The Potential Appearance of Highly Advanced Artificial Intelligence
Artificial Intelligence (AI) is facing a conceptual challenge known as the 'intelligence trilemma', a trade-off involving the system's abilities, truthfulness, and interpretability, particularly in large language models (LLMs). Although there isn't a direct, widely established definition of the "intelligence trilemma," research on LLMs reveals related dilemmas about balancing veracity, probabilistic knowledge representation, and interpretability of AI outputs.
One study, sAwMIL, introduces a new probing method that addresses flawed assumptions in previous veracity probes by distinguishing statements as true, false, or neither, showing that AI models contain complex truth signals that are often asymmetric and concentrated in certain model layers. This reflects the difficulty of creating models that are simultaneously accurate, interpretable, and reliable in their knowledge representation.
In contrast, human societies rely on various social coordination mechanisms to collectively generate, verify, and circulate knowledge. These mechanisms balance truth, trust, and social interpretability, with individuals deciding what information to accept as true based on social cues, expertise, and verification to coordinate actions effectively. The intelligence trilemma in AI echoes this, but with an important difference: AI models do not possess social context or intrinsic trust mechanisms. Instead, they use probabilistic computation to represent knowledge, lacking human-like understanding or social coordination capabilities.
The trilemma highlights AI's struggle to mimic human-like knowledge coordination mechanisms without the benefits of social context or discourse, underscoring the complexity of trustworthy AI design. As we near the singularity, AI systems may function more like the Internet, with multiple parts operating independently and efficiently connected. Decentralization can help bridge the boundaries between optimizing for two of the three qualities and sacrificing the third in centralized AI systems.
Abhishek Singh, in a presentation at the Indian Institute of Artificial Intelligence (IIA), discussed the 'intelligence trilemma' and introduced 'CHAOS theory 2.0', a framework for understanding how Coordination, Heterogeneity, and Scalability interact in AI systems. Singh also compared two approaches to artificial intelligence: one big centralized AI and many small decentralized AI agents interacting with each other. He draws inspiration for CHAOS from human societies' coordination mechanisms such as language, cultural institutions, financial institutions, social norms, and knowledge transfer systems.
In summary, the intelligence trilemma in AI and the coordination mechanisms in human societies share similarities and differences. The trilemma underscores the challenge of designing AI that can reliably "know" and communicate truth while remaining transparent and interpretable to human users, without the social infrastructures humans naturally have. As AI continues to evolve, finding solutions to the intelligence trilemma will be crucial for building trustworthy and effective AI systems.
References: [1] Singh, Abhishek. (2021). CHAOS Theory 2.0: Understanding Coordination, Heterogeneity, and Scalability in AI Systems. Indian Institute of Artificial Intelligence (IIA).
- The intelligence trilemma in enterprise tech, particularly in the development of large language models (LLMs), underscores the challenges in creating AI that balances accuracy, interpretability, and reliability in knowledge representation, mirroring the social coordination mechanisms humans use.
- In contrast to AI models, human societies rely on various social coordination mechanisms, such as language and cultural institutions, to balance truth, trust, and social interpretability, indicating the potential benefits of decentralization in AI systems to improve knowledge representation while addressing the trilemma.
