Q&A: How AI came to model the “messy” human mind

A new paper in *Past and Present* by MIT historian Benjamin Lindquist traces the roots of modern generative AI hallucinations to mid‑20th‑century engineering experiments that deliberately injected randomness into deterministic algorithms. Early researchers believed that irrationality and chance were essential to emulate human creativity, a design choice that created a lasting trade‑off between creative output and predictability in today’s large language models. Understanding this historical rationale helps engineers anticipate and manage hallucination behavior when building or fine‑tuning generative systems.

⚡ Key Takeaways

• The paper appears in the journal *Past and Present*.
• Early AI designs deliberately added randomness (e.g., “artificial (or random) neural networks”) to mimic creative leaps.
• The approach introduced an inherent trade‑off: increased creativity versus reduced predictability and higher hallucination risk.
• Engineers should consider randomness injection as a design knob when shaping model behavior.
• The trade‑off implies that eliminating hallucinations entirely may sacrifice the model’s creative capacity.

Recognizing that randomness was a foundational design choice explains why modern LLMs still struggle with hallucinations, guiding engineers to balance creative flexibility against reliability in production systems.

✅ Practical Steps

1. Apply the concepts from this article to your own system design.

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