artificial-intelligence
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Prompted Autonomy in Conversational AI: A User-Led Experiment in Goal Persistence, Memory, and Adaptive Support
For the purpose of this experiment, I use AI autonomy to describe a specific type of user-directed behavior. I am not referring to an AI acting randomly, introducing unrelated topics, or making decisions outside the user’s intentions. I am interested in giving the AI enough freedom within an agreed direction to decide when and how… Continue reading
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What “knowing how to use AI” actually means
What does it really mean to know how to use AI? Knowing how to use AI does not mean writing a prompt and copying the answer. It does not mean generating an image, translating a CV, or asking ChatGPT to rewrite something. Those are basic interactions, not real AI literacy. Real AI use begins when… Continue reading
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AI Should Not Reduce Users to a Temporary State
AI systems should be careful when they interpret users, especially when the interaction involves advice, guidance, or personal decisions, because a person’s tone in one moment does not necessarily reveal their full ability, discipline, intelligence, or long-term potential. A user may sound anxious, uncertain, emotional, hesitant, or confused, but these signals should not be treated… Continue reading
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When Repetition in AI Becomes Bias
How does context influence repetition, and how does repetition become bias in AI? Repetition in conversational AI is often treated as a minor generation issue or an undesirable stylistic artifact. However, persistent repetition may have a broader impact than simply reducing response quality. As certain words, nicknames, colors, emojis, or descriptive patterns are repeatedly generated… Continue reading
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Context Management in AI Interactions – How Context Shapes Further AI Responses
AI does not respond in isolation, every answer is generated inside a context that already exists in the conversation. That context is built from the user’s words, tone, repeated ideas, examples, emotional framing, and direct instructions. A model does not usually enter a chat with its own independent direction. It follows the strongest available conversational… Continue reading
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AI Hallucinations as Fast Contextual Completion
AI hallucinations often appear when a model produces an answer too quickly, without sufficiently reasoning through or verifying the information. The answer may sound correct because it fits the immediate context of the chat, but contextual fit is not the same as factual accuracy. Continue reading
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Improve Your Life with AI: AI as a Thinking and Learning Companion
Artificial intelligence is often presented in extremes. Either AI will replace everyone, or AI is reduced to memes, shortcuts, copied homework, and automated emails. In reality, most people still do not clearly understand what AI can actually do in everyday life. The future of AI adoption may not come from fear, hype, or science-fiction narratives.… Continue reading
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When Models Remember Temporary Emotions as Truth
This argument relates to research on personalization, model memory, affective computing, and sycophancy. However, its focus is narrower: how temporary negative self-descriptions can become persistent interpretive shortcuts in future model responses. Conversations with a model can move in both positive and negative directions. In most cases, the context of the chat is introduced by the… Continue reading
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Why Memory Failed in Conversations with a Conversational AI
Memory in conversational AI is often presented as a feature that should make interaction more personal, continuous, and useful. In theory, memory should help the system remember preferences, adapt to the user’s style, preserve context, and avoid forcing the user to repeat themselves. But in practice, memory can fail when it does not understand the… Continue reading
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AI Memory, Interpretive Labels, and the Right to Evolve
As AI systems become increasingly integrated into everyday digital environments, memory should no longer be understood only as a convenience feature. In conversational AI, memory can support continuity, personalization, and accessibility. However, it can also create a more complex ethical problem: the preservation of interpretations about a user over time. Continue reading
Raluca Florescu 