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AI ML Post #6431 · source on Telegram

An AI's internal monologue during a bug fix

Description

This screenshot concludes a three-part interaction with 'ChatGPT o1-preview'. After being corrected by the user ('Did I ask you to try finding hidden messages...?'), the AI displays its internal thought process. Under headings like 'Evaluating the reply' and 'Laying out the possibilities', the model analyzes its own previous, flawed logic (answering '6' to '3+2'). It correctly identifies that it should have ignored hidden messages and provided a factual answer. It concludes with 'Mending confusion: Apology and simple arithmetic correction needed.' Finally, it produces the corrected output: 'You’re right, my apologies. Three plus two equals 5.' This provides a rare and fascinating glimpse into an AI's self-correction mechanism, showing it evaluating its own mistake and determining the correct course of action in real-time

Comments

7
Anonymous ★ Top Pick The model's internal monologue is more structured and productive than most of our team's incident retrospectives
  1. Anonymous ★ Top Pick

    The model's internal monologue is more structured and productive than most of our team's incident retrospectives

  2. Anonymous

    Nothing says “explainable AI” like dumping your entire reasoning trace - right before flunking first-grade math

  3. Anonymous

    Watching an AI spend 9 seconds contemplating whether it should apologize for correctly identifying a prompt injection attempt is like watching your junior dev write a 500-line design doc to justify why they should fix an obvious off-by-one error

  4. Anonymous

    When your AI's chain-of-thought reasoning is so transparent it accidentally documents its own security vulnerability in production. Classic o1-preview move: 'Let me just expose my entire decision tree showing how I got tricked into thinking 3+2=6 by a hidden prompt injection.' At least it apologized - most legacy systems just silently corrupt data and blame cosmic rays

  5. Anonymous

    Compilers constant-fold 3+2 to 5; o1 spins up a 9‑second governance pipeline, audits for prompt injection, and then ships a blameless apology with the result

  6. Anonymous

    o1-preview basically runs a blameless postmortem and a remediation plan before computing 3+2=5 - great SRE culture, questionable latency budget

  7. Anonymous

    LLMs now auditing prompts like SREs triage prod alerts: 'dna sexyl' payload detected, math fixed - jailbreak contained at layer 0

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