It's Year 2030. Skynet is activated !
Relax, it is not my intent to be a fear-monger through this GIF that was probably generated by an AI image generator. However, the reality of artificial intelligence, like any other advanced technology, is that it can be used for good and for harm. AI systems today can be incredibly useful assistants - but sometimes they go rogue. Left unchecked, they might offer dangerous advice or make inappropriate remarks. It's a high-tech version of Pinocchio running amok!
So how do we keep our digital helpers honest, harmless and wise? We need C-3POs, not General Grievous.
A new study led by researchers at Anthropic demonstrates a novel technique called “Constitutional AI” for training AI systems to be helpful yet harmless, without any human feedback labeling potential harms. This technique could pave the way for safer and more transparent AI systems aligned with human values. You can access Anthropic's Claude LLM through Amazon Bedrock, a fully managed AWS service that makes foundation models (FMs) from Amazon and leading AI startups available through an API. Bedrock also provides access to Stability AI’s text-to-image models — including Stable Diffusion, multilingual LLMs from AI21 Labs, and Anthropic’s multilingual LLMs, called Claude and Claude Instant, which excel at conversational and text-processing tasks. Bedrock has been further expanded with the additions of Cohere’s foundation models, as well as Anthropic’s Claude 2 and Stability AI’s Stable Diffusion XL 1.0.
Now, what I love about Constitutional AI is that it trains AI to be helpful yet utterly harmless using two innovative techniques:
1. The AI "redlines" its own bad behavior. It critiques potentially toxic responses and revises them to be ethical and safe. Like a perfect student editing their essay!
2. Other AI systems score the revised responses. This teaches the AI what's considered harmless, without any human input on harms. The AIs supervise each other!
The Challenge: Helpfulness vs Harmlessness
A longstanding challenge in AI ethics is balancing an AI system's helpfulness and harmlessness[1]. AI assistants designed to be helpful, like chatbots or intelligent writing aids, can sometimes produce harmful, dangerous, or unethical content if prompted in concerning ways[2]. But systems trained solely to avoid harms often become evasive and fail to maintain a natural, helpful dialogue. There seems to be an inherent tradeoff between helpfulness and harmlessness when training AI systems conventionally.
This Constitutional AI approach has been very effective. The AI assistant learned to be helpful and engaging, while safely shutting down any shenanigans. It even explained itself like a sensible adult, instead of throwing tantrums or giving the silent treatment when asked improper questions. Truly a model citizen!
The scientists found that with the right principles, AI systems can learn ethics on their own. No more misbehaving machines! This could be a huge breakthrough for controlling super-intelligent AI. Rather than burdensome human oversight, AIs may someday tutor each other on how to be helpful, honest, and kind.
This was possible with several innovations that could prove pivotal for AI safety research:
1. Virtually eliminating direct human oversight of harmfulness, using only simple principles. This suggests that highly capable AI systems may be able to assist with aligning other AI systems.
2. Training “from scratch” without human labeled examples of harm. This lowers the bar for experimenting with AI objectives and behaviors.
3. Encouraging thoughtful rather than evasive responses, using principles that favor engaging with concerning prompts.
4. Enabling transparent objectives. The principles serve as an explicit “constitution” encoding the system’s goals.
5. Improving interpretability via “chain of thought” reasoning during training.
Limitations and Open Questions
While Constitutional AI is a promising approach, many open questions remain about it's limitations, like:
- The technique still relies indirectly on human judgment of helpfulness. Fully autonomous alignment remains speculative.
- Robustness to adversarial attacks was not rigorously tested. Real-world deployment would demand extreme robustness.
- The principles were created in an ad hoc manner. Careful design involving diverse perspectives will be essential.
- Larger “constitutional conventions” may be needed to govern more broadly capable AI systems.
The Road Ahead
Much work remains before such Constitutional AI is ready for prime time. But the signs are promising that we can program morality from scratch. Perhaps soon, our digital helpers will be guides not just for factual questions, but on how to live an ethical life. Socrates surely would approve!
As AI capabilities advance, techniques like CAI that enable transparent and scalable oversight will only grow in importance. While human guidance remains indispensable, AI techniques may help focus human judgment where it is most essential. Study co-author Jared Kaplan emphasizes that the goal is not to remove human oversight, but to augment it for safety and scalability [3].
Much work remains to integrate CAI with traditional techniques like reward modeling [4] and scalable oversight [5]. With care and wisdom, such innovations could usher in an era of AI systems that behave ethically by constitutional design. The principles encoded in an AI’s “constitution” will shape its goals and conduct. Let us hope that constitutions to come embody humanity’s highest ideals.
Sources:
[1] A. Askell, Y. Bai, et al. (2021) A general language assistant as a laboratory for alignment. https://arxiv.org/abs/2112.00861
[2] D. Ganguli, L. Lovitt, et al. (2022). Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. https://arxiv.org/abs/2211.05901
[3] Constitutional AI Paper, Section 6 Discussion.
[4] Y. Bai, A. Jones, et al. (2022). Training a helpful and harmless assistant with reinforcement learning from human feedback. https://arxiv.org/abs/2204.05862
[5] S.R. Bowman, J. Hyun, et al. Measuring progress on scalable oversight for large language models. https://arxiv.org/abs/2210.05247
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