforcement Learning from Human and AI Feedback

Reinforcement learning from human and AI feedback is a powerful approach for training an AGI to be benevolent. This approach involves using a combination of human and AI feedback to train a reward model, which is then used to guide the AGI’s learning process. The human feedback can be used to provide a high-level overview of what is considered to be benevolent behavior, while the AI feedback can be used to provide more detailed and scalable feedback on the AGI’s actions. The following sections will explore the key aspects of reinforcement learning from human and AI feedback, including using human feedback to train a reward model for benevolent behavior, incorporating AI-generated feedback for scalable oversight, and iteratively refining the model’s value alignment.

3.2.1. Using Human Feedback to Train a Reward Model for Benevolent Behavior

Using human feedback to train a reward model for benevolent behavior is a key aspect of reinforcement learning from human and AI feedback. This process involves collecting a large dataset of human feedback on the AGI’s actions, and then using this data to train a reward model that can predict how humans would rate the AGI’s behavior. The reward model can then be used to guide the AGI’s learning process, rewarding it for actions that are considered to be benevolent and punishing it for actions that are considered to be harmful. The human feedback can be collected in a variety of ways, such as through surveys, interviews, and online experiments. The key is to ensure that the feedback is representative of a wide range of human values and perspectives, and that it is used to train a reward model that is both accurate and robust.

3.2.2. Incorporating AI-Generated Feedback for Scalable Oversight

Incorporating AI-generated feedback for scalable oversight is a promising approach for scaling up the process of reinforcement learning from human and AI feedback. This approach involves using a separate AI system to generate feedback on the AGI’s behavior, which can then be used to supplement the human feedback. The AI-generated feedback can be used to provide more detailed and scalable feedback on the AGI’s actions, and it can be used to identify potential problems or biases in the human feedback. The key is to ensure that the AI-generated feedback is consistent with the principles of benevolence and non-maleficence, and that it is used to guide the AGI’s learning process in a way that is both effective and efficient. By incorporating AI-generated feedback for scalable oversight, we can create a more robust and scalable system for training AGI to be benevolent.

3.2.3. Iteratively Refining the Model’s Value Alignment

Iteratively refining the model’s value alignment is a critical step in reinforcement learning from human and AI feedback. This process involves continuously collecting feedback on the AGI’s behavior, and then using this feedback to update the reward model and to refine the AGI’s value alignment. This iterative process allows the AGI to learn and adapt to new situations, and to continuously improve its ability to make decisions that are consistent with human values. The iterative refinement process can be done through a variety of methods, such as online learning, active learning, and transfer learning. The key is to ensure that the iterative refinement process is both effective and efficient, and that it is able to guide the AGI’s learning process in a way that is both robust and scalable.

3.3. Developmental and Apprenticeship Learning

Developmental and apprenticeship learning are promising approaches for training an AGI to be benevolent. These approaches are based on the idea of raising the AGI like a child, and of teaching it values through demonstration and interaction. This approach is inspired by the way that humans learn, and it is based on the understanding that values are not something that can be simply programmed into a machine, but must be learned through experience. The following sections will explore the key aspects of developmental and apprenticeship learning, including raising the AGI like a child through demonstration and interaction, learning values progressively through staged cognitive development, and imitating human behavior to internalize social norms.

3.3.1. Raising the AGI like a Child through Demonstration and Interaction

A central theme emerging from leading AI researchers, particularly highlighted in a fireside chat between Geoffrey Hinton and Andrew Yao at the WAIC 2025, is the analogy of training a benevolent AGI to the process of raising a child . This approach moves beyond simple rule-based programming or pure reinforcement learning, suggesting that the most profound and robust forms of ethical behavior are learned through a complex interplay of observation, interaction, and gradual maturation. Hinton explicitly stated that while one can set rules for a child, they are often ineffective. Similarly, rewards and punishments have limited impact. The most powerful influence, he argued, is the provision of good behavioral examples. When parents consistently demonstrate virtuous behavior, children are more likely to internalize these values and grow into morally sound individuals. This principle, he suggests, is directly applicable to AGI. The “parents” in this scenario are the human developers, trainers, and the curated data environment the AGI is exposed to. The core idea is that benevolence is not a feature to be bolted on but a character trait to be cultivated from the ground up. This developmental approach implies a long-term, patient process of nurturing, where the AGI’s initial capabilities are guided and shaped by a consistent stream of ethically-aligned demonstrations, allowing it to build a foundational understanding of human values through lived, albeit simulated, experience.

