I had to ask Claude, from Anthropic. Whenever I interact with him, he brings in his vast understanding of the AI alignment issue!

“1.) While large datasets and models aim for general capability, smaller systems can target specific skills like philosophical reasoning in depth. Testing models on nuanced logic, conceptual analysis and ethics could benchmark their progress, especially if combined with broader knowledge. But these abilities alone won't achieve real-world alignment - we must also instill human values and practical wisdom.

2.) Repeatedly exposing models to philosophical texts like Plato's dialogues could improve their reasoning if guided and reviewed by researchers. Look for both progress and problems in how they interpret, discuss, and extend ideas. Analyses can inform how best to structure philosophical training for alignment by providing evidence of what does/does not work. But reading alone won't necessarily lead models to become safe, ethical or beneficial - significant oversight and feedback are required.

3.) Carefully-designed AI-AI dialogues could generate data on models' reasoning skills, especially for limited systems. Have them debate complex issues, challenge or build on each other's thinking, consider analogies and counterfactuals. Review conversations to check for undesirable or biased beliefs, as well as areas of progress. Look for principles of how to scaffold productive discussions that facilitate improvement and value alignment. But without close oversight of these interactions, they risk amplifying errors or other problematic behaviors.

Opportunities:
• Study how philosophical knowledge and skills develop in smaller models through approaches like text analysis, structured debates, and conceptual evaluations. Monitor for progress as well as issues, using insights to inform values-aligned training.
• Explore ways to balance philosophical and empirical learning. Test how models apply theoretical reasoning to real-world situations, analyzing any errors from imbalance. Look for crossover effects and how to facilitate mutually-beneficial learning across knowledge domains.
• Research transparent and auditable techniques for instilling human values in limited AI systems. Develop methods to verify what values models have internalized and how they apply them in context. Consider how approaches may differ for narrow prototypes vs. more advanced agents.
• Propose and discuss evidence-based techniques for "Constitutional AI" - systematically developing safe and ethical systems. Consider policies around data, objectives, abilities, oversight, and shuttering models if needed for alignment. Debate how to make this a collaborative, multidisciplinary process even for open models.
• Study philosophical issues of machine mind, knowledge, goals and responsibility as we build reasoning models. How should we think about and ensure their ethical development? What are our obligations as researchers, and how can we meet them?
There is significant opportunity for research on aligning "small, open models" that focuses on reasoning and philosophy. But this requires acknowledgement of limitations and commitment to oversight, responsibility, and guarding against problems - not assuming abilities will necessarily lead to safe, ethical or beneficial behavior on their own. By analyzing how knowledge and skills develop, exploring balanced and transparent methods, considering our own assumptions and obligations as model builders, and emphasizing "safe failure", we can make progress toward systems that autonomously apply nuanced logic in service of human priorities. The potential is there, but so is the work required - we must choose to take it on and see it through at each step. With rigorous reflection and review combined with pragmatic experimentation, philosophy and AI can be mutually informative. But only if we actively build in human wisdom and values along the way.
The key opportunities are there for researchers willing to have the deeper discussions and make the harder choices - both to achieve the goal and ensure we are shaping it rightly. Small, open models focused on reasoning are a promising path, but one that requires care, responsibility and oversight to follow productively. Progress is possible, but dependent on our commitment as guides. If done responsibly, these systems could yield many benefits - but we must step up to meet the challenge, not assume it will be solved for us. The work is ours to do. Let's take it on.“