The Unknowns
Honest exploration of what we don't know about AI. These are the open questions, the mysteries, and the frontiers where our understanding breaks down.
Why We Talk About What We Don't Know
Most AI education focuses on what works. But true understanding requires knowing the limits of our knowledge. These unknowns aren't failures—they're the frontiers where breakthroughs happen. Understanding them helps you think critically about AI claims and capabilities.
Open Questions in AI
The Emergence Mystery
Why do large language models suddenly develop capabilities that weren't explicitly trained? Emergence remains one of AI's most puzzling phenomena.
Open Questions:
- ?Why does scaling lead to qualitative jumps in capability?
- ?Can we predict which capabilities will emerge?
- ?Is emergence a fundamental property or a measurement artifact?
The Alignment Problem
How do we ensure AI systems do what we actually want, not just what we literally say? This remains unsolved at scale.
Open Questions:
- ?How do we specify human values formally?
- ?Can we verify alignment in complex systems?
- ?What happens when AI goals subtly diverge from human intent?
The Interpretability Gap
Modern neural networks are black boxes. We can't fully explain why they make specific decisions.
Open Questions:
- ?What do individual neurons actually represent?
- ?Can we build inherently interpretable systems?
- ?Is full interpretability even possible?
The Consciousness Question
Do AI systems have any form of subjective experience? How would we even know if they did?
Open Questions:
- ?What is the relationship between intelligence and consciousness?
- ?Could large language models have proto-consciousness?
- ?Is the question even scientifically answerable?
The Hallucination Problem
LLMs confidently generate false information. We don't fully understand why or how to reliably prevent it.
Open Questions:
- ?Why do models "believe" false statements?
- ?Can hallucinations ever be fully eliminated?
- ?How do we balance creativity with factuality?
The Generalization Mystery
Deep learning works despite violating classical learning theory. We don't fully understand why neural networks generalize so well.
Open Questions:
- ?Why don't overparameterized models overfit?
- ?What is the true inductive bias of neural networks?
- ?How does architecture affect generalization?
Active Research Frontiers
These are areas where top AI labs are actively working to push the boundaries of our understanding.
Mechanistic Interpretability
Reverse-engineering neural networks to understand their internal algorithms
Constitutional AI
Training AI systems to follow principles and self-correct harmful outputs
Scaling Laws
Understanding how capabilities change with model size, data, and compute
Multimodal Understanding
How models integrate and reason across different types of data
Understanding Requires Humility
The best way to navigate AI's unknowns is to build a strong foundation in what we do know.