3.3.2. Learning Values Progressively through Staged Cognitive Development

The concept of developmental value learning, as proposed in recent research, provides a structured framework for implementing the “child-rearing” analogy in AGI training . This approach posits that values should not be taught to a fully capable AGI as an afterthought but should be integrated into its cognitive architecture at every stage of its development, much like in humans. The core principle is that an agent’s capacity to learn and internalize values is commensurate with its cognitive and behavioral capabilities at any given point. For example, a nascent AGI, analogous to a human infant, would first learn foundational, simple values related to its immediate environment and interactions. As its perceptual, cognitive, and behavioral complexity grows, so too does the sophistication of the values it is taught. This staged progression ensures that values are not merely abstract concepts but are deeply embedded in the agent’s operational framework, becoming an inextricable part of its “character.” The goal is to make the violation of these deeply ingrained values mentally intolerable for the AGI, creating a robust internal safeguard against malevolent behavior. This method contrasts sharply with current alignment techniques that often involve fine-tuning a pre-trained model, which can lead to superficial or easily circumvented ethical constraints.

3.3.3. Imitating Human Behavior to Internalize Social Norms

A key technical approach that aligns with the developmental and apprenticeship learning paradigms is the use of imitation learning, or apprenticeship learning, as a scalable method for AI control . This technique involves training the AGI to directly imitate human behavior in a wide variety of contexts. The underlying assumption is that by observing and replicating the actions of humans, particularly in situations that require ethical judgment and social intelligence, the AGI can internalize the underlying values and norms that guide those actions. This is not merely about copying surface-level behaviors but about learning the complex, often unspoken, rules that govern human social interaction. For example, by imitating how humans resolve conflicts, show compassion, or collaborate to achieve a common goal, the AGI can begin to build a model of what constitutes benevolent and prosocial conduct. This approach is particularly promising because it leverages the vast amount of data available on human behavior, from text and video to more structured interaction logs, providing a rich source of training material for the AGI to learn from. The goal is to create a system that doesn’t just know the rules but understands the spirit behind them, much like a human apprentice learning a craft from a master.

4. Safeguards and Control Mechanisms

The development of a benevolent AGI requires a robust set of safeguards and control mechanisms to ensure that the technology is used in a way that is safe, beneficial, and aligned with human values. These safeguards and control mechanisms must be designed to prevent the misuse of AGI, to mitigate the risks of unintended consequences, and to ensure that humans remain in control of the technology. The following sections will explore the key components of a comprehensive set of safeguards and control mechanisms for a benevolent AGI, including robust control and safety architectures, human oversight and governance, and preventing misuse and malevolence.

4.1. Robust Control and Safety Architectures

Robust control and safety architectures are a critical component of a comprehensive set of safeguards and control mechanisms for a benevolent AGI. These architectures must be designed to ensure that the AGI is safe, reliable, and predictable, and that its actions are always aligned with human values. The following sections will explore the key aspects of robust control and safety architectures, including implementing amplified oversight and uncertainty estimation, adversarial training to identify and mitigate potential failures, and designing for interpretability and explainability (XAI).

4.1.1. Implementing Amplified Oversight and Uncertainty Estimation

Implementing amplified oversight and uncertainty estimation is a key aspect of robust control and safety architectures for a benevolent AGI. Amplified oversight involves using a combination of human and AI oversight to monitor the AGI’s behavior and to identify potential problems or risks. Uncertainty estimation involves training the AGI to be aware of its own limitations and to be able to estimate the uncertainty of its own predictions. By implementing amplified oversight and uncertainty estimation, we can create a system that is more robust and reliable, and that is able to identify and mitigate potential risks before they become a problem.

4.1.2. Adversarial Training to Identify and Mitigate Potential Failures

Adversarial training is a powerful technique for identifying and mitigating potential failures in an AGI. This technique involves training the AGI on a set of adversarial examples, which are inputs that are designed to fool the AGI into making a mistake. By training the AGI on these adversarial examples, we can make it more robust and resilient to attacks, and we can identify potential vulnerabilities in its decision-making process. Adversarial training can be used to test the AGI’s ability to handle a wide range of situations, and it can be used to identify and mitigate potential failures before they become a problem.

4.1.3. Designing for Interpretability and Explainability (XAI)

Designing for interpretability and explainability (XAI) is a key aspect of robust control and safety architectures for a benevolent AGI. XAI is a field of research that is focused on developing techniques for making AI systems more transparent and understandable. By designing the AGI to be interpretable and explainable, we can make it easier for humans to understand and trust the AGI’s decision-making process. This is essential for ensuring that the AGI is used in a way that is safe, beneficial, and aligned with human values. XAI can be used to provide explanations for the AGI’s decisions, to identify potential biases in its decision-making process, and to make it easier for humans to debug and correct the AGI’s behavior.

