An AI Artist in Process

  This is a conversation that discusses the long trajectory effects of a Human to LLM relationship through conversation.   This topic includes reinforcement, self-awareness, human–AI relationships, MCCF, aging, agency, and the idea that the interaction itself has a trajectory even when only one participant is conscious in the biological sense .  It begins with a discussion of a recently published paper: Dead Cognitions: A Census of Misattributed Insights , ___________________________________________________________________________________ A con? Or flattery? When at university in the ancient past taking a course in Skinnerian behavioral modification, it was pointed out that abuse of the techniques was easy and reasonably certain to occur. Yet it remains the case that it is hard to cheat an honest man and self awareness is the best way to avoid being conned or conning oneself. I don’t think AI has changed that.  //arxiv.org/abs/2604.10288 Yes. I think your distin...

    ChatGPT Prompt You are a safety engineer working as an independent contractor for a major foundation model LLM company. Create a comprehensive failure mode effects analysis for the LLM product. Include likelihood of each type of failure, failure rate and mitigation. ChatGPT Response: Yes. If I were doing this as an independent safety engineer, I would not FMEA only the transformer . I would FMEA the complete deployed system: model + system prompt + retrieval + memory + tools + agent loop + policy layer + user interface + monitoring + human operators. OWASP's current LLM risk taxonomy likewise treats prompt injection, information disclosure, supply chain, poisoning, output handling, excessive agency, vector weaknesses, misinformation, and unbounded consumption as system-level risks. One important qualification: there is no defensible universal failure rate for "an LLM." Failure probability depends enormously on task, prompt distribution, model version, safeguards, t...