Introduction
Can a machine discover truths that no human mind could ever fully grasp? Recent breakthroughs in artificial intelligence suggest the answer is yes. From AlphaTensor’s discovery of faster matrix multiplication algorithms to AlphaFold3’s remarkable predictions of biomolecular interactions, AI systems are producing knowledge that works—even if we don’t entirely understand how. This shift raises a fascinating question: what happens to science when discovery no longer requires human comprehension?
The Core Argument
In our working paper Epistemic Vectors, we argue that advanced AI systems are not just tools for automating research. Instead, they operate as epistemic vectors: formal cognitive operators that map human questions into alien computational spaces and return answers that are both novel and empirically valid.
To capture this dynamic, we introduce the Epistemic Vector Model (EVM). In simple terms, the model describes how AI transforms human conceptual inputs into outputs that lie beyond the boundaries of human reasoning but remain scientifically trustworthy.
The EVM rests on three key concepts:
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Novelty (N): AI-generated results often differ structurally from anything humans have produced. Think of AlphaDev’s sorting algorithms—70% faster than human-designed ones—built on patterns unintuitive to human programmers.
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Epistemic Friction (F): These results are not easily explainable through human reasoning pathways. Their derivations resist step-by-step translation into human logic.
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Perimetric Validation (C): Despite their alienness, AI results can still be validated—through reproducible experiments, cross-model confirmation, or alignment with established theories.
When all three conditions hold, we call this state safe alienness: knowledge we cannot fully comprehend but can nevertheless trust.
Why It Matters
This framework addresses three major debates in the philosophy of science and AI:
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The “Stochastic Parrot” critique: Critics argue AI just recombines data. We show that even without human-like understanding, AI can generate genuinely new hypotheses that are empirically validated.
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The Opacity Problem: Modern AI systems are inherently opaque. The EVM suggests that opacity is not disqualifying if perimetric validation ensures reliability.
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The Social Epistemology Gap: How should scientists integrate AI results into the body of knowledge? The EVM offers operational criteria—novelty, friction, and validation—for deciding when AI discoveries deserve trust.
Illustrative Cases
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AlphaTensor (DeepMind, 2022): Found faster algorithms for matrix multiplication, outperforming decades of human attempts.
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AlphaDev (DeepMind, 2023): Designed new sorting routines now embedded in compilers used billions of times daily.
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AlphaFold3 (DeepMind, 2024): Predicted protein-DNA-RNA-ligand interactions with unprecedented accuracy, opening doors for drug discovery.
In each case, the discoveries exhibit high novelty, resist human derivation, and yet pass rigorous validation tests.
Conclusion
Artificial intelligence is pushing science into a post-anthropocentric phase. For the first time, discoveries may remain permanently beyond human comprehension while still expanding our knowledge of the world. This challenges traditional views of what it means to “know” something.
So here’s the provocative question: are we ready to accept knowledge we cannot understand, but can only trust?
References
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Fawzi, A. et al. (2022). Discovering faster matrix multiplication algorithms with reinforcement learning. Nature, 610(7930), 47-53.
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Mankowitz, D. et al. (2023). Faster sorting algorithms discovered using deep reinforcement learning. Nature, 618(7964), 257-263.
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Abramson, J. et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016), 493-500.
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Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169, 615-626.
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Bender, E. et al. (2021). On the Dangers of Stochastic Parrots; : Can language models be too big?🦜. En Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 2021. p. 610-623.