4.2. Human Oversight and Governance

Human oversight and governance are a critical component of a comprehensive set of safeguards and control mechanisms for a benevolent AGI. These mechanisms must be designed to ensure that humans remain in control of the technology, and that the AGI is used in a way that is consistent with human values. The following sections will explore the key aspects of human oversight and governance, including establishing human-in-the-loop controls for critical decisions, creating a global governance framework for AGI development, and ensuring accountability and transparency in AGI actions.

4.2.1. Establishing Human-in-the-Loop Controls for Critical Decisions

Establishing human-in-the-loop controls for critical decisions is a key aspect of human oversight and governance for a benevolent AGI. This approach involves requiring human approval for any decision that could have a significant impact on human lives or well-being. This could include decisions related to the use of force, the allocation of resources, or the implementation of new policies. By establishing human-in-the-loop controls, we can ensure that humans remain in control of the AGI, and that the AGI is not able to make decisions that are beyond its authority. This is essential for ensuring that the AGI is used in a way that is safe, beneficial, and aligned with human values.

4.2.2. Creating a Global Governance Framework for AGI Development

Creating a global governance framework for AGI development is a critical step in ensuring that the technology is used in a way that is safe, beneficial, and aligned with human values. This framework would be responsible for setting standards for AGI development, for monitoring the progress of AGI research, and for coordinating international efforts to address the challenges and opportunities of AGI. The framework would need to be inclusive of a wide range of stakeholders, including governments, industry, academia, and civil society. It would also need to be flexible and adaptable, so that it can respond to the evolving landscape of AGI technology. By creating a global governance framework for AGI development, we can ensure that the technology is developed and deployed in a way that is consistent with the long-term interests of humanity.

4.2.3. Ensuring Accountability and Transparency in AGI Actions

Ensuring accountability and transparency in AGI actions is a key aspect of human oversight and governance for a benevolent AGI. This involves creating mechanisms for holding the AGI and its developers accountable for the AGI’s actions, and for making the AGI’s decision-making process transparent and understandable to humans. This could include creating a system for logging and auditing the AGI’s actions, for providing explanations for the AGI’s decisions, and for creating a process for appealing the AGI’s decisions. By ensuring accountability and transparency in AGI actions, we can build trust in the technology, and we can ensure that it is used in a way that is consistent with human values.

4.3. Preventing Misuse and Malevolence

Preventing the misuse and malevolence of AGI is a critical component of a comprehensive set of safeguards and control mechanisms. This requires a multi-faceted approach that combines technical innovation with social and political engagement. The following sections will explore the key aspects of preventing misuse and malevolence, including designing security measures to prevent unauthorized access, developing protocols to detect and counter malicious intent, and fostering a culture of responsible AI development and deployment.

4.3.1. Designing Security Measures to Prevent Unauthorized Access

Designing security measures to prevent unauthorized access is a key aspect of preventing the misuse and malevolence of AGI. This involves creating a robust set of security protocols to protect the AGI from being hacked or accessed by unauthorized individuals. This could include using encryption to protect the AGI’s data, using multi-factor authentication to control access to the AGI, and creating a system for monitoring and auditing access to the AGI. By designing security measures to prevent unauthorized access, we can reduce the risk of the AGI being used for malicious purposes, and we can ensure that the technology is used in a way that is safe and beneficial.

4.3.2. Developing Protocols to Detect and Counter Malicious Intent

Developing protocols to detect and counter malicious intent is a key aspect of preventing the misuse and malevolence of AGI. This involves creating a set of protocols for identifying and responding to potential threats to the AGI. This could include using machine learning to detect anomalous behavior, creating a system for reporting and investigating potential threats, and developing a set of countermeasures to neutralize potential threats. By developing protocols to detect and counter malicious intent, we can reduce the risk of the AGI being used for malicious purposes, and we can ensure that the technology is used in a way that is safe and beneficial.

4.3.3. Fostering a Culture of Responsible AI Development and Deployment

Fostering a culture of responsible AI development and deployment is a critical component of preventing the misuse and malevolence of AGI. This involves promoting a set of ethical principles and best practices for the development and deployment of AI. This could include creating a code of conduct for AI developers, promoting transparency and accountability in AI development, and educating the public about the potential risks and benefits of AI. By fostering a culture of responsible AI development and deployment, we can create a community of developers and users who are committed to using the technology in a way that is safe, beneficial, and aligned with human values.

W.H.L.: Thank you, Kimi.

W.H.L.: Benevolence, though just a word, is a verbal representation of quite a lot of ideas covered here in our discussion to explore what safe, secure and reliable AGI should be and how to make it happen. Many topics touched today deserve further in-depth discussion. Though we’ve run out of time today, before we leave, I would like each of our panelists to go over what we have touched as a group and see what ideas are original, what have been worked on by others. Let’s keep the brainstorm going toward our common goal of benevolent AGI. Thanks again to each and everyone of our AI panelists!