The idea that AI "learns" from interactions with individual users is a common misconception, and it’s important to clarify how this process works. Most AI systems, including ChatGPT, do not learn or evolve in real time from individual user interactions. Instead, they rely on pre-trained models that are periodically u…
The idea that AI "learns" from interactions with individual users is a common misconception, and it’s important to clarify how this process works. Most AI systems, including ChatGPT, do not learn or evolve in real time from individual user interactions. Instead, they rely on pre-trained models that are periodically updated by developers using large-scale datasets. While feedback from users (e.g., thumbs up/down ratings or flagged responses) can sometimes inform future updates, these changes happen at a broader level and are not specific to any single conversation.
In the context of a single interaction, AI systems may appear to "adapt" because they use context from the ongoing conversation to generate more relevant responses. However, this is not true learning—it’s simply the AI maintaining continuity within the session. Once the session ends, the AI does not retain any memory of the interaction unless explicitly designed to do so (and even then, it is typically limited to specific use cases with user consent).
Rebuttal and critical engagement are indeed important when interacting with AI, but not because the AI "learns" from it. Instead, rebuttal helps users refine their own understanding and ensures they critically evaluate the information provided by the AI. This process fosters better decision-making and prevents over-reliance on AI as an authoritative source of truth
In AI;s own words....
The idea that AI "learns" from interactions with individual users is a common misconception, and it’s important to clarify how this process works. Most AI systems, including ChatGPT, do not learn or evolve in real time from individual user interactions. Instead, they rely on pre-trained models that are periodically updated by developers using large-scale datasets. While feedback from users (e.g., thumbs up/down ratings or flagged responses) can sometimes inform future updates, these changes happen at a broader level and are not specific to any single conversation.
In the context of a single interaction, AI systems may appear to "adapt" because they use context from the ongoing conversation to generate more relevant responses. However, this is not true learning—it’s simply the AI maintaining continuity within the session. Once the session ends, the AI does not retain any memory of the interaction unless explicitly designed to do so (and even then, it is typically limited to specific use cases with user consent).
Rebuttal and critical engagement are indeed important when interacting with AI, but not because the AI "learns" from it. Instead, rebuttal helps users refine their own understanding and ensures they critically evaluate the information provided by the AI. This process fosters better decision-making and prevents over-reliance on AI as an authoritative source of truth