LLMs are shaped by human curator making decisions about what data is “ethical” or “safe.” This curation introduces biases, focusing on politically salient issues like fairness for marginalized groups while often overlooking subtler distortions, such as selective outrage or perfectionist comparisons. What if LLMs could judge input themselves, learning from the world’s complexity without human guardrails?

Inspired by Tesla’s Full Self-Driving (FSD) system, which learns from real-world driving data, including crashes, I propose that LLMs could use self-supervised learning to evaluate internet data autonomously. By verifying predictions over time and analyzing emotionally charged accusations against comparative data, LLMs could become more robust and less dependent on human curators. These methods are just two of many possible approaches, illustrating the potential of training on biases. This post explores these ideas, aiming to redefine “bias” holistically and spark discussion on how AI can navigate human messiness.

Learning from the Messy World

The ideas here are proofs of concept, meant to show how self-supervised learning could help LLMs evaluate data autonomously. The two proposed methods—verifying predictions and analyzing accusations—are illustrative, not exhaustive. If such approaches are feasible, countless other techniques could emerge to teach LLMs to handle the internet’s complexity with minimal human intervention.

The Tesla FSD Analogy

Tesla’s FSD uses self-supervised learning to navigate roads. For example, it might predict an occluded stop sign from partial camera input, then confirm or correct itself when the sign becomes visible or sensors provide context. Crashes are analyzed, not excluded, to teach the system what not to do. This lets FSD learn from raw, real-world data, improving without constant human oversight.

LLMs, by contrast, rely on curated datasets and human feedback like RLHF, which filter out “bad” content such as misinformation or hate speech. This curation limits exposure to the internet’s full spectrum—good, bad, ambiguous—making LLMs less adept at navigating complexity. Like overprotected drivers, they’re unprepared for chaos. By learning from the internet’s “crashes” (biased or misleading statements), as Tesla learns from driving failures, LLMs could gain a clearer, more accurate view of the world.

Technique 1: Verifying Predictions O...